multiomics: A user-friendly multi-omics data harmonisation R pipeline

Tyrone Chen; Al J Abadi; Kim-Anh Lê Cao; Sonika Tyagi

doi:10.12688/f1000research.53453.1

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multiomics: A user-friendly multi-omics data harmonisation R pipeline

[version 1; peer review: 2 not approved]

Tyrone Chen ¹, Al J Abadi², Kim-Anh Lê Cao², Sonika Tyagi ^1,3

PUBLISHED 06 Jul 2021

Author details Author details

¹ School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia
² Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia
³ Department of Infectious Disease and Monash eResearch Centre, The Alfred Hospital and Monash University, Melbourne, VIC, 3004, Australia

Tyrone Chen
Roles: Conceptualization, Data Curation, Formal Analysis, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Al J Abadi
Roles: Methodology, Software, Validation, Visualization, Writing – Review & Editing

Kim-Anh Lê Cao
Roles: Formal Analysis, Funding Acquisition, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Sonika Tyagi
Roles: Conceptualization, Data Curation, Funding Acquisition, Project Administration, Resources, Supervision, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

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Abstract

Data from multiple omics layers of a biological system is growing in quantity, heterogeneity and dimensionality. Simultaneous multi-omics data integration is a growing field of research as it has strong potential to unlock information on previously hidden biological relationships leading to early diagnosis, prognosis and expedited treatments. Many tools for multi-omics data integration are being developed. However, these tools are often restricted to highly specific experimental designs, and types of omics data. While some general methods do exist, they require specific data formats and experimental conditions. A major limitation in the field is a lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation. There is an increasing demand for a generic multi-omics pipeline to facilitate general-purpose data exploration and analysis of heterogeneous data. Therefore, we present our R multiomics pipeline as an easy to use and flexible pipeline that takes unrefined multi-omics data as input, sample information and user-specified parameters to generate a list of output plots and data tables for quality control and downstream analysis. We have demonstrated application of the pipeline on two separate COVID-19 case studies. We enabled limited checkpointing where intermediate output is staged to allow continuation after errors or interruptions in the pipeline and generate a script for reproducing the analysis to improve reproducibility. A seamless integration with the mixOmics R package is achieved, as the R data object can be loaded and manipulated with mixOmics functions. Our pipeline can be installed as an R package or from the git repository, and is accompanied by detailed documentation with walkthroughs on two case studies. The pipeline is also available as Docker and Singularity containers.

Keywords

machine learning, multi-omics, data integration, data harmonisation, multivariate analysis

Corresponding authors: Tyrone Chen, Sonika Tyagi

Competing interests: No competing interests were disclosed.

Grant information: S. T acknowledges the AISRF EMCR Fellowship by the Australian Academy of Science and Australian Women ResearchSuccess Grant at Monash University. T. C received funding from the Australian Government Research Training ProgramScholarship and Monash Faculty of Science Dean’s Postgraduate Research Scholarship. K-A. L-C was supported in partby the National Health and Medical Research Council (NHMRC) Career Development fellowship (GNT1159458)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Chen T et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Chen T, Abadi AJ, Lê Cao KA and Tyagi S. multiomics: A user-friendly multi-omics data harmonisation R pipeline [version 1; peer review: 2 not approved]. F1000Research 2021, 10:538 (https://doi.org/10.12688/f1000research.53453.1) First published: 06 Jul 2021, 10:538 (https://doi.org/10.12688/f1000research.53453.1) Latest published: 02 Aug 2023, 10:538 (https://doi.org/10.12688/f1000research.53453.2)

Introduction

A biological phenotype is an emergent property of a complex network of biological interactions. Since relying on a single layer of omics data to test a biological hypothesis results in an incomplete perspective of a biological system, interest in multi-omics data integration is steadily increasing as a means to decipher complex biological phenotypes.¹

We illustrate these points with a hypothetical case of measuring protein and transcript levels in a same set of matched samples. Each of these omics data layers contain independent information. A correlation score is then obtained between expression levels of the two blocks of omics data, resulting in an interpretable association measure. While correlation scores are a primitive metric, especially in this context of protein and transcript,² they represent an additional layer of data summarising valuable relationships. Identifying highly correlated features across independent blocks of omics data could potentially reinforce the validity of the result, while highlighting interesting features (strong positive or negative correlations) for further investigation [Figure 1]. Hence, exploiting such parallel measurements from a multi-omics perspective allows a more comprehensive and cohesive view of such complex and often dynamic systems, and this resolution would be expected to improve as more omics layers are added. Published multi-omics studies discovering novel biological insights which are not possible with single-omics data further supports our points.^3-9 With the increasing volume of multi-omics data present in publicly accessible biological data repositories,^10-12 multi-omics data integration is expected to be the core strategy of modern and future biological data analyses.

Figure 1. An illustration of a hypothetical multi-omics perspective on a simple biological system.

The rectangles represent different layers of omics data (e.g. proteome, transcriptome and lipidome) while the circles represent features within their respective omics data layer. Black single-line arrows show correlation between features within the omics data (e.g. a regulatory factor) while blue double-lines show correlation between features across different omics data layers. A powerful abstraction of the system under study can be obtained by reviewing multiple layers of omics data holistically.

As a result, methods have been developed to leverage the multitude of data modalities in characterising biological systems. While many tools are available, most of these methods are heavily customised to fit a specific experimental design, and are not generic enough to handle most use cases.¹ Furthermore, many tools that claim to perform data integration actually perform high-level data aggregation, where datasets are processed individually and only summarised, high level information is analysed together.¹ Of these algorithms, few perform data integration of multiple layers of omics data simultaneously, which we refer to specifically as “data harmonisation” to distinguish it from the more general term of “data integration”.¹

While some “data harmonisation” algorithms exist, it is important to note that at this time, no end-to-end pipeline or framework exists which allows the user to quickly and easily input unrefined data, run a pipeline and export output data which can be used for downstream analyses and further downstream analyses. Therefore, to facilitate this, we developed multiomics, a flexible, easy-to-install and easy-to-use pipeline.

We present a pipeline targeted at bioinformaticians called multiomics¹³ with some important features, implementing one of the state of the art tools in data harmonisation from the mixOmics R package.¹⁴ It is portable with multiple implementations, and can be installed as an R¹⁵ package or used by cloning the associated git repository.¹⁶ A series of diagnostic plots are generated automatically and compiled into a pdf file. There is seamless integration with mixOmics, where data generated by the pipeline is exported automatically as a R data object of mixOmics classes. As a form of checkpointing, the R data object is updated at every major stage of the pipeline, and can be loaded directly into the mixOmics suite of tools for further investigation or plot customisation. To increase reproducibility, command line arguments are also exported as a script file which can be rerun directly to reproduce the output. To improve usability, the option to provide command line arguments as a json file is also available.

Detailed documentation is provided both within the source git repository and as vignettes in the R package. Multiple installation methods are shown in the git repository to maximise accessibility of our pipeline for users. Additionally, walkthroughs of two case studies are included. Complete and detailed examples of input data format are also provided, including a sample dataset which can be loaded directly from the R package. In this manuscript, we summarise these information and show a minimum working example to highlight some of the features of our pipeline.

Methods

Implementation

Quick install

You can install this directly as a R package from gitlab:

install.packages("devtools")
library("devtools")
install_gitlab("tyagilab/sars-cov-2", subdir="multiomics")

Docker and singularity containers

Docker¹⁷ and Singularity^18,19 images are also available if the user prefers to use containers directly. Note that you typically need root access to run Docker, if this is not possible try Singularity.

# download the Docker image
docker pull tyronechen/multiomics:1.0.0

# check that it works correctly
docker run --rm -it tyronechen/multiomics:1.0.0 Rscript -e 'packageVersion("multiomics")'

# this opens a bash shell where you can use run_pipeline.R
docker run --rm -it --entrypoint bash tyronechen/multiomics:1.0.0

# copy the script from install location or repository as shown in the previous section
# once you have a copy of the script in your current working directory, you can run this commandRscript run_pipeline.R -h

If you don’t have root access, you can try Singularity. The Singularity image file is large and you may need to set $SINGULARITY_TMPDIR to a custom location with at least 1 GB of free space.

# set singularity tmpdir to a location of your choice
# if you are not in a HPC you can usually skip this
export SINGULARITY_TMPDIR=/path/to/directory

singularity pull multiomics.sif docker://tyronechen/multiomics:1.0.0

# copy the script from install location or repository as shown above and runsingularity exec multiomics.sif Rscript run_pipeline.R -h

Manual install

If the above automated install steps do not work, detailed manual installation instructions are available in the source git repository at https://gitlab.com/tyagilab/sars-cov-2/-/tree/master for conda and R.

You may need to install mixOmics from source. Follow the installation instructions on https://github.com/aljabadi/mixOmics#installation:

install_github("mixOmicsTeam/mixOmics")

The actual script used to run the pipeline is not directly callable but provided as a separate script. Running the following command will show you the path to the script. A copy of this is also available in the source git repository.

system.file("scripts", "run_pipeline.R", package="multiomics")
# outside of R
Rscript run_pipeline.R -h

Operation

Example input

Three elements are the minimum required input for the pipeline [Figure 2]. First, at least two files corresponding to omics data blocks are required. Next, a file containing biological class information is required. Finally, a list of unique names labelling each data block is required. Examples of these input files and their internal data structure as they appear in the pipeline are shown.

# download omic data block 1
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/",
  "/raw/master/data/case_study_2/data_lipidomics.tsv",
  sep="-"
)
download.file(url, "data_lipidomics.tsv")

# download omic data block 2
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/",
  "/raw/master/data/case_study_2/data_metabolomics.tsv",
  sep="-"
)
download.file(url, "data_metabolomics.tsv")

# download class information
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/",
  "/raw/master/data/case_study_2/classes_diablo.tsv",
  sep="-"
)
download.file(url, "classes_diablo.tsv")

# inspect the data
> lipid <- read.table( 
  "data_lipidomics.tsv", sep="\t", header=TRUE, row.names=1 
  )
> head(lipid[,1:2])
#    AC.10.0_RT_6.936 AC.12.0_RT_7.955
# C1    18.13745    16.84196
# C10    20.48135    18.06048
# C100    22.32588    21.30632
# C101    20.56189    18.84777
# C102    22.28591    17.98330
# C103    18.18658    16.97716

> metab <- read.table( 
  "data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1 
  )
> head(metab[,1:2])
#  X1.2.Propanediol..2TMS.de X2.3.Dihydroxybutanoic.ac
# C1        22.52599        13.89898
# C10        22.63460        17.85105
# C100        22.12956        13.34028
# C101        21.94220        17.44137
# C102        21.87579        17.88084
# C103        21.37599        14.28262

> biological_classes <- read.table( 
  "classes_diablo.tsv", sep="\t", header=TRUE, row.names=1 
  )
> head(biological_classes)
#  Hospital_free_days_45
# C1      More severe
# C10      More severe
# C100      Less severe
# C101      Less severe
# C102      More severe
# C103      More severe

> data_names
# [1] "lipidome" "metabolome"

Figure 2. Technical notes for the pipeline.

We summarise pipeline installation steps and the flow of data through the pipeline. This figure was originally published on gitlab under a CC-BY-3.0 AU license and is reproduced here with permission.

Note that column names and row names should be truncated to avoid bugs in the pipeline associated with name length. Furthermore, usage of non-alphanumeric characters in their names should be avoided as R quietly replaces these with. (periods).

Examples of these data and class files for two case studies are included in the source git repository.

Running the pipeline

The pipeline is run with the command Rscript run_pipeline.R and passing a list of command line arguments either as strings of text or in a json file (recommended). Running the actual pipeline can take some time. The main bottleneck is parameter tuning which scales exponentially with the number of omics data blocks, but it is possible to disable this if the user wants to perform a test run or is already aware of the parameters. We note that R Data objects are periodically exported that allow for seamless integration with functions in the underlying mixOmics package when needed. A secondary bottleneck is data imputation, which scales with the number of components used and the dimensions of the input data. If needed, it is possible to impute and export this imputed data either with the pipeline or with the underlying mixOmics function, and then substitute that as input. The user can adjust the number of cpus if needed to speed up the process. Data imputation can be skipped if it is not required.

Code for the pipeline can be examined in detail from the git repository or individual functions can be inspected directly after loading the R multiomics package.

Example output

Output files include a pdf file compiling all graphical output.^20-24 Note that this can be quite large, especially if you have a large dataset. A graphml file is also exported for input into cytoscape.²⁵ Due to the size and volume of plots, we provide a link to some example plots here. A manuscript using figures generated from this pipeline is also available for reference.²⁶

Each analysis generates a series of text files containing feature weights. In some ways, these are functionally analogous to differential expression analyses, where these coefficients summarise the features with the most phenotypically relevant information. At the same time, a table of feature correlations across multi-omics data is generated. Some examples of these are shown below:

# download single-omic variable weights
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/",
  "-/raw/master/results/case_study_2/",
  "lipidome_sPLSDA_max.txt",
  sep="" 
  )
download.file(url, "lipidome_sPLSDA_max.txt")

# download multi-omic variable weights
# this is for a single block of omics data
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/", 
  "-/raw/master/results/case_study_2/", 
  "lipidome_DIABLO_max.txt", 
  sep="" 
  )
download.file(url, "lipidome_DIABLO_max.txt")

# download multi-omic correlations
url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/", 
  "-/raw/master/results/case_study_2/", 
  "DIABLO_var_keepx_correlations.txt", 
  sep="" 
  )
download.file(url, "DIABLO_var_keepx_correlations.txt")

> lipid_splsda <- read.table( 
  "lipidome_sPLSDA_max.txt", header=TRUE, sep="\t", row.names=1 
  )
> colnames(lipid_splsda)
# [1] "More.severe"  "Contrib.Less.severe" "Contrib.More.severe" "Contrib"
# [5] "GroupContrib"  "color"          "importance"
> head(lipid_splsda[,1:2])
#                More.severe Contrib.Less.severe
# Unknown_mz_794.50909_._R   0.5552234      -0.3042823
# Unknown_mz_784.5116_._RT  -0.5015304       0.6519465
# Unknown_mz_632.40179_._R  -0.4719458       0.3883697
# Unknown_mz_594.49445_._R  -0.6700148       0.5980478
# Unknown_mz_481.04605_._R  -0.7062099       0.5334766
# Unknown_mz_289.07495_._R  -0.6902981       0.1644617

> lipid_diablo <- read.table( 
  "lipidome_DIABLO_max.txt", header=TRUE, sep="\t", row.names=1 
  )
> colnames(lipid_diablo)
# [1] "More.severe"  "Contrib.Less.severe" "Contrib.More.severe" "Contrib"
# [5] "GroupContrib"  "color"          "importance"
> head(lipid_diablo[,1:2])
#                More.severe Contrib.Less.severe
# Unknown_mz_632.40179_._R  -0.4719458       0.3883697
# Unknown_mz_784.5116_._RT  -0.5015304       0.6519465
# Unknown_mz_229.15463_._R  -0.4703626       0.3841564
# Unknown_mz_794.50909_._R   0.5552234      -0.3042823
# Unknown_mz_243.13472_._R  -0.4772169       0.3736290
# Unknown_mz_289.07495_._R  -0.6902981       0.1644617

> correlations <- read.table( 
  "DIABLO_var_keepx_correlations.txt", header=TRUE, sep="\t", row.names=1 
  )
> dim(correlations)
# [1] 80 80
> head(correlations[,1:2])
#                PI.18.2_18.1_RT_20.436 PI.38.6_RT_20.119
# PI.18.2_18.1_RT_20.436       0.23614781      0.224478910
# PI.38.6_RT_20.119          0.22447891      0.222982076
# Unknown_mz_229.15463_._R      0.06458937      -0.019670684
# Unknown_mz_243.13472_._R      0.08228558      -0.004164718
# Unknown_mz_247.09216_._R      0.18821603      0.143643243
# Unknown_mz_289.07495_._R      0.11050560      0.040398304

An R data file containing all of the information above and a script containing command line arguments which can be used to reproduce the analysis are also exported to enable full reproducibility.

Examples of these output files for two case studies are included in the source git repository.

Use cases

We demonstrate a sample use case of our pipeline with reference to an earlier re-analysis of a published dataset.^13,26 Our tool takes as input at least two data files present as tables of quantitative information, with samples as rows and features as columns. A list of names corresponding to the names of these data blocks are required. A file containing class information is also required as a list of newline separated values. Examples of these data and class files for two case studies are included in the source git repository. Other command line arguments are also possible pertaining to distance metrics of choice for prediction, number of features to select and others. A full description of these can be obtained by running Rscript run_pipeline.R -h, which will list every flag in detail. Because of the number of command line arguments, an option is provided to pass these parameters as a json file to the pipeline. Examples of these json files for two case studies are included in the source git repository.

Example data included within the multiomics package

Regarding input data, some example data²⁷ is provided as part of our R package.

library(multiomics)
data(two_omics)
help(two_omics)

> names(two_omics)
# [1] "classes"  "lipidome"  "metabolome"

> sapply(two_omics, dim)
$classes
# NULL

$lipidome
# [1] 100 3357

$metabolome
# [1] 100 150

Alternatively, you may download this from our git repository directly. This is a subset of anonymised clinical data provided in a separate publication.²⁷

Example processing workflow

We provide a fully processed dataset as a guide for the user. The steps below can be reproduced by downloading the R data object with the following command:

url <- paste( 
  "https://gitlab.com/tyagilab/sars-cov-2/", 
  "/raw/master/results/case_study_2/RData.RData", 
  sep="-"
)
download.file(url, "RData.RData)
load("RData.RData")
ls()
# [1] "argv"       "classes"          "data"
# [4] "data_imp"     "data_pca_multilevel"   "data_plsda"
# [7] "data_splsda"   "diablo"           "dist_diablo"
# [10] "dist_plsda"    "dist_splsda"        "linkage"
# [13] "mappings"     "pca_impute"         "pca_withna"
# [16] "pch"        "perf_diablo"        "tuned_diablo"
# [19] "tuned_splsda"

Inspecting the minimum required input (classes and data) reveals the following:

# number of samples
> length(classes)
# [1] 100

# data dimensions
> sapply(data, dim)
#   lipidome metabolome proteome transcriptome
# [1,]     100    100     100      100
# [2,]    3357    150    517    13263

> table(classes)
# classes
# Less severe More severe
#      49      51

> head(data$lipidome[,1:3])
# AC.10.0_RT_6.936 AC.12.0_RT_7.955 AC.13.0_RT_8.306
# C1     18.13745    16.84196    12.84435
# C10     20.48135    18.06048    15.17862
# C100    22.32588    21.30632    14.91515
# C101    20.56189    18.84777    14.46379
# C102    22.28591    17.98330    14.90019
# C103    18.18658    16.97716    13.36094

Data preprocessing

First, data is filtered if associated options are specified by the user. Features with missing values across sample groups are discarded by default. The user can also choose to filter out features (columns) exceeding a certain threshold of missing values.

Imputing missing values is optional as PLS-derived methods can function without this step. However, we include this information in case the user would like to perform this step manually. Remaining missing values can be imputed by the user-specified --icomp flag. Imputation is effective when the quantity of missing values is <20% of the data. To investigate if the data has been significantly changed, the user can plot a correlation plot of the principal components before and after imputation. Since imputation can take a long time, especially for large datasets, the imputed data is saved by default and the user can load it in directly as input if desired.

If the study design is longitudinal (e.g. has repeated measurements on the same sample), then the --pch flag should be enabled by the user. The user should pass in a file with the same format as the classes file, but containing information regarding the repeated measurements.^23,28 Providing this information allows the pipeline to adjust for this internally.

Method parameters

Most of the parameters for the machine learning algorithms are specified by the user. These cover the three methods PLSDA (partial least squares discriminant analysis), sPLSDA (sparse PLSDA) and multi-block sPLSDA (also known as DIABLO). The underlying methods are implemented within the mixOmics software package and more information is available on their website http://mixomics.org/. For each method, a distance metric is specified, either “max.dist”, “centroids.dist” or “mahalanobis.dist”. Unlike PLSDA, sPLSDA and multi-block sPLSDA focus on selecting subset of the most relevant features and therefore require a user-specified list describing the quantity of features to be selected from the data. The number of components to derive for each method is also provided. For this section, several exploratory runs with a wide range can be carried out to find the optimal configuration of features, e.g. starting at 5,10,30,50,100, inspecting subsequent output and further narrowing the range. The user can specify a few additional special parameters to the multi-block sPLSDA (block.splsda) function. The linkage parameter is a continuous value from 0 to 1, and describes the type of analysis, with a value closer to 0 prioritising class discrimination and a value closer to 1 prioritising correlation between data sets. Meanwhile, setting the number of multi-block sPLSDA components to 0 causes the pipeline to perform parameter tuning internally. Note that this can take a long time, and scales exponentially per added block of omics data. The user can also specify the number of cpus to be used for parallel processing, which mainly affects parameter tuning. Using our example, these arguments are provided here:

> argv
# ...
# $ncpus
# [1] 16

# $diablocomp
# [1] 0

# $linkage
# [1] 0.1

# $diablo_keepx
# [1] "5,6,7,8,9,10,30"

# $pcomp
# [1] 10

# $plsdacomp
# [1] 2

# $splsdacomp
# [1] 2

# $splsda_keepx
# [1] "5,6,7,8,9,10,30"

# $dist_plsda
# [1] "centroids.dist"

# $dist_splsda
# [1] "centroids.dist"

# $dist_diablo
# [1] "centroids.dist"
# ...

Performance metrics

To examine the performance of each method, “M-fold” or “leave-one-out” cross-validation is performed to generate error rate plots. To account for cases where sample classes are imbalanced, balanced error rates which simply averages the class-wise error rates are also calculated and shown [Figure 3].

Figure 3. Example error rate plot.

Error rates are calculated by “leave-one-out” cross-validation implemented in mixOmics. These plots are generated for each analysis type (PLSDA/sPLSDA/DIABLO). An example showing error rates for DIABLO is shown here. This figure was originally published on gitlab under a CC-BY-3.0 AU license and is reproduced here with permission.

Result visualisation

Results are exported in a series of plots and compiled into a pdf [Figure 4]. They can also be accessed internally from our provided R data object.

Figure 4. Example results visualisation.

(a) Multi-block sPLSDA (DIABLO) plots for component 1 and 2 can be interpreted similar to a PCA except that the model aims to discriminate the sample groups. (b) Clustered image maps show the relationship between variables and omics data blocks. (c) Barplots of loading weights show the contributions of variables towards each biological condition for each block. (d) Circosplot depicts the high multivariate correlations between the selected features from each block. Line thickness indicates the strength of the correlation. This figure was originally published on gitlab under a CC-BY-3.0 AU license and is reproduced here with permission.

Output control

Pipeline output can be controlled by specifying a number of flags. By default, the pipeline deposits data in the current working directory. This behaviour can be easily modified. Setting outfile_dir specifies the master output directory. An R data object containing objects shown in the loaded RData file can be renamed with the rdata option, generating a file similar to the one used in this example. The plot flag defines the pdf file containing all graphical output as a multi-page pdf of all plots generated in the pipeline. A reproducible script is generated and named by the user with the args flag (this defaults to Rscript.sh).

> argv
# ...
# $outfile_dir
# [1] "../results/"

# $rdata
# [1] "RData.RData"

# $plot
# [1] "Rplots.pdf"

# $args
# [1] "Rscript.sh"
# ...

Reproducibility and integration with mixOmics

Finally, the pipeline has a limited check-pointing built-in. At each milestone in the pipeline, the relevant output is saved and written out as a RData file, similar to the one presented above. This allows the user to manually inspect the data and adjust it to their needs where needed. In the case of completed output, the user can further customise plots and data exports for publication or downstream analysis. Importantly, data objects are compatible with core mixOmics functions, and allows seamless integration with the mixOmics suite of tools if the user intends to extend or perform their own custom analysis workflows.

Data availability

Source data

Primary data was generated by third parties and is publicly available.^27,29 For case study 1, translatome data is available from the source publication as Supplementary Table 1 and proteome data is available as Supplementary Table 2. For case study 2, the authors provided their data in a sql database.

Underlying data

Zenodo: Multi-omics data harmonisation for the discovery of COVID-19 drug targets. https://doi.org/10.5281/zenodo.4602867.¹³

This project contains the following data.

• Documentation in markdown format describing pipeline usage on two case studies.
• Input data files in plain text (see Source Data for more information).
• Graphical output as pdf files and feature weights as text files.
• Source code, including code to reproduce figures in this article and source code for the R package.
• Docker file specifications for use with Docker and singularity images.

Gitlab: SARS-CoV-2.https://gitlab.com/tyagilab/sars-cov-2.¹³

• Documentation in markdown format describing pipeline usage on two case studies.
• Input data files in plain text (see Source Data for more information).
• Graphical output as pdf files and feature weights as text files.
• Source code, including code to reproduce figures in this article and source code for the R package.
• Docker file specifications for use with Docker and singularity images.

The following underlying data is used in this article:

• data_lipidome.tsv (Text file as raw input data (lipidomics) for case study 2.)
• data_metabolome.tsv (Text file as raw input data (metabolomics) for case study 2.)
• classes_diablo.tsv (Text file as raw input data (biological classes) for case study 2.)
• RData.RData (R data object containing all input, intermediate and output data for case study 2.)
• manuscript_figures (Example output plots that can be generated by the pipeline.)^27,29

Code and data is available under the MIT license. Documentation is available under the CC-BY-3.0 AU license.

Extended data

The following extended data is available in the same repository:

• data/case_study_1 (All raw input data for case study 1.)
• data/case_study_2 (All raw input data for case study 2.)
• results/case_study_1 (Example output data for case study 1.)
• results/case_study_2 (Example output data for case study 2.)

Similar to underlying data, extended code and data is available under the MIT license. Documentation is available under the CC-BY-3.0 AU license.

Software availability

• Software available through R directly:

install.packages("devtools")
library("devtools")
install_github("mixOmicsTeam/mixOmics")
install_gitlab("tyagilab/sars-cov-2", subdir="multiomics")

The actual script used to run the pipeline is not directly callable but provided as a separate script.

# this will show you the path to the script
system.file("scripts", "run_pipeline.R", package="multiomics")

• Source code available from: https://gitlab.com/tyagilab/sars-cov-2
• Archived source code at time of publication: https://doi.org/10.5281/zenodo.4562009
• License: MIT License. Documentation provided under a CC-BY-3.0 AU license

The specific version numbers of the packages used are shown below, along with the version of the R installation.

> library(multiomics)
> sessionInfo()
# R version 4.0.3 (2020-10-10)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: Ubuntu 16.04.7 LTS
#
# Matrix products: default
# BLAS:  /usr/lib/atlas-base/atlas/libblas.so.3.0
# LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0
#
# locale:
# [1] LC_CTYPE=C.UTF-8    LC_NUMERIC=C        LC_TIME=C.UTF-8
#  [4] LC_COLLATE=C.UTF-8   LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8
# [7] LC_PAPER=C.UTF-8    LC_NAME=C          LC_ADDRESS=C
# [10] LC_TELEPHONE=C     LC_MEASUREMENT=C.UTF-8  LC_IDENTIFICATION=C

# attached base packages:
# [1] stats  graphics  grDevices utils  datasets  methods  base
# other attached packages:
# [1] multiomics_0.0.0.9000

# loaded via a namespace (and not attached):
# [1] compiler_4.0.3  assertthat_0.2.1 cli_2.3.1  tools_4.0.3  glue_1.4.2
# [6] rlang_0.4.10

Author contributions

Conceptualization, S. T, T. C; Data Curation, S. T, T. C; Formal Analysis, K-A. L-C, T. C; Funding Acquisition, K-A. L-C, S. T; Methodology, A. J. A, K-A. L-C; Project Administration, S. T; Resources, S. T; Supervision, K-A. L-C, S. T; Software, A. J. A, K-A. L-C, T. C; Validation, A. J. A, K-A. L-C, S. T, T. C; Visualization, A. J. A, K-A. L-C, Writing Original Draft Preparation, S. T, T. C; Writing Review & Editing, A. J. A, K-A. L-C, S. T, T. C.

Competing interests

There is no competing interest.

Grant information

S. T acknowledges the AISRF EMCR Fellowship by the Australian Academy of Science and Australian Women Research Success Grant at Monash University. T. C received funding from the Australian Government Research Training Program Scholarship and Monash Faculty of Science Deans Postgraduate Research Scholarship. K-A. L-C was supported in part by the National Health and Medical Research Council (NHMRC) Career Development fellowship (GNT1159458).

Acknowledgements

The authors thank the HPC team at Monash eResearch Centre for their continuous personnel support. This work was supported by the MASSIVE HPC facility. We acknowledge and pay respects to the Elders and Traditional Owners of the land on which our 4 Australian campuses stand.

References

1. Chen T, Tyagi S: Integrative computational epigenomics to build data-driven gene regulation hypotheses. GigaScience. June 2020; 9(6): 1–13. 2047-217X. PubMed Abstract | Publisher Full Text | Free Full Text
2. Maier T, Güell M, Serrano L: Correlation of mRNA and protein in complex biological samples. FEBS Lett. October 2009; 583(24): 3966–3973. 0014-5793. PubMed Abstract | Publisher Full Text
3. Benevento M, Tonge PD, Puri MC, et al.: Proteome adaptation in cell reprogramming proceeds via distinct transcriptional networks. Nat Commun. December 2014; 5(1). 2041-1723. PubMed Abstract | Publisher Full Text
4. Clancy JL, Patel HR, Hussein SMI, et al.: Small RNA changes en route to distinct cellular states of induced pluripotency. Nat Commun. December 2014; 5 (1). 2041-1723. PubMed Abstract | Publisher Full Text
5. Hussein SMI, Puri MC, Tonge PD, et al.: Genome-wide characterization of the routes to pluripotency. Nature. December 2014; 516(7530): 198–206. 0028-0836, 1476-4687. PubMed Abstract | Publisher Full Text
6. Lee D-S, Shin J-Y, Tonge PD, et al.: An epigenomic roadmap to induced pluripotency reveals DNA methylation as a reprogramming modulator. Nat Commun. December 2014; 5(1). 2041-1723. PubMed Abstract | Publisher Full Text | Free Full Text
7. Tonge PD, Corso AJ, Monetti C, et al.: Divergent reprogramming routes lead to alternative stem-cell states. Nature. December 2014; 516(7530): 192–197. 0028-0836, 1476-4687. PubMed Abstract | Publisher Full Text
8. Angermueller C, Clark SJ, Lee HJ, et al.: Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat Methods. January 2016; 13(3): 229–232. 1548-7091, 1548-7105. PubMed Abstract | Publisher Full Text | Free Full Text
9. Argelaguet R, Clark SJ, Mohammed H, et al.: Multi-omics profiling of mouse gastrulation at single-cell resolution. Nature. December 2019; 576(7787): 487–491. 0028-0836, 1476-4687. PubMed Abstract | Publisher Full Text | Free Full Text
10. Leinonen R, Sugawara H, Shumway M: The sequence read archive. Nucleic Acids Res. November 2010; 39(Database): D19–D21. 0305-1048, 1362-4962. PubMed Abstract | Publisher Full Text | Free Full Text
11. Mashima J, Kodama Y, Fujisawa T, et al.: DNA data bank of Japan. Nucleic Acids Res. October 2016; 45(D1): D25–D31. 0305-1048, 1362-4962. PubMed Abstract | Publisher Full Text | Free Full Text
12. Athar A, Füllgrabe A, George N, et al.: ArrayExpress update – from bulk to single-cell expression data. Nucleic Acids Res. October 2018; 47(D1): D711–D715. 0305-1048, 1362-4962. PubMed Abstract | Publisher Full Text | Free Full Text
13. Chen T, Philip M, Lê Cao K-A, et al.: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets. Brief. Bioinform. May 2021. 1467-5463, 1477-4054. PubMed Abstract | Publisher Full Text | Free Full Text
14. Rohart F, Gautier Be, Singh A, et al.: mixOmics: An r package for ‘omics feature selection and multiple data integration. PLoS Comput Biol. November 2017; 13(11): e1005752. 1553-7358. PubMed Abstract | Publisher Full Text | Free Full Text
15. R Core Team: R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. Reference Source
16. Chacon S, Straub B Pro Git. Apress; 2014. 9781484200773, 9781484200766. Publisher Full Text
17. Merkel D: Docker: Lightweight Linux containers for consistent development and deployment. Linux J. March 2014; 2014(239). 1075-3583.
18. Kurtzer GM: Singularity 2.1.2 - Linux application and environment containers for science.August 2016. Publisher Full Text
19. Kurtzer GM, Sochat V, Bauer MW: Singularity: Scientific containers for mobility of compute. PLoS ONE. May 2017; 12(5): e0177459. 1932-6203. PubMed Abstract | Publisher Full Text | Free Full Text
20. Lê Cao K-A, Rossouw D, Robert-Granié Christèle, et al.: A sparse PLS for variable selection when integrating omics data. Stat. Appl. Genet. Mol. January 2008; 7(1). ISSN 1544-6115. PubMed Abstract | Publisher Full Text
21. Lê Cao K-A, Boitard S, Besse P: Sparse PLS discriminant analysis: Biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinf. June 2011; 12(1). 1471-2105. PubMed Abstract | Publisher Full Text | Free Full Text
22. González I, Lê Cao K-A, Davis MJ, et al.: Visualising associations between paired ‘omics’ data sets. BioData Min. November 2012; 5(1): 1–23. 1756-0381. PubMed Abstract | Publisher Full Text | Free Full Text
23. Liquet B, Lê Cao K-A, Hocini H, et al.: A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinf. December 2012; 13(1): 1–14. 1471-2105. PubMed Abstract | Publisher Full Text | Free Full Text
24. Singh A, Shannon CP, Gautier B, et al.: DIABLO: An integrative approach for identifying key molecular drivers from multi-omics assays. Method. Biochem. Anal. January 2019; 35(17): 3055–3062. 1367-4803, 1460-2059. PubMed Abstract | Publisher Full Text | Free Full Text
25. Smoot ME, Ono K, Ruscheinski J, et al.: Cytoscape 2.8: New features for data integration and network visualization. Method. Biochem. Anal. December 2010; 27(3): 431–432. 1367-4803, 1460-2059. PubMed Abstract | Publisher Full Text | Free Full Text
26. Chen T, Philip M, Lê Cao K-A, et al.: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets. Brief. Bioinform. May 2021; 0(0): 0. 1467-5463, 1477-4054. PubMed Abstract | Publisher Full Text | Free Full Text
27. Overmyer KA, Shishkova E, Miller IJ, et al.: Large-scale multi-omic analysis of COVID-19 severity. Cell Systems. January 2021; 12(1): 23–40.e7. 2405-4712. PubMed Abstract | Publisher Full Text | Free Full Text
28. Westerhuis JA, van Velzen EJJ, Hoefsloot HCJ, et al.: Multivariate paired data analysis: Multilevel PLSDA versus OPLSDA. Metabolomics. October 2009; 6(1): 119–128. 1573-3882, 1573-3890. PubMed Abstract | Publisher Full Text | Free Full Text
29. Bojkova D, Klann K, Koch B, et al.: Proteomics of SARS-CoV-2-infected host cells reveals therapy targets. Nature. May 2020; 583(7816): 469–472. 0028-0836, 1476-4687. PubMed Abstract | Publisher Full Text

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 06 Jul 2021

Author details Author details

Tyrone Chen
Roles: Conceptualization, Data Curation, Formal Analysis, Software, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Al J Abadi
Roles: Methodology, Software, Validation, Visualization, Writing – Review & Editing

Kim-Anh Lê Cao
Roles: Formal Analysis, Funding Acquisition, Methodology, Software, Supervision, Validation, Visualization, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

S. T acknowledges the AISRF EMCR Fellowship by the Australian Academy of Science and Australian Women ResearchSuccess Grant at Monash University. T. C received funding from the Australian Government Research Training ProgramScholarship and Monash Faculty of Science Dean’s Postgraduate Research Scholarship. K-A. L-C was supported in partby the National Health and Medical Research Council (NHMRC) Career Development fellowship (GNT1159458)
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

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Published: 02 Aug 2023, 10:538

https://doi.org/10.12688/f1000research.53453.2

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Published: 06 Jul 2021, 10:538

https://doi.org/10.12688/f1000research.53453.1

© 2021 Chen T et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chen T, Abadi AJ, Lê Cao KA and Tyagi S. multiomics: A user-friendly multi-omics data harmonisation R pipeline [version 1; peer review: 2 not approved]. F1000Research 2021, 10:538 (https://doi.org/10.12688/f1000research.53453.1)

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Reviewer Report 08 Dec 2021

Arjun Krishnan, Department of Computational Mathematics, Science, and Engineering & Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824-1226, USA

Not Approved

https://doi.org/10.5256/f1000research.56837.r89102

In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?
How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (2021¹)?
The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
The writing can be considerably tightened.
- The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.
- Figure 1 is not contributing to the exposition and can be removed.
- What do the following statements on Page 4 at the beginning of passage 3 mean?
  - “implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.
  - “It is portable with multiple implementations”
- Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Partly
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Partly
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

References

1. Chen T, Philip M, Lê Cao KA, Tyagi S: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.Brief Bioinform. 2021. PubMed Abstract | Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Computational biology, Bioinformatics, Machine learning, Software development

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Author Response 17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

17 Nov 2023

Author Response
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as ... Continue reading
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?

----
Conventional biological pipelines involve multiple layers of data preprocessing and summarisation, where at each stage information is irreversibly lost. Therefore, in this context, unrefined and information rich refers to data in a primary form at a stage before heavy information loss occurs, such as matrices of molecular abundance data. Our pipeline takes these quantitative matrices as input, and can identify low-level correlations across individual molecules. We made this point clearer in introduction paragraph 5 of the latest version of the manuscript.

We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined.
----
> 2. How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (20211)?
----
Although the concept and function of the analysis workflow is identical, we saw the gap in the field of developing generic multiomics data integration solutions. Hence, we automated the workflow and the latest version contains major improvements to the internal logic and additional features since the original pipeline was first developed. A new simplified case study is available as well in the latest update and available on github.
----
> 3. The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
----
Data preprocessing steps such as the filtering of low-variance columns and formatting missing/zero values are our own custom additions and not part of the original mixOmics pipeline. These functions were written in collaboration with the original mixOmics authors and designed to create input compatible with mixOmics functions. (ref section: “Data preprocessing”)

Regarding speed and memory bottlenecks, the latest version of the pipeline has been updated to align with the improved parallelisation in the latest version of mixOmics, and now also outputs a more comprehensive table of parameters for easy reuse to skip computationally expensive parameter tuning. To avoid the data imputation bottleneck, we save the imputation output for reuse, therefore imputation only needs to be run once instead of every pipeline iteration (ref section: “Running the pipeline”).

We use mixOmics at the centre of our multiomics pipeline. Here, our key contribution is in the specific context of adding a layer of abstraction for non-expert users, resulting in an end-to-end pipeline for many independent mixOmics functions that can be run in a single command. This is non-trivial and conceptually similar to projects like nf-core (Ewels et al, 2020), where their main contribution to the field is achieving a high degree of automation to streamline complex bioinformatics pipelines. The main advantages are that it is both (a) accessible to new users who can generate a large quantity of relevant information in a single step, as well as being (b) convenient to expert users as an initial screen, as the internal data structures are identical and can be extracted for further analysis.

Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x
----
> 4. The writing can be considerably tightened.

The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.

----
We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined. The information is also condensed as recommended.
----

Figure 1 is not contributing to the exposition and can be removed.

----
We believe that providing a figure targeting a general audience would be helpful, and note that this figure was used in multiple presentations to summarise the importance of multi-omics to a non-expert audience (including non-biologists), who found it helpful based on their feedback. In particular, the low-level correlations across individual molecules that we show are not a part of conventional multi-omics studies and is an important piece of information for most audiences, including biologists. We therefore decided to retain this figure.
----

What do the following statements on Page 4 at the beginning of passage 3 mean?

“implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.

----
This was redundant with a previous statement and is now removed. Original intention was to highlight the uniqueness of mixOmics as a generic data integration method. This point is now emphasised in the introduction section.
----

“It is portable with multiple implementations”

----
“Portable” refers to the usability of our package across different machines and operating systems. “Multiple implementations” referred to the original availability of our software as both a docker container as well as a R package.

In the latest version of the manuscript, we removed “multiple implementations” since the latest version of our software does not use a docker container, as we considered this to be now redundant with the R package.

As for “portability”, this still holds across different linux machines, but we did not test this for use on windows or mac, and we now clarified this point in the installation instructions for the github repository. We prioritised the linux version since the pipeline can be computationally expensive, and it would ideally be run on high performance compute clusters which run on linux.
----

Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

----
We thank the reviewers for catching this, now fixed.
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?

----
Conventional biological pipelines involve multiple layers of data preprocessing and summarisation, where at each stage information is irreversibly lost. Therefore, in this context, unrefined and information rich refers to data in a primary form at a stage before heavy information loss occurs, such as matrices of molecular abundance data. Our pipeline takes these quantitative matrices as input, and can identify low-level correlations across individual molecules. We made this point clearer in introduction paragraph 5 of the latest version of the manuscript.

We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined.
----
> 2. How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (20211)?
----
Although the concept and function of the analysis workflow is identical, we saw the gap in the field of developing generic multiomics data integration solutions. Hence, we automated the workflow and the latest version contains major improvements to the internal logic and additional features since the original pipeline was first developed. A new simplified case study is available as well in the latest update and available on github.
----
> 3. The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
----
Data preprocessing steps such as the filtering of low-variance columns and formatting missing/zero values are our own custom additions and not part of the original mixOmics pipeline. These functions were written in collaboration with the original mixOmics authors and designed to create input compatible with mixOmics functions. (ref section: “Data preprocessing”)

Regarding speed and memory bottlenecks, the latest version of the pipeline has been updated to align with the improved parallelisation in the latest version of mixOmics, and now also outputs a more comprehensive table of parameters for easy reuse to skip computationally expensive parameter tuning. To avoid the data imputation bottleneck, we save the imputation output for reuse, therefore imputation only needs to be run once instead of every pipeline iteration (ref section: “Running the pipeline”).

We use mixOmics at the centre of our multiomics pipeline. Here, our key contribution is in the specific context of adding a layer of abstraction for non-expert users, resulting in an end-to-end pipeline for many independent mixOmics functions that can be run in a single command. This is non-trivial and conceptually similar to projects like nf-core (Ewels et al, 2020), where their main contribution to the field is achieving a high degree of automation to streamline complex bioinformatics pipelines. The main advantages are that it is both (a) accessible to new users who can generate a large quantity of relevant information in a single step, as well as being (b) convenient to expert users as an initial screen, as the internal data structures are identical and can be extracted for further analysis.

Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x
----
> 4. The writing can be considerably tightened.

The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.

----
We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined. The information is also condensed as recommended.
----

Figure 1 is not contributing to the exposition and can be removed.

----
We believe that providing a figure targeting a general audience would be helpful, and note that this figure was used in multiple presentations to summarise the importance of multi-omics to a non-expert audience (including non-biologists), who found it helpful based on their feedback. In particular, the low-level correlations across individual molecules that we show are not a part of conventional multi-omics studies and is an important piece of information for most audiences, including biologists. We therefore decided to retain this figure.
----

What do the following statements on Page 4 at the beginning of passage 3 mean?

“implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.

----
This was redundant with a previous statement and is now removed. Original intention was to highlight the uniqueness of mixOmics as a generic data integration method. This point is now emphasised in the introduction section.
----

“It is portable with multiple implementations”

----
“Portable” refers to the usability of our package across different machines and operating systems. “Multiple implementations” referred to the original availability of our software as both a docker container as well as a R package.

In the latest version of the manuscript, we removed “multiple implementations” since the latest version of our software does not use a docker container, as we considered this to be now redundant with the R package.

As for “portability”, this still holds across different linux machines, but we did not test this for use on windows or mac, and we now clarified this point in the installation instructions for the github repository. We prioritised the linux version since the pipeline can be computationally expensive, and it would ideally be run on high performance compute clusters which run on linux.
----

Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

----
We thank the reviewers for catching this, now fixed.
Competing Interests: There is no competing interest Close
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COMMENTS ON THIS REPORT

Author Response 17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

17 Nov 2023

Author Response
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as ... Continue reading
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?

----
Conventional biological pipelines involve multiple layers of data preprocessing and summarisation, where at each stage information is irreversibly lost. Therefore, in this context, unrefined and information rich refers to data in a primary form at a stage before heavy information loss occurs, such as matrices of molecular abundance data. Our pipeline takes these quantitative matrices as input, and can identify low-level correlations across individual molecules. We made this point clearer in introduction paragraph 5 of the latest version of the manuscript.

We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined.
----
> 2. How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (20211)?
----
Although the concept and function of the analysis workflow is identical, we saw the gap in the field of developing generic multiomics data integration solutions. Hence, we automated the workflow and the latest version contains major improvements to the internal logic and additional features since the original pipeline was first developed. A new simplified case study is available as well in the latest update and available on github.
----
> 3. The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
----
Data preprocessing steps such as the filtering of low-variance columns and formatting missing/zero values are our own custom additions and not part of the original mixOmics pipeline. These functions were written in collaboration with the original mixOmics authors and designed to create input compatible with mixOmics functions. (ref section: “Data preprocessing”)

Regarding speed and memory bottlenecks, the latest version of the pipeline has been updated to align with the improved parallelisation in the latest version of mixOmics, and now also outputs a more comprehensive table of parameters for easy reuse to skip computationally expensive parameter tuning. To avoid the data imputation bottleneck, we save the imputation output for reuse, therefore imputation only needs to be run once instead of every pipeline iteration (ref section: “Running the pipeline”).

We use mixOmics at the centre of our multiomics pipeline. Here, our key contribution is in the specific context of adding a layer of abstraction for non-expert users, resulting in an end-to-end pipeline for many independent mixOmics functions that can be run in a single command. This is non-trivial and conceptually similar to projects like nf-core (Ewels et al, 2020), where their main contribution to the field is achieving a high degree of automation to streamline complex bioinformatics pipelines. The main advantages are that it is both (a) accessible to new users who can generate a large quantity of relevant information in a single step, as well as being (b) convenient to expert users as an initial screen, as the internal data structures are identical and can be extracted for further analysis.

Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x
----
> 4. The writing can be considerably tightened.

The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.

----
We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined. The information is also condensed as recommended.
----

Figure 1 is not contributing to the exposition and can be removed.

----
We believe that providing a figure targeting a general audience would be helpful, and note that this figure was used in multiple presentations to summarise the importance of multi-omics to a non-expert audience (including non-biologists), who found it helpful based on their feedback. In particular, the low-level correlations across individual molecules that we show are not a part of conventional multi-omics studies and is an important piece of information for most audiences, including biologists. We therefore decided to retain this figure.
----

What do the following statements on Page 4 at the beginning of passage 3 mean?

“implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.

----
This was redundant with a previous statement and is now removed. Original intention was to highlight the uniqueness of mixOmics as a generic data integration method. This point is now emphasised in the introduction section.
----

“It is portable with multiple implementations”

----
“Portable” refers to the usability of our package across different machines and operating systems. “Multiple implementations” referred to the original availability of our software as both a docker container as well as a R package.

In the latest version of the manuscript, we removed “multiple implementations” since the latest version of our software does not use a docker container, as we considered this to be now redundant with the R package.

As for “portability”, this still holds across different linux machines, but we did not test this for use on windows or mac, and we now clarified this point in the installation instructions for the github repository. We prioritised the linux version since the pipeline can be computationally expensive, and it would ideally be run on high performance compute clusters which run on linux.
----

Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

----
We thank the reviewers for catching this, now fixed.
> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?

----
Conventional biological pipelines involve multiple layers of data preprocessing and summarisation, where at each stage information is irreversibly lost. Therefore, in this context, unrefined and information rich refers to data in a primary form at a stage before heavy information loss occurs, such as matrices of molecular abundance data. Our pipeline takes these quantitative matrices as input, and can identify low-level correlations across individual molecules. We made this point clearer in introduction paragraph 5 of the latest version of the manuscript.

We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined.
----
> 2. How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (20211)?
----
Although the concept and function of the analysis workflow is identical, we saw the gap in the field of developing generic multiomics data integration solutions. Hence, we automated the workflow and the latest version contains major improvements to the internal logic and additional features since the original pipeline was first developed. A new simplified case study is available as well in the latest update and available on github.
----
> 3. The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
----
Data preprocessing steps such as the filtering of low-variance columns and formatting missing/zero values are our own custom additions and not part of the original mixOmics pipeline. These functions were written in collaboration with the original mixOmics authors and designed to create input compatible with mixOmics functions. (ref section: “Data preprocessing”)

Regarding speed and memory bottlenecks, the latest version of the pipeline has been updated to align with the improved parallelisation in the latest version of mixOmics, and now also outputs a more comprehensive table of parameters for easy reuse to skip computationally expensive parameter tuning. To avoid the data imputation bottleneck, we save the imputation output for reuse, therefore imputation only needs to be run once instead of every pipeline iteration (ref section: “Running the pipeline”).

We use mixOmics at the centre of our multiomics pipeline. Here, our key contribution is in the specific context of adding a layer of abstraction for non-expert users, resulting in an end-to-end pipeline for many independent mixOmics functions that can be run in a single command. This is non-trivial and conceptually similar to projects like nf-core (Ewels et al, 2020), where their main contribution to the field is achieving a high degree of automation to streamline complex bioinformatics pipelines. The main advantages are that it is both (a) accessible to new users who can generate a large quantity of relevant information in a single step, as well as being (b) convenient to expert users as an initial screen, as the internal data structures are identical and can be extracted for further analysis.

Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x
----
> 4. The writing can be considerably tightened.

The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.

----
We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined. The information is also condensed as recommended.
----

Figure 1 is not contributing to the exposition and can be removed.

----
We believe that providing a figure targeting a general audience would be helpful, and note that this figure was used in multiple presentations to summarise the importance of multi-omics to a non-expert audience (including non-biologists), who found it helpful based on their feedback. In particular, the low-level correlations across individual molecules that we show are not a part of conventional multi-omics studies and is an important piece of information for most audiences, including biologists. We therefore decided to retain this figure.
----

What do the following statements on Page 4 at the beginning of passage 3 mean?

“implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.

----
This was redundant with a previous statement and is now removed. Original intention was to highlight the uniqueness of mixOmics as a generic data integration method. This point is now emphasised in the introduction section.
----

“It is portable with multiple implementations”

----
“Portable” refers to the usability of our package across different machines and operating systems. “Multiple implementations” referred to the original availability of our software as both a docker container as well as a R package.

In the latest version of the manuscript, we removed “multiple implementations” since the latest version of our software does not use a docker container, as we considered this to be now redundant with the R package.

As for “portability”, this still holds across different linux machines, but we did not test this for use on windows or mac, and we now clarified this point in the installation instructions for the github repository. We prioritised the linux version since the pipeline can be computationally expensive, and it would ideally be run on high performance compute clusters which run on linux.
----

Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

----
We thank the reviewers for catching this, now fixed.
Competing Interests: There is no competing interest Close
Report a concern

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Reviewer Report 29 Nov 2021

Javad Zahiri, Department of Neuroscience, University of California San Diego, La Jolla, California, USA

Not Approved

https://doi.org/10.5256/f1000research.56837.r98916

In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

No
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Bioinformatics, computational genomics, machine learning

CITE

Report a concern

Author Response 17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

17 Nov 2023

Author Response

> In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of ... Continue reading > In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

----
We agree and acknowledge that we did not sufficiently test the installation process of the pipeline to be usable for non-expert users. Since the original submission, we introduced a new automated test involving a full run of the pipeline on a new case study (a recent example is publicly visible online here), made major changes to the internal logic of the pipeline, and improved the install experience for users. We also now use a new simplified case study, and provided the associated data internally within the package as well as through downloadable text files as a backup option. The latest version of the pipeline should now be installable and accessible following the instructions provided. Our publicly available github issue tracker remains open for any bug reports or feature requests.
----

> Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

----
We agree that providing some rationale for using this tool over others available is useful for readers. We note that our previous review covered this topic in significant detail (Chen & Tyagi, 2020), and provides strong justification for its use, since it is one of the only methods that is generic in scope as well as input data, compared to most existing methods that make restrictive assumptions or require highly specific input data. In the latest manuscript text, we made the above point clear in paragraph 5, while the new paragraphs 2,3,5 together provide a summary of key points to justify its use over the current ecosystem of tools, and this information combined with the cited review places this tool in context of the field.

Chen T., Tyagi S., Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, https://doi.org/10.1093/gigascience/giaa064
----
> In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

----
We agree and acknowledge that we did not sufficiently test the installation process of the pipeline to be usable for non-expert users. Since the original submission, we introduced a new automated test involving a full run of the pipeline on a new case study (a recent example is publicly visible online here), made major changes to the internal logic of the pipeline, and improved the install experience for users. We also now use a new simplified case study, and provided the associated data internally within the package as well as through downloadable text files as a backup option. The latest version of the pipeline should now be installable and accessible following the instructions provided. Our publicly available github issue tracker remains open for any bug reports or feature requests.
----

> Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

----
We agree that providing some rationale for using this tool over others available is useful for readers. We note that our previous review covered this topic in significant detail (Chen & Tyagi, 2020), and provides strong justification for its use, since it is one of the only methods that is generic in scope as well as input data, compared to most existing methods that make restrictive assumptions or require highly specific input data. In the latest manuscript text, we made the above point clear in paragraph 5, while the new paragraphs 2,3,5 together provide a summary of key points to justify its use over the current ecosystem of tools, and this information combined with the cited review places this tool in context of the field.

Chen T., Tyagi S., Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, https://doi.org/10.1093/gigascience/giaa064
----
Competing Interests: There is no competing interest. Close
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COMMENTS ON THIS REPORT

Author Response 17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

17 Nov 2023

Author Response

> In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of ... Continue reading > In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

----
We agree and acknowledge that we did not sufficiently test the installation process of the pipeline to be usable for non-expert users. Since the original submission, we introduced a new automated test involving a full run of the pipeline on a new case study (a recent example is publicly visible online here), made major changes to the internal logic of the pipeline, and improved the install experience for users. We also now use a new simplified case study, and provided the associated data internally within the package as well as through downloadable text files as a backup option. The latest version of the pipeline should now be installable and accessible following the instructions provided. Our publicly available github issue tracker remains open for any bug reports or feature requests.
----

> Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

----
We agree that providing some rationale for using this tool over others available is useful for readers. We note that our previous review covered this topic in significant detail (Chen & Tyagi, 2020), and provides strong justification for its use, since it is one of the only methods that is generic in scope as well as input data, compared to most existing methods that make restrictive assumptions or require highly specific input data. In the latest manuscript text, we made the above point clear in paragraph 5, while the new paragraphs 2,3,5 together provide a summary of key points to justify its use over the current ecosystem of tools, and this information combined with the cited review places this tool in context of the field.

Chen T., Tyagi S., Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, https://doi.org/10.1093/gigascience/giaa064
----
> In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

----
We agree and acknowledge that we did not sufficiently test the installation process of the pipeline to be usable for non-expert users. Since the original submission, we introduced a new automated test involving a full run of the pipeline on a new case study (a recent example is publicly visible online here), made major changes to the internal logic of the pipeline, and improved the install experience for users. We also now use a new simplified case study, and provided the associated data internally within the package as well as through downloadable text files as a backup option. The latest version of the pipeline should now be installable and accessible following the instructions provided. Our publicly available github issue tracker remains open for any bug reports or feature requests.
----

> Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

----
We agree that providing some rationale for using this tool over others available is useful for readers. We note that our previous review covered this topic in significant detail (Chen & Tyagi, 2020), and provides strong justification for its use, since it is one of the only methods that is generic in scope as well as input data, compared to most existing methods that make restrictive assumptions or require highly specific input data. In the latest manuscript text, we made the above point clear in paragraph 5, while the new paragraphs 2,3,5 together provide a summary of key points to justify its use over the current ecosystem of tools, and this information combined with the cited review places this tool in context of the field.

Chen T., Tyagi S., Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, https://doi.org/10.1093/gigascience/giaa064
----
Competing Interests: There is no competing interest. Close
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Version 2

VERSION 2 PUBLISHED 06 Jul 2021

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1	2	3	4	5
Version 2 (revision) 02 Aug 23			read	read	read
Version 1 06 Jul 21	read	read

Javad Zahiri, University of California San Diego, La Jolla, USA
Arjun Krishnan, Michigan State University, East Lansing, USA
Giri Narasimhan, Florida International University, Biomolecular Sciences Institute, Miami, Florida, USA

Parshatd Govindasamy, Florida International University, Miami, USA
Junyi Chen, The University of Hong Kong, Hong Kong, Hong Kong
Prashanth Suravajhala, Amrita School of Biotechnology, Kerala, India

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Reviewer Report

7 Views

08 Jan 2025 | for Version 2

Prashanth Suravajhala, Amrita School of Biotechnology, Kerala, India

7 Views Cite this report Responses(0)

Approved

Gist/Summary: The authors come up with a multiomics R suite/pipeline that claims to be user-friendly and novel. The authors have come up with a great tool ensuring transcriptomic/protein data are well augmented. While the authors have written the manuscript and executed methods very nicely, I have a few comments which may allow them to improve their works

Tools, viz. COMO, MOMIC and OMIX could be cited providing their limitations. Although a couple of tools were mentioned, the introduction lacks these mentions.
Is this tools applicable for Shiny R?
Is there a benchmark set for Sepsis datasets?

The authors whence mentioning reproducibility may consider citing Minimum Information About Bioinformatics Investigation ( MIABi) guidelines

The git/bitbucket pages may be mentioned
Scores on a scale of 0-5 with 5 being the best,

Language : 4
Novelty: 3
Brevity: 4
Scope and relevance: 4

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Yes
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

Yes

References

1. Chen T, Philip M, Lê Cao KA, Tyagi S: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.Brief Bioinform. 2021; 22 (6). PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Systems genomics of rare diseases

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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6 Views

14 Sep 2024 | for Version 2

Junyi Chen, The University of Hong Kong, Hong Kong, Hong Kong

6 Views Cite this report Responses(0)

Not Approved

The multiomics pipeline aims to address an important need for harmonizing multi-omics data. However, there are some limitations that temper its potential usefulness:

The case study presented uses a small sample size of only 12 samples across 3 different omic layers. Given the complexity of integrating diverse, high-dimensional data, larger sample sizes would be needed to draw meaningful conclusions.

Further, the case study lacks a comprehensive demonstration of analyzing and interpreting results. The vignette focuses only on printing data shapes and tables without including actual figures/visualizations of the analysis outputs such as feature selection results, dimensionality reduction plots, or correlation networks.

Without a more thorough walkthrough on a real-world dataset, it is difficult to assess how well the pipeline performs on complex, noisy multi-omics data typical of real studies. The outputs may overfit or identify spurious correlations given the small sample.

Other than ease-of-use advantages, it remains unclear what are the other biological insights this approach enables compared to existing tools. Integration with visualization packages could help demonstrate the pipeline's utility.

Also, there is insufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool. In the step:

"
url <- paste( "https://github.com/tyronechen/SARS-CoV-2/", "raw/master/results/case_study_3/data. RData", sep="" )
"
we cannot download the data correctly.

In summary, while the concept is appealing, the case study and documentation fall short of demonstrating the practical applicability and performance of this pipeline on challenging multi-omics research questions. The small test data and lack of detailed validation limit confidence in results for real-world use cases. Expanding the case studies and results transparency would strengthen the package.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Yes
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Bioinformatics, Machine learning, scRNA-seq

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11 Views

21 Jun 2024 | for Version 2

Giri Narasimhan, Florida International University, Biomolecular Sciences Institute, Miami, Florida, USA

Parshatd Govindasamy, Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, Florida, USA

11 Views Cite this report Responses(0)

Not Approved

Comments on Version 2:
Integration of multiple omics data sets is important and meaningful. The gaps in current tools and techniques are well articulated.

I agree with previous reviewers that Fig. 1 does a poor job of conveying the core ideas. Instead of calling the 3 corners “Omics data layer 1”, “Omics data layer 2”, “Omics data layer 3”, call them, for example, “Metabolomics”, “Transcriptomics”, and “Proteomics”, or something along those lines. Instead of entities like A, B, C, X, Y, Z, use real names of entities (genes, proteins, etc.) from your data set.
Fig. 2 has the same problem. I found it hard to connect to the multiomics context. What is the equivalent of “Feature 1 and Feature 2” or “Treatment 1” and “Treatment 2” in the metabolomics, genomics, and proteomics contexts?
The instructions were fine at the start, but then became very inconsistent. A “>” was added prior to a command in some places and not in others. It is important to be consistent.
One important typo in the instructions was the extra space in a file name. A search with “data. RData” would show this error, probably caused by a cut-and-paste problem. This happens at 2-3 places:

url <- paste(
     "https://github.com/tyronechen/SARS-CoV-2/",
     "raw/master/results/case_study_3/data. RData",
     sep=""
)

I don’t see instructions to generate the visualizations in Fig. 3. Nor do I see the file “Rplots.pdf”. It may have to do with setting the $outfile_dir, for which no explicit instructions are provided. Thus, as a reader/reviewer, the inability to see the final visualizations is problematic. I had the same problem with the 3 other R scripts provided on the Github site.
Finally, it is important to show that interpreting the figures is natural and useful. A discussion of the biological interpretation of the results presented in Fig, 3 (instead of a generic caption) would have helped this effort.
Also, it would have been useful to include a clear discussion and/or example of what was discovered here (for this dataset) that could not have been discovered without an integrated analysis of the same dataset.

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Partly
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

No
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

No
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Microbiome analysis

We confirm that we have read this submission and believe that we have an appropriate level of expertise to state that we do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report

72 Views

08 Dec 2021 | for Version 1

Arjun Krishnan, Department of Computational Mathematics, Science, and Engineering & Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824-1226, USA

72 Views Cite this report Responses(1)

Not Approved

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?
How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (2021¹)?
The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
The writing can be considerably tightened.
- The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.
- Figure 1 is not contributing to the exposition and can be removed.
- What do the following statements on Page 4 at the beginning of passage 3 mean?
  - “implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.
  - “It is portable with multiple implementations”
- Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

Is the rationale for developing the new software tool clearly explained?

Yes
Is the description of the software tool technically sound?

Partly
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Partly
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

References

1. Chen T, Philip M, Lê Cao KA, Tyagi S: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.Brief Bioinform. 2021. PubMed Abstract | Publisher Full Text

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Computational biology, Bioinformatics, Machine learning, Software development

Respond to this report

Responses (1)

Author Response

17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

> In this article, Chen and colleagues present an R pipeline for multi-omics data analysis that can potentially accept unrefined data and produce convenient outputs. The pipeline is available as an R package and as Docker/Singularity containers. It is built on top of and closely integrated with the popular mixOmics package. Though this work could be useful, in its current form, it is unfortunately unclear what the new contributions are.

Major comments

The authors talk about the “lack of a single or multi-omics pipeline which can accept data in an unrefined, information-rich form pre-integration and subsequently generate output for further investigation”. What does “unrefined and information-rich mean” mean? As this is the primary motivator for this new pipeline, it needs to be explained clearly, especially in terms of the content and structure of data that multiomics can accept but existing packages like mixOmics cannot?

----
Conventional biological pipelines involve multiple layers of data preprocessing and summarisation, where at each stage information is irreversibly lost. Therefore, in this context, unrefined and information rich refers to data in a primary form at a stage before heavy information loss occurs, such as matrices of molecular abundance data. Our pipeline takes these quantitative matrices as input, and can identify low-level correlations across individual molecules. We made this point clearer in introduction paragraph 5 of the latest version of the manuscript.

We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined.
----
> 2. How is this pipeline different from the one published by the same authors in Briefings in Bioinformatics: A multi-modal data harmonisation approach for discovery of COVID-19 drug targets (Chen et al. (20211)?
----
Although the concept and function of the analysis workflow is identical, we saw the gap in the field of developing generic multiomics data integration solutions. Hence, we automated the workflow and the latest version contains major improvements to the internal logic and additional features since the original pipeline was first developed. A new simplified case study is available as well in the latest update and available on github.
----
> 3. The proposed pipeline – multiomics – heavily relies on the mixOmics package for all its data preprocessing, multivariate analyses, and plotting. The authors note that the speed and memory bottlenecks (e.g. parameter tuning and data imputation) are still problems. So, is multiomics a convenient wrapper for mixOmics? What are the contributions of the multiomics pipeline in terms of features that are not already part of mixOmics or any other existing multi-omics packages?
----
Data preprocessing steps such as the filtering of low-variance columns and formatting missing/zero values are our own custom additions and not part of the original mixOmics pipeline. These functions were written in collaboration with the original mixOmics authors and designed to create input compatible with mixOmics functions. (ref section: “Data preprocessing”)

Regarding speed and memory bottlenecks, the latest version of the pipeline has been updated to align with the improved parallelisation in the latest version of mixOmics, and now also outputs a more comprehensive table of parameters for easy reuse to skip computationally expensive parameter tuning. To avoid the data imputation bottleneck, we save the imputation output for reuse, therefore imputation only needs to be run once instead of every pipeline iteration (ref section: “Running the pipeline”).

We use mixOmics at the centre of our multiomics pipeline. Here, our key contribution is in the specific context of adding a layer of abstraction for non-expert users, resulting in an end-to-end pipeline for many independent mixOmics functions that can be run in a single command. This is non-trivial and conceptually similar to projects like nf-core (Ewels et al, 2020), where their main contribution to the field is achieving a high degree of automation to streamline complex bioinformatics pipelines. The main advantages are that it is both (a) accessible to new users who can generate a large quantity of relevant information in a single step, as well as being (b) convenient to expert users as an initial screen, as the internal data structures are identical and can be extracted for further analysis.

Ewels, P.A., Peltzer, A., Fillinger, S. et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38, 276–278 (2020). https://doi.org/10.1038/s41587-020-0439-x
----
> 4. The writing can be considerably tightened.

The first two paragraphs in Introduction can be condensed to a few sentences so that the practicalities of multi-omics data analysis can be brought up soon.

----
We considered point (1) in relation to point (4), and made the above information clearer and more concise by reorganising the logic flow of the introduction. As a result, some paragraphs in the introduction are reordered or combined. The information is also condensed as recommended.
----

Figure 1 is not contributing to the exposition and can be removed.

----
We believe that providing a figure targeting a general audience would be helpful, and note that this figure was used in multiple presentations to summarise the importance of multi-omics to a non-expert audience (including non-biologists), who found it helpful based on their feedback. In particular, the low-level correlations across individual molecules that we show are not a part of conventional multi-omics studies and is an important piece of information for most audiences, including biologists. We therefore decided to retain this figure.
----

What do the following statements on Page 4 at the beginning of passage 3 mean?
- “implementing one of the state of the art tools in data harmonisation from the mixOmics R package”.

----
This was redundant with a previous statement and is now removed. Original intention was to highlight the uniqueness of mixOmics as a generic data integration method. This point is now emphasised in the introduction section.
----

“It is portable with multiple implementations”

----
“Portable” refers to the usability of our package across different machines and operating systems. “Multiple implementations” referred to the original availability of our software as both a docker container as well as a R package.

In the latest version of the manuscript, we removed “multiple implementations” since the latest version of our software does not use a docker container, as we considered this to be now redundant with the R package.

As for “portability”, this still holds across different linux machines, but we did not test this for use on windows or mac, and we now clarified this point in the installation instructions for the github repository. We prioritised the linux version since the pipeline can be computationally expensive, and it would ideally be run on high performance compute clusters which run on linux.
----

Fix: “run a pipeline and export output data which can be used for downstream analyses and further downstream analyses".

----
We thank the reviewers for catching this, now fixed.

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Competing Interests

There is no competing interest

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Reviewer Report

97 Views

29 Nov 2021 | for Version 1

Javad Zahiri, Department of Neuroscience, University of California San Diego, La Jolla, California, USA

97 Views Cite this report Responses(1)

Not Approved

Is the rationale for developing the new software tool clearly explained?

Partly
Is the description of the software tool technically sound?

No
Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?

Yes
Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?

Partly
Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Bioinformatics, computational genomics, machine learning

Respond to this report

Responses (1)

Author Response

17 Nov 2023

Tyrone Chen, School of Biological Sciences, Monash University, Clayton, 3800, Australia

> In the present study, "multiomics: A user-friendly multi-omics data harmonisation R pipeline" the authors tried to develop a tool for multiple omics integration and analysis. The problem is of utmost importance. However, the tool needs more work to be suitable for publication.
The major problem is that installation is not easy at all for non-expert users. I had a bunch of biological researchers to install the tool, but they couldn't.

In addition the below codes produce errors:

> metab <- read.table(
"data_metabolomics.tsv", sep="\t", header=TRUE, row.names=1)

> data_names
(the variable has not been defined)

>download.file(url, "RData.RData)

----
We agree and acknowledge that we did not sufficiently test the installation process of the pipeline to be usable for non-expert users. Since the original submission, we introduced a new automated test involving a full run of the pipeline on a new case study (a recent example is publicly visible online here), made major changes to the internal logic of the pipeline, and improved the install experience for users. We also now use a new simplified case study, and provided the associated data internally within the package as well as through downloadable text files as a backup option. The latest version of the pipeline should now be installable and accessible following the instructions provided. Our publicly available github issue tracker remains open for any bug reports or feature requests.
----

> Another important point is comparing multiomics to other recent similar tools like MOVICS and CNet (among several tools) and showing the current tool's strength compared to others.

----
We agree that providing some rationale for using this tool over others available is useful for readers. We note that our previous review covered this topic in significant detail (Chen & Tyagi, 2020), and provides strong justification for its use, since it is one of the only methods that is generic in scope as well as input data, compared to most existing methods that make restrictive assumptions or require highly specific input data. In the latest manuscript text, we made the above point clear in paragraph 5, while the new paragraphs 2,3,5 together provide a summary of key points to justify its use over the current ecosystem of tools, and this information combined with the cited review places this tool in context of the field.

Chen T., Tyagi S., Integrative computational epigenomics to build data-driven gene regulation hypotheses, GigaScience, Volume 9, Issue 6, June 2020, giaa064, https://doi.org/10.1093/gigascience/giaa064
----

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Competing Interests

There is no competing interest.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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multiomics: A user-friendly multi-omics data harmonisation R pipeline

Abstract

Keywords

Introduction

Figure 1. An illustration of a hypothetical multi-omics perspective on a simple biological system.

Methods

Implementation

Docker and singularity containers

Operation

Figure 2. Technical notes for the pipeline.

Running the pipeline

Example output

Use cases

Example data included within the multiomics package

Example processing workflow

Data preprocessing

Method parameters

Performance metrics

Figure 3. Example error rate plot.

Result visualisation

Figure 4. Example results visualisation.

Output control

Reproducibility and integration with mixOmics

Data availability

Source data

Underlying data

Extended data

Software availability

Author contributions

Competing interests

Grant information

Acknowledgements

References

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