Contents

Abstract

autonomics is created to make cross-platform omics analysis intuitive and enjoyable. It contains import + preprocess + analyze + visualize one-liners for RNAseq, MS proteomics, SOMAscan and Metabolon platforms and a generic importer for data from any rectangular omics file. With a focus on not only automation but also customization, these importers have a flexible formula/ block/contrastdefs interface which allows to define any statistical formula, fit any general linear model, quantify any contrast, and use random effects or precision weights if required, building on top of the powerful limma platform for statistical analysis. It also offers exquisite support for analyzing complex designs such as the innovative contrastogram visualization to unravel and summarize the differential expression structure in complex designs. By autonomating repetitive tasks, generifying common steps, and intuifying complex designs, it makes cross-platform omics data analysis a fun experience. Try it and enjoy :-).

Autonomics offers import + preprocess + analyze + visualize one-liners for RNAseq, MS proteomics, SOMAscan and Metabolon platforms, as well a generic importer for data from any rectangular omics file. We will discuss each of these in more detail in the following sections, but all of them follow the following general steps:

1 RNAseq

Autonomics provides ready-to-use importers for both count as well as BAM files, which read / preprocess / analyze RNAseq data. Specific to RNAseq is counting reads using Rsubread::featureCounts (for read_rna_bams)), normalizing for library composition (cpm) or gene length (tpm), estimating voom precision weights, and using these in linear modeling. Let’s illustrate both of these on example datasets:

read_rnaseq_counts

Billing et al. (2019) studied the differentiation of embryonic (E00) into mesenchymal stem cells (E00.8, E01, E02, E05, E15, E30), with similar properties as bone-marrow mesenchymal stem cells (M00). Importing the RNAseq counts:

require(autonomics, quietly = TRUE)
## 
## Attaching package: 'autonomics'
## The following objects are masked from 'package:stats':
## 
##     biplot, loadings
file <- system.file('extdata/billing19.rnacounts.txt', package = 'autonomics')
object <-  read_rnaseq_counts(file = file, fit = 'limma', plot = TRUE, label = 'gene')
##  Read /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/billing19.rnacounts.txt
##               Infer subgroup from sample_ids
##               Infer subgroup from sample_ids
##      Preprocess
##          Keep 24/24 features: count >= 10 (~subgroup)
##          counts: add 0.5
##          cpm:    tmm scale libsizes
##              cpm
##          voom: ~subgroup
##          counts: rm 0.5
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
##                       Intercept  1   .     .   .   .   .   .   . 
##                       E00.8     -1   1     .   .   .   .   .   . 
##                       E01       -1   .     1   .   .   .   .   . 
##                       E02       -1   .     .   1   .   .   .   . 
##                       E05       -1   .     .   .   1   .   .   . 
##                       E15       -1   .     .   .   .   1   .   . 
##                       E30       -1   .     .   .   .   .   1   . 
##                       M00       -1   .     .   .   .   .   .   1
##               lmFit(~subgroup, weights = assays(object)$weights)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       0    24     0    24
##                   2:       E00.8  limma       1     2     1     4
##                   3:         E01  limma       3     2     5     3
##                   4:         E02  limma       2     2     3     4
##                   5:         E05  limma       1     2     2     2
##                   6:         E15  limma       8     9     8    10
##                   7:         E30  limma       7    10     8    10
##                   8:         M00  limma       6     7     6     7
##      Plot summary

read_rnaseq_counts(sfile)

The sfile/sfileby arguments in both importers allow to merge in sample data from a separate file by a specified column. This is especially useful in clinical datasets, such as the GSE161731 Covid-19 dataset, for which both a counts file as well as a sample file can be downloaded from GEO:

basedir <- file.path(tempdir(), 'datasets')
dir.create(basedir, showWarnings = FALSE, recursive = TRUE)
if (!dir.exists(file.path(basedir, 'GSE161731'))){
    sink <- GEOquery::getGEOSuppFiles("GSE161731", baseDir = basedir)
}
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)
object <- read_rnaseq_counts(
  file    = file.path(basedir, 'GSE161731/GSE161731_counts.csv.gz'), 
  sfile   = file.path(basedir, 'GSE161731/GSE161731_counts_key.csv.gz'), 
  by.y    = 'rna_id', 
  formula = ~ gender,
  plot    = TRUE)
##  Read /tmp/RtmpsgvrVb/datasets/GSE161731/GSE161731_counts.csv.gz
##      Merge sdata: /tmp/RtmpsgvrVb/datasets/GSE161731/GSE161731_counts_key.csv.gz
##      Preprocess
##          Keep 18974/60675 features: count >= 10 (~gender)
##          counts: add 0.5
##          cpm:    tmm scale libsizes
##              cpm
##          voom: ~gender
##          counts: rm 0.5
##               Add PCA
##     LinMod
##               Code gender
##                                level
##                     coefficient Female Male
##                       Intercept  1      .  
##                       Male      -1      1
##               lmFit(~gender, weights = assays(object)$weights)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma    2990 14603  3005 14613
##                   2:        Male  limma      40    74  1207  1447
##      Plot summary
## Warning: ggrepel: 96 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

read_rnaseq_counts(block)

The block argument in both importers allows to model e.g. subject_id as a random effect, which is very common in clinical data. Repeating the GEO Covid-19 analysis with subject_id as a block effect. Outcomment the line below, which takes a few minutes to complete.

#object <- read_rnaseq_counts(
#  file  = '~/autonomicscache/datasets/GSE161731/GSE161731_counts.csv.gz', 
#  sfile = '~/autonomicscache/datasets/GSE161731/GSE161731_counts_key.csv.gz', 
#  sfileby = 'rna_id', 
#  subgroupvar = 'gender', 
#  block = 'subject_id')

read_rnaseq_bams

Billing et al. (2016) compared three types of stem cells: embryonic (E), embryonic differentiated into mesenchymal (EM), and bone-marrow mesenchymal (BM). Importing a downsized version of the RNAseq BAM files (with only a subset of all reads):

# not run to avoid issues with R CMD CHECK 
if (requireNamespace('Rsubread')){
  object <- read_rnaseq_bams(
                dir    = download_data('billing16.bam.zip'), 
                paired = TRUE, 
                genome = 'hg38', 
                pca    = TRUE, 
                fit   = 'limma', 
                plot   = TRUE)
}

cpm/tmm/voom effects on power

Proper preprocessing leads to increased power:

  file <- system.file('extdata/billing19.rnacounts.txt', package = 'autonomics')
# log2counts
  object <- read_rnaseq_counts(file, 
       cpm = FALSE, voom = FALSE, fit = 'limma', verbose = FALSE, plot = FALSE)
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
##                       Intercept  1   .     .   .   .   .   .   . 
##                       E00.8     -1   1     .   .   .   .   .   . 
##                       E01       -1   .     1   .   .   .   .   . 
##                       E02       -1   .     .   1   .   .   .   . 
##                       E05       -1   .     .   .   1   .   .   . 
##                       E15       -1   .     .   .   .   1   .   . 
##                       E30       -1   .     .   .   .   .   1   . 
##                       M00       -1   .     .   .   .   .   .   1
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr   upfdr 
##       7      13
# log2cpm
  object <- read_rnaseq_counts(file,
       cpm = TRUE,  voom = FALSE, fit = 'limma', verbose = FALSE, plot = FALSE)
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
##                       Intercept  1   .     .   .   .   .   .   . 
##                       E00.8     -1   1     .   .   .   .   .   . 
##                       E01       -1   .     1   .   .   .   .   . 
##                       E02       -1   .     .   1   .   .   .   . 
##                       E05       -1   .     .   .   1   .   .   . 
##                       E15       -1   .     .   .   .   1   .   . 
##                       E30       -1   .     .   .   .   .   1   . 
##                       M00       -1   .     .   .   .   .   .   1
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr   upfdr 
##      29      34
# log2cpm + voom
  object <- read_rnaseq_counts(file,  # log2 cpm + voom
       cpm = TRUE,  voom = TRUE,  fit = 'limma', verbose = FALSE, plot = FALSE)
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
##                       Intercept  1   .     .   .   .   .   .   . 
##                       E00.8     -1   1     .   .   .   .   .   . 
##                       E01       -1   .     1   .   .   .   .   . 
##                       E02       -1   .     .   1   .   .   .   . 
##                       E05       -1   .     .   .   1   .   .   . 
##                       E15       -1   .     .   .   .   1   .   . 
##                       E30       -1   .     .   .   .   .   1   . 
##                       M00       -1   .     .   .   .   .   .   1
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr   upfdr 
##      28      34

2 Proteingroups/phosphosites

A popular approach in (DDA) MS proteomics data analysis is to use MaxQuant to produce proteinGroups.txt and phospho (STY)Sites.txt files. These can be imported using read_proteingroups / read_phosphosites, which :

LFQ intensities

An LFQ intensities example is the Fukuda et al. (2020) dataset, which compares the proteome of 30d zebrafish juveniles versus adults using label-free quantification. In the volcano plot measured values are shown with circles, imputed values as triangles.

file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
object <- read_maxquant_proteingroups(file = file, plot = TRUE)
##     Read      proteingroups   /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/fukuda20.proteingroups.txt
##               contaminanthdrs /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/contaminants.tsv
##               maxquanthdrs
##     Annotate  Uncollapse        20 proIds into     32  proteins
##               Drop REV_                            32  proteins
##               Annotate                                     0 using uniprothdrs
##                                                            1 using contaminanthdrs
##                                                           29 using maxquanthdrs
##                                                            0 using uniprotrestapi
##               Filter                               32  proteins
##               within proId                         31  proteins  swissprot > trembl
##                                                    25  proteins  fullseq   > fragment
##                                                    20  proteins  protein   > transcript > homolog > prediction > uncertain
##               Add REV__                            20  proteins
##               Recollapse        20 proIds
##     SumExp
##               Replace 0->NA for 27/120 values (in 11/20 features of 6/6 samples)
##               Standardize snames: LFQ intensity 30dpt.R1  ->  30dpt.R1
##               Infer subgroup from sample_ids
##               Retain 19/20 features: ~reverse == ""
##               Retain 18/19 features: ~contaminant == ""
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient X30dpt Adult
##                       Intercept  1      .   
##                       Adult     -1      1
##               lmFit(~subgroup)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       0    18     0    18
##                   2:       Adult  limma       2     2     3     2
##      Plot summary

Normalized ratios

Normalized ratios were used in the proteomics profiling of the earlier described Billing et al. (2019) study, which examined the differentiation of embryonic stem cells (E00) into mesenchymal stem cells (E01,E02, E05, E15, E30), and compared these to bone-marrow derived mesenchymal stem cells (M00). The proteomics profiling experiment was performed using MS1 labeling: a light (L) labeled internal standard was created by mixing all samples in equal amounts, the subsequent samples were either medium (M) or heavy (H) labeled. Not all label combinations were of interest, and the select_subgroups argument allows to specifies which subgroup combinations to retain:

file <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
subgroups <- c('E00_STD', 'E01_STD', 'E02_STD', 'E05_STD', 'E15_STD', 'E30_STD', 'M00_STD')
object <- read_maxquant_proteingroups(file = file, subgroups = subgroups, plot = TRUE)
##     Read      proteingroups   /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/billing19.proteingroups.txt
##               contaminanthdrs /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/contaminants.tsv
##               maxquanthdrs
##     Annotate  Uncollapse        25 proIds into     83  proteins
##               Drop REV_                            45  proteins
##               Annotate                                     0 using uniprothdrs
##                                                            1 using contaminanthdrs
##                                                           34 using maxquanthdrs
##                                                            0 using uniprotrestapi
##               Filter                               45  proteins
##               within proId                         24  proteins  swissprot > trembl
##               Add REV__                            24  proteins
##               Recollapse        25 proIds
##     SumExp
##               Replace NaN->NA for 175/825 values (in 12/25 features of 33/33 samples)
##               Standardize snames: Ratio M/L normalized STD(L).E00(M).E01(H).R1  ->  STD(L).E00(M).E01(H).R1{M/L}
##               Infer subgroup from sample_ids
##               Retain 21/33 samples: ~subgroup %in% subgroups
##               Retain 24/25 features: ~reverse == ""
##               Retain 23/24 features: ~contaminant == ""
##               Retain 22/23 features: non-zero, non-NA, and non-NaN for some sample
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00_STD E01_STD E02_STD E05_STD E15_STD E30_STD M00_STD
##                       Intercept  1       .       .       .       .       .       .     
##                       E01_STD   -1       1       .       .       .       .       .     
##                       E02_STD   -1       .       1       .       .       .       .     
##                       E05_STD   -1       .       .       1       .       .       .     
##                       E15_STD   -1       .       .       .       1       .       .     
##                       E30_STD   -1       .       .       .       .       1       .     
##                       M00_STD   -1       .       .       .       .       .       1
##               lmFit(~subgroup)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       1     5     2     6
##                   2:     E01_STD  limma       0     0     0     1
##                   3:     E02_STD  limma       0     2     1     2
##                   4:     E05_STD  limma       0     0     0     3
##                   5:     E15_STD  limma       2     8     2    11
##                   6:     E30_STD  limma       2     8     2     8
##                   7:     M00_STD  limma       2     5     2     6
##      Plot summary

The phosphosites can be read in a similar fashion:

fosfile <- system.file('extdata/billing19.phosphosites.txt',  package = 'autonomics')
profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
subgroups <- c('E00_STD', 'E01_STD', 'E02_STD', 'E05_STD', 'E15_STD', 'E30_STD', 'M00_STD')
object <- read_maxquant_phosphosites(fosfile = fosfile, profile = profile, subgroups = subgroups, plot = TRUE)
##     Read      proteingroups   /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/billing19.proteingroups.txt
##               phosphosites    /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/billing19.phosphosites.txt
##                                       Read    21 phosphosites in proteins, contaminants, reverse
##                                       Retain  21 phosphosites in single proteingroup
##                                       Retain  21 phosphosites with signal in some sample
##                                       Keep proteingroup uniprots
##               contaminanthdrs /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/contaminants.tsv
##               maxquanthdrs
##     Annotate  Uncollapse        21 fosIds into     53  proteins
##               Drop REV_                            28  proteins
##               Annotate                                     0 using uniprothdrs
##                                                            1 using contaminanthdrs
##                                                           20 using maxquanthdrs
##                                                            0 using uniprotrestapi
##               Filter                               28  proteins
##               within fosId                         16  proteins  swissprot > trembl
##               Add REV__                            16  proteins
##               Recollapse        21 fosIds
##     SumExp
##               Replace NaN->NA for 104/693 values (in 6/21 features of 33/33 samples)
##               Infer subgroup from sample_ids
##               Retain 21/33 samples: ~subgroup %in% subgroups
##               Retain 20/21 features: ~reverse == ""
##               Retain 19/20 features: ~contaminant == ""
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient E00_STD E01_STD E02_STD E05_STD E15_STD E30_STD M00_STD
##                       Intercept  1       .       .       .       .       .       .     
##                       E01_STD   -1       1       .       .       .       .       .     
##                       E02_STD   -1       .       1       .       .       .       .     
##                       E05_STD   -1       .       .       1       .       .       .     
##                       E15_STD   -1       .       .       .       1       .       .     
##                       E30_STD   -1       .       .       .       .       1       .     
##                       M00_STD   -1       .       .       .       .       .       1
##               lmFit(~subgroup)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       3     3     5     3
##                   2:     E01_STD  limma       1     1     1     2
##                   3:     E02_STD  limma       2     0     3     2
##                   4:     E05_STD  limma       1     1     1     4
##                   5:     E15_STD  limma       1     6     2     6
##                   6:     E30_STD  limma       0     4     1     5
##                   7:     M00_STD  limma       2     3     2     4
##      Plot summary

3 Metabolon

Metabolon delivers metabolomics results in the form of an xlsx file, with values of interest likely in the (second) OrigScale sheet. Features are laid out in rows, samples in columns, and additional feature/sample data are self-contained in the file. Metabolon data can be read/analyzed with the autonomics function read_metabolon, as illustrated below on the dataset of Halama and and Atkin (2018), who investigates the effects of hypoglycemia on a number of subjects (SUB) at four different time points (t0, t1, t2, t3):

file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
object <- read_metabolon(file,  subgroupvar = 'subgroup', block = 'Subject', plot = TRUE)
##  Read: /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/atkin.metabolon.xlsx
##               Infer subgroup from sample_ids
##               log2 metabolon
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient t0 t1 t2 t3
##                       Intercept  1  .  .  .
##                       t1        -1  1  .  .
##                       t2        -1  .  1  .
##                       t3        -1  .  .  1
##               Dupcor `Subject`
##               lmFit(~subgroup | Subject)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       0    20     0    20
##                   2:          t1  limma       8     1    10     1
##                   3:          t2  limma       7     3     8     3
##                   4:          t3  limma       1     1     4     2
##      Plot summary

4 Somascan

Somascan results are returned in a txt file with extension .adat. Features are laid out in columns and samples in rows. Rectangles with feature/sample data are also contained in the file. The autonomics function read_somascan reads/analyzes SOMAscan files, additionally filtering samples/features for quality and type. This is illustrated on the above described dataset from Halama and and Atkin (2018), who investigated the effects of hypoglycemia on a number of subjects (SUB) at four different time points (t0, t1, t2, t3):

file <- system.file('extdata/atkin.somascan.adat', package = 'autonomics')
object <- read_somascan(file, block = 'Subject', plot = TRUE)
##  Read: /tmp/RtmphdRJQu/Rinst171f806f813fd2/autonomics/extdata/atkin.somascan.adat
##               log2 somascan
##               Add PCA
##     LinMod
##               Code subgroup
##                                level
##                     coefficient t0 t1 t2 t3
##                       Intercept  1  .  .  .
##                       t1        -1  1  .  .
##                       t2        -1  .  1  .
##                       t3        -1  .  .  1
##               Dupcor `Subject`
##               lmFit(~subgroup | Subject)
##                      coefficient    fit downfdr upfdr downp   upp
##                           <fctr> <fctr>   <int> <int> <int> <int>
##                   1:   Intercept  limma       0    20     0    20
##                   2:          t1  limma       0     1     1     3
##                   3:          t2  limma       2     2     2     4
##                   4:          t3  limma       0     0     0     0
##      Plot summary

SessionInfo

sessionInfo()
## R version 4.4.0 (2024-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] autonomics_1.12.1 BiocStyle_2.32.0 
## 
## loaded via a namespace (and not attached):
##   [1] DBI_1.2.3                   gridExtra_2.3              
##   [3] readxl_1.4.3                rlang_1.1.4                
##   [5] magrittr_2.0.3              matrixStats_1.3.0          
##   [7] compiler_4.4.0              RSQLite_2.3.7              
##   [9] reshape2_1.4.4              vctrs_0.6.5                
##  [11] stringr_1.5.1               pkgconfig_2.0.3            
##  [13] crayon_1.5.2                fastmap_1.2.0              
##  [15] magick_2.8.3                dbplyr_2.5.0               
##  [17] XVector_0.44.0              labeling_0.4.3             
##  [19] utf8_1.2.4                  rmarkdown_2.27             
##  [21] tzdb_0.4.0                  UCSC.utils_1.0.0           
##  [23] tinytex_0.51                purrr_1.0.2                
##  [25] bit_4.0.5                   xfun_0.44                  
##  [27] MultiAssayExperiment_1.30.2 zlibbioc_1.50.0            
##  [29] cachem_1.1.0                GenomeInfoDb_1.40.1        
##  [31] jsonlite_1.8.8              fractional_0.1.3           
##  [33] blob_1.2.4                  highr_0.11                 
##  [35] DelayedArray_0.30.1         BiocParallel_1.38.0        
##  [37] tweenr_2.0.3                codingMatrices_0.4.0       
##  [39] parallel_4.4.0              R6_2.5.1                   
##  [41] bslib_0.7.0                 stringi_1.8.4              
##  [43] RColorBrewer_1.1-3          limma_3.60.2               
##  [45] GenomicRanges_1.56.0        jquerylib_0.1.4            
##  [47] cellranger_1.1.0            Rcpp_1.0.12                
##  [49] bookdown_0.39               SummarizedExperiment_1.34.0
##  [51] knitr_1.47                  R.utils_2.12.3             
##  [53] readr_2.1.5                 IRanges_2.38.0             
##  [55] Matrix_1.7-0                igraph_2.0.3               
##  [57] tidyselect_1.2.1            abind_1.4-5                
##  [59] yaml_2.3.8                  codetools_0.2-20           
##  [61] curl_5.2.1                  plyr_1.8.9                 
##  [63] lattice_0.22-6              tibble_3.2.1               
##  [65] rARPACK_0.11-0              Biobase_2.64.0             
##  [67] withr_3.0.0                 evaluate_0.23              
##  [69] polyclip_1.10-6             BiocFileCache_2.12.0       
##  [71] xml2_1.3.6                  pillar_1.9.0               
##  [73] BiocManager_1.30.23         filelock_1.0.3             
##  [75] MatrixGenerics_1.16.0       stats4_4.4.0               
##  [77] ellipse_0.5.0               generics_0.1.3             
##  [79] hms_1.1.3                   S4Vectors_0.42.0           
##  [81] ggplot2_3.5.1               munsell_0.5.1              
##  [83] scales_1.3.0                glue_1.7.0                 
##  [85] tools_4.4.0                 data.table_1.15.4          
##  [87] RSpectra_0.16-1             locfit_1.5-9.9             
##  [89] GEOquery_2.72.0             grid_4.4.0                 
##  [91] tidyr_1.3.1                 edgeR_4.2.0                
##  [93] colorspace_2.1-0            GenomeInfoDbData_1.2.12    
##  [95] ggforce_0.4.2               cli_3.6.2                  
##  [97] fansi_1.0.6                 mixOmics_6.28.0            
##  [99] S4Arrays_1.4.1              rematch_2.0.0              
## [101] dplyr_1.1.4                 corpcor_1.6.10             
## [103] pcaMethods_1.96.0           gtable_0.3.5               
## [105] R.methodsS3_1.8.2           sass_0.4.9                 
## [107] digest_0.6.35               BiocGenerics_0.50.0        
## [109] SparseArray_1.4.8           ggrepel_0.9.5              
## [111] farver_2.1.2                memoise_2.0.1              
## [113] htmltools_0.5.8.1           R.oo_1.26.0                
## [115] lifecycle_1.0.4             httr_1.4.7                 
## [117] statmod_1.5.0               bit64_4.0.5                
## [119] MASS_7.3-60.2

References

Appendix

A M Billing, S S Dib, A M Bhagwat, I T da Silva, R D Drummond, S Hayat, R Al-Mismar, H Ben-Hamidane, N Goswami, K Engholm-Keller, M R Larsen, K Suhre, A Rafii, J Graummann (2019). Mol Cell Proteomics. 18(10):1950-1966. doi: 10.1074/mcp.RA119.001356.

A M Billing, H Ben Hamidane, S S Dib, R J Cotton, A M Bhagwat, P Kumar, S Hayat, N A Yousri, N Goswami, K Suhre, A Rafii, J Graumann (2016). Comprehensive transcriptomics and proteomics characterization of human mesenchymal stem cells reveals source specific cellular markers. Sci Rep. 9;6:21507. doi: 10.1038/srep21507.

R Fukuda, R Marin-Juez, H El-Sammak, A Beisaw, R Ramadass, C Kuenne, S Guenther, A Konzer, A M Bhagwat, J Graumann, D YR Stainier (2020). EMBO Rep. 21(8): e49752. doi: 10.15252/embr.201949752

A Halama, M Kulinski, S Dib, S B Zaghlool, K S Siveen, A Iskandarani, J Zierer 3, K S Prabhu, N J Satheesh, A M Bhagwat, S Uddin, G Kastenmüller, O Elemento, S S Gross, K Suhre (2018). Accelerated lipid catabolism and autophagy are cancer survival mechanisms under inhibited glutaminolysis. Cancer Lett., 430:133-147. doi:10.1016/j.canlet.2018.05.017

A Halama, H Kahal, A M Bhagwat, J Zierer, T Sathyapalan, J Graumann, K Suhre, S L Atkin (2018). Metabolic and proteomics signatures of hypoglycaemia in type 2 diabetes. Diabetes, obesity and metabolism, https://doi.org/10.1111/dom.13602