3 Methods
The methods detailed below correspond to the analyses selected in the article. Some sections in this report correspond to additional analyses. Refer to the source code for details.
The analyses were produced using Snakemake (https://doi.org/10.12688/f1000research.29032.1) as workflow manager and R Bookdown for reporting. The source code for all the analyses is available at https://github.com/guillaumecharbonnier/mw-ibrahim2022 and a compiled version of the Bookdown report is available at https://guillaumecharbonnier.github.io/mw-ibrahim2022.
3.1 Individual reprocessing of published experiments
3.1.1 Microarrays
Normalized microarrays from seven studies (GSE84185, GSE43261, GSE56028, GSE118669, GSE54307, GSE6476 and GSE42940) were retrieved from GEO using GEOquery (https://doi.org/10.1093/bioinformatics/btm254). One last microarray was retrieved directly from original authors (Surget et al, 2009) and was normalized using RMA from the oligo R package (https://dx.doi.org/10.1093%2Fbioinformatics%2Fbtq431). Microarrays latest annotations were retrieved from Ensembl Biomart on 2021-10-01 for Affymetrix Mouse430_2, Affymetrix Mouse 430A_2, Affymetrix RaGene 1_0_st_v1 and Agilent Sureprint G3_GE_8x60k. When unavailable, annotations from original authors were completed using megablast queries (GSE42940). Probes with mapping to multiple genes were discarded. Differential gene expression analyses were produced with Limma (https://doi.org/10.1093/nar/gkv007).
3.1.2 RNA-Seq
Raw reads from four studies (SRP056481, SRP057486, SRP084288 and SRP131063) were retrieved from SRA using sra-tools. Reads were trimmed using sickle (-q 20) (https://github.com/najoshi/sickle#citation) and aligned on GRCm38 (for mouse samples) or Rnor6 (for rat samples) with STAR (https://doi.org/10.1093/bioinformatics/bts635) using default settings. Gene-level counts were produced using subread featureCounts (https://doi.org/10.1093/bioinformatics/btt656) with Ensembl release 102 annotations. Differential gene expression analyses were produced with Deseq2 (https://doi.org/10.1186/s13059-014-0550-8) using apeglm shrinkage (10.1093/bioinformatics/bty895).
3.2 Integration plots
To compare differential expression analyses between different experiments, we kept only genes with pvalue below 0.05 and in the top 300 of any compared designs. Plots were produced using R ggplot2 package (https://doi.org/10.1007/978-0-387-98141-3).
Gene.stable.ID and Gene.name columns come from Biomart annotations while other gene annotations are from the original authors. As such, incomplete concordance is observed.
3.3 R session environment
## R version 4.0.5 (2021-03-31)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS/LAPACK: /media/gcharbonnier/samsung_m3/miniconda/envs/r_rmd_antares/lib/libopenblasp-r0.3.15.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_DK.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 grid parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] biomaRt_2.46.3 XML_3.99-0.6
## [3] ensembldb_2.14.1 AnnotationFilter_1.14.0
## [5] GenomicFeatures_1.42.3 maptools_1.1-1
## [7] sp_1.4-5 knitr_1.33
## [9] R.utils_2.10.1 R.oo_1.24.0
## [11] R.methodsS3_1.8.1 DT_0.18
## [13] plyr_1.8.6 umap_0.2.7.0
## [15] factoextra_1.0.7 ReactomePA_1.34.0
## [17] enrichplot_1.10.2 clusterProfiler_3.18.1
## [19] readxl_1.3.1 openxlsx_4.2.3
## [21] gprofiler2_0.2.0 CEMiTool_1.14.1
## [23] tximeta_1.8.5 fishpond_1.6.0
## [25] EnhancedVolcano_1.8.0 apeglm_1.12.0
## [27] DESeq2_1.30.1 SummarizedExperiment_1.20.0
## [29] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [31] edgeR_3.32.1 limma_3.46.0
## [33] oligo_1.54.1 Biostrings_2.58.0
## [35] XVector_0.30.0 oligoClasses_1.52.0
## [37] org.Hs.eg.db_3.12.0 ragene10sttranscriptcluster.db_8.7.0
## [39] org.Rn.eg.db_3.12.0 mouse4302.db_3.2.3
## [41] org.Mm.eg.db_3.12.0 AnnotationDbi_1.52.0
## [43] GEOquery_2.58.0 RColorBrewer_1.1-2
## [45] GenomicRanges_1.42.0 GenomeInfoDb_1.26.4
## [47] IRanges_2.24.1 S4Vectors_0.28.1
## [49] styler_1.4.1 httr_1.4.2
## [51] dendsort_0.3.4 dendextend_1.15.1
## [53] circlize_0.4.12 ComplexHeatmap_2.6.2
## [55] NMF_0.21.0 Biobase_2.50.0
## [57] BiocGenerics_0.36.0 cluster_2.1.2
## [59] rngtools_1.5 pkgmaker_0.32.2
## [61] registry_0.5-1 ggh4x_0.1.2.1
## [63] ggcorrplot_0.1.3 ggrepel_0.9.1
## [65] ggbeeswarm_0.6.0 ggpubr_0.4.0
## [67] ggplot2_3.3.3 yaml_2.2.1
## [69] pbapply_1.5-0 data.table_1.14.0
##
## loaded via a namespace (and not attached):
## [1] statnet.common_4.4.1 Hmisc_4.5-0
## [3] Rsamtools_2.6.0 foreach_1.5.1
## [5] crayon_1.4.1 MASS_7.3-54
## [7] backports_1.2.1 impute_1.64.0
## [9] GOSemSim_2.16.1 rlang_0.4.11
## [11] intergraph_2.0-2 extrafontdb_1.0
## [13] extrafont_0.17 BiocParallel_1.24.1
## [15] rjson_0.2.20 bit64_4.0.5
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## [23] tidyr_1.1.3 proj4_1.0-10.1
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## [33] fastmatch_1.1-0 maps_3.3.0
## [35] shiny_1.6.0 xfun_0.23
## [37] askpass_1.1 clue_0.3-59
## [39] tidygraph_1.2.0 tibble_3.1.2
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## [43] png_0.1-7 withr_2.4.2
## [45] rle_0.9.2 bitops_1.0-7
## [47] ggforce_0.3.3 cellranger_1.1.0
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## [51] pillar_1.6.1 GlobalOptions_0.1.2
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## [69] httpuv_1.6.1 rtracklayer_1.50.0
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## [75] utf8_1.2.1 later_1.2.0
## [77] dplyr_1.0.6 BiocFileCache_1.14.0
## [79] jsonlite_1.7.2 scales_1.1.1
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## [103] graphlayouts_0.7.1 viridisLite_0.4.0
## [105] digest_0.6.27 assertthat_0.2.1
## [107] mime_0.10 rappdirs_0.3.3
## [109] Rttf2pt1_1.3.8 emdbook_1.3.12
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## [115] Formula_1.2-4 Cairo_1.5-12.2
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## [143] DO.db_2.9 Matrix_1.3-3
## [145] qvalue_2.22.0 iterators_1.0.13
## [147] stringr_1.4.0 htmlwidgets_1.5.3
## [149] polyclip_1.10-0 purrr_0.3.4
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