References

Ahrens, M., Ammerpohl, O., Schönfels, W. von, Kolarova, J., Bens, S., Itzel, T., … Hampe, J. (2013). DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metabolism, 18(2), 296–302. http://doi.org/10.1016/j.cmet.2013.07.004

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., … Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086. http://doi.org/10.1038/nmeth.4463

Altelaar, A. F. M., Munoz, J., & Heck, A. J. R. (2013). Next-generation proteomics: Towards an integrative view of proteome dynamics. Nature Reviews. Genetics, 14(1), 35–48. http://doi.org/10.1038/nrg3356

Alvarez, M. J., Shen, Y., Giorgi, F. M., Lachmann, A., Ding, B. B., Ye, B. H., & Califano, A. (2016). Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature Genetics, 48(8), 838–847. http://doi.org/10.1038/ng.3593

Alvarez, M. J., Subramaniam, P. S., Tang, L. H., Grunn, A., Aburi, M., Rieckhof, G., … Califano, A. (2018). A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors. Nature Genetics, 50(7), 979–989. http://doi.org/10.1038/s41588-018-0138-4

Amarasinghe, S. L., Su, S., Dong, X., Zappia, L., Ritchie, M. E., & Gouil, Q. (2020). Opportunities and challenges in long-read sequencing data analysis. Genome Biology, 21(1), 30. http://doi.org/10.1186/s13059-020-1935-5

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., … Sherlock, G. (2000). Gene ontology: Tool for the unification of biology. Nature Genetics, 25(1), 25–29. http://doi.org/10.1038/75556

Aytes, A., Mitrofanova, A., Lefebvre, C., Alvarez, M. J., Castillo-Martin, M., Zheng, T., … Abate-Shen, C. (2014). Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell, 25(5), 638–651. http://doi.org/10.1016/j.ccr.2014.03.017

Badia-i-Mompel, P., Santiago, J. V., Braunger, J., Geiss, C., Dimitrov, D., Müller-Dott, S., … Saez-Rodriguez, J. (2022). decoupleR: Ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. http://doi.org/10.1093/bioadv/vbac016

Banerjee, S., Biehl, A., Gadina, M., Hasni, S., & Schwartz, D. M. (2017). JAK-STAT signaling as a target for inflammatory and autoimmune diseases: Current and future prospects. Drugs, 77(5), 521–546. http://doi.org/10.1007/s40265-017-0701-9

Baran-Gale, J., Chandra, T., & Kirschner, K. (2018). Experimental design for single-cell RNA sequencing. Briefings in Functional Genomics, 17(4), 233–239. http://doi.org/10.1093/bfgp/elx035

Barbie, D. A., Tamayo, P., Boehm, J. S., Kim, S. Y., Moody, S. E., Dunn, I. F., … Hahn, W. C. (2009). Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature, 462(7269), 108–112. http://doi.org/10.1038/nature08460

Barbosa, S., Niebel, B., Wolf, S., Mauch, K., & Takors, R. (2018). A guide to gene regulatory network inference for obtaining predictive solutions: Underlying assumptions and fundamental biological and data constraints. Bio Systems, 174, 37–48. http://doi.org/10.1016/j.biosystems.2018.10.008

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 57, 289–300.

Blischak, J. D., Carbonetto, P., & Stephens, M. (2019). Creating and sharing reproducible research code the workflowr way. F1000Research, 8, 1749. http://doi.org/10.12688/f1000research.20843.1

Blouin, A., Bolender, R. P., & Weibel, E. R. (1977). Distribution of organelles and membranes between hepatocytes and nonhepatocytes in the rat liver parenchyma. A stereological study. The Journal of Cell Biology, 72(2), 441–455. http://doi.org/10.1083/jcb.72.2.441

Boyer, J. L. (2013). Bile formation and secretion. Comprehensive Physiology, 3(3), 1035–1078. http://doi.org/10.1002/cphy.c120027

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68(6), 394–424. http://doi.org/10.3322/caac.21492

Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016). Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34(5), 525–527. http://doi.org/10.1038/nbt.3519

Brubaker, D. K., Proctor, E. A., Haigis, K. M., & Lauffenburger, D. A. (2019). Computational translation of genomic responses from experimental model systems to humans. PLoS Computational Biology, 15(1), e1006286. http://doi.org/10.1371/journal.pcbi.1006286

Burd, A. L., Ingraham, R. H., Goldrick, S. E., Kroe, R. R., Crute, J. J., & Grygon, C. A. (2004). Assembly of major histocompatibility complex (MHC) class II transcription factors: Association and promoter recognition of RFX proteins. Biochemistry, 43(40), 12750–12760. http://doi.org/10.1021/bi030262o

Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018). Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature Biotechnology, 36(5), 411–420. http://doi.org/10.1038/nbt.4096

Campos, G., Schmidt-Heck, W., De Smedt, J., Widera, A., Ghallab, A., Pütter, L., … Godoy, P. (2020). Inflammation-associated suppression of metabolic gene networks in acute and chronic liver disease. Archives of Toxicology, 94(1), 205–217. http://doi.org/10.1007/s00204-019-02630-3

Cantini, L., Calzone, L., Martignetti, L., Rydenfelt, M., Blüthgen, N., Barillot, E., & Zinovyev, A. (2018). Classification of gene signatures for their information value and functional redundancy. NPJ Systems Biology and Applications, 4, 2. http://doi.org/10.1038/s41540-017-0038-8

Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., … Shendure, J. (2020). A human cell atlas of fetal gene expression. Science, 370(6518). http://doi.org/10.1126/science.aba7721

Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., … Shendure, J. (2017). Comprehensive single-cell transcriptional profiling of a multicellular organism. Science, 357(6352), 661–667. http://doi.org/10.1126/science.aam8940

Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., … Consortium, G. (2015). A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreservation and Biobanking, 13(5), 311–319. http://doi.org/10.1089/bio.2015.0032

Carvalho, B. S., & Irizarry, R. A. (2010). A framework for oligonucleotide microarray preprocessing. Bioinformatics, 26(19), 2363–2367. http://doi.org/10.1093/bioinformatics/btq431

Chen, J., Gao, G., Wang, H., Ye, X., Zhou, J., & Lin, J. (2019). Expression and clinical significance of latent-transforming growth factor beta-binding protein 2 in primary hepatocellular carcinoma. Medicine, 98(39), e17216. http://doi.org/10.1097/{MD}.0000000000017216

Consortium, G. (2013). The genotype-tissue expression (GTEx) project. Nature Genetics, 45(6), 580–585. http://doi.org/10.1038/ng.2653

Costache, M. I., Ioana, M., Iordache, S., Ene, D., Costache, C. A., & Săftoiu, A. (2015). VEGF expression in pancreatic cancer and other malignancies: A review of the literature. Romanian Journal of Internal Medicine = Revue Roumaine de Medecine Interne, 53(3), 199–208. http://doi.org/10.1515/rjim-2015-0027

Crick, F. (1970). Central dogma of molecular biology. Nature, 227(5258), 561–563. http://doi.org/10.1038/227561a0

Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and ROC curves. In Proceedings of the 23rd international conference on machine learning - ICML ’06 (pp. 233–240). New York, New York, USA: ACM Press. http://doi.org/10.1145/1143844.1143874

Davis, S., & Meltzer, P. S. (2007). GEOquery: A bridge between the gene expression omnibus (GEO) and BioConductor. Bioinformatics, 23(14), 1846–1847. http://doi.org/10.1093/bioinformatics/btm254

Ding, H., Douglass, E. F., Sonabend, A. M., Mela, A., Bose, S., Gonzalez, C., … Califano, A. (2018). Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. Nature Communications, 9(1), 1471. http://doi.org/10.1038/s41467-018-03843-3

Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., … Regev, A. (2016). Perturb-seq: Dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell, 167(7), 1853–1866.e17. http://doi.org/10.1016/j.cell.2016.11.038

Dobie, R., Wilson-Kanamori, J. R., Henderson, B. E. P., Smith, J. R., Matchett, K. P., Portman, J. R., … Henderson, N. C. (2019). Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis. Cell Reports, 29(7), 1832–1847.e8. http://doi.org/10.1016/j.celrep.2019.10.024

Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., … Gingeras, T. R. (2013). STAR: Ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21. http://doi.org/10.1093/bioinformatics/bts635

Draghici, S., Khatri, P., Tarca, A. L., Amin, K., Done, A., Voichita, C., … Romero, R. (2007). A systems biology approach for pathway level analysis. Genome Research, 17(10), 1537–1545. http://doi.org/10.1101/gr.6202607

Dugourd, A., & Saez-Rodriguez, J. (2019). Footprint-based functional analysis of multiomic data. Current Opinion in Systems Biology, 15, 82–90. http://doi.org/10.1016/j.coisb.2019.04.002

Durinck, S., Spellman, P. T., Birney, E., & Huber, W. (2009). Mapping identifiers for the integration of genomic datasets with the r/bioconductor package biomaRt. Nature Protocols, 4(8), 1184–1191. http://doi.org/10.1038/nprot.2009.97

Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1), 207–210. http://doi.org/10.1093/nar/30.1.207

Ernst, J., & Bar-Joseph, Z. (2006). STEM: A tool for the analysis of short time series gene expression data. BMC Bioinformatics, 7, 191. http://doi.org/10.1186/1471-2105-7-191

Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21 Suppl 1, i159–68. http://doi.org/10.1093/bioinformatics/bti1022

Essaghir, A., Toffalini, F., Knoops, L., Kallin, A., Helden, J. van, & Demoulin, J.-B. (2010). Transcription factor regulation can be accurately predicted from the presence of target gene signatures in microarray gene expression data. Nucleic Acids Research, 38(11), e120. http://doi.org/10.1093/nar/gkq149

Feng, X., Wang, H., Takata, H., Day, T. J., Willen, J., & Hu, H. (2011). Transcription factor Foxp1 exerts essential cell-intrinsic regulation of the quiescence of naive t cells. Nature Immunology, 12(6), 544–550. http://doi.org/10.1038/ni.2034

Fisher, R. A. (1992). Statistical methods for research workers. In S. Kotz & N. L. Johnson (Eds.), Breakthroughs in statistics (pp. 66–70). New York: Springer. http://doi.org/10.1007/978-1-4612-4380-9\_6

Foroutan, M., Bhuva, D. D., Lyu, R., Horan, K., Cursons, J., & Davis, M. J. (2018). Single sample scoring of molecular phenotypes. BMC Bioinformatics, 19(1), 404. http://doi.org/10.1186/s12859-018-2435-4

Fox, J. G., Barthold, S., Newcomer, C. E., Smith, A., & Quimby, F. W. (2006). The mouse in biomedical research (2nd ed., p. 2192). Amsterdam: Academic Pr.

Fruman, D. A., & Rommel, C. (2014). PI3K and cancer: Lessons, challenges and opportunities. Nature Reviews. Drug Discovery, 13(2), 140–156. http://doi.org/10.1038/nrd4204

Fry, E. A., & Inoue, K. (2018). Aberrant expression of ETS1 and ETS2 proteins in cancer. Cancer Reports and Reviews, 2(3). http://doi.org/10.15761/{CRR}.1000151

Garcia-Alonso, L., Holland, C. H., Ibrahim, M. M., Turei, D., & Saez-Rodriguez, J. (2019). Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research, 29(8), 1363–1375. http://doi.org/10.1101/gr.240663.118

Garcia-Alonso, L., Iorio, F., Matchan, A., Fonseca, N., Jaaks, P., Peat, G., … Saez-Rodriguez, J. (2018). Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Research, 78(3), 769–780. http://doi.org/10.1158/0008-5472.{CAN}-17-1679

Genga, R. M. J., Kernfeld, E. M., Parsi, K. M., Parsons, T. J., Ziller, M. J., & Maehr, R. (2019). Single-cell RNA-sequencing-based CRISPRi screening resolves molecular drivers of early human endoderm development. Cell Reports, 27(3), 708–718.e10. http://doi.org/10.1016/j.celrep.2019.03.076

Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., … Zhang, J. (2004). Bioconductor: Open software development for computational biology and bioinformatics. Genome Biology, 5(10), R80. http://doi.org/10.1186/gb-2004-5-10-r80

Ghallab, A., Hofmann, U., Sezgin, S., Vartak, N., Hassan, R., Zaza, A., … Reif, R. (2019). Bile microinfarcts in cholestasis are initiated by rupture of the apical hepatocyte membrane and cause shunting of bile to sinusoidal blood. Hepatology, 69(2), 666–683. http://doi.org/10.1002/hep.30213

Ghallab, A., Myllys, M., Holland, C. H., Zaza, A., Murad, W., Hassan, R., … Hengstler, J. G. (2019). Influence of liver fibrosis on lobular zonation. Cells, 8(12). http://doi.org/10.3390/cells8121556

Godoy, P., Widera, A., Schmidt-Heck, W., Campos, G., Meyer, C., Cadenas, C., … Hengstler, J. G. (2016). Gene network activity in cultivated primary hepatocytes is highly similar to diseased mammalian liver tissue. Archives of Toxicology, 90(10), 2513–2529. http://doi.org/10.1007/s00204-016-1761-4

Grau, J., Grosse, I., & Keilwagen, J. (2015). PRROC: Computing and visualizing precision-recall and receiver operating characteristic curves in r. Bioinformatics, 31(15), 2595–2597. http://doi.org/10.1093/bioinformatics/btv153

Greenbaum, D., Colangelo, C., Williams, K., & Gerstein, M. (2003). Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biology, 4(9), 117. http://doi.org/10.1186/gb-2003-4-9-117

Han, H., Cho, J.-W., Lee, S., Yun, A., Kim, H., Bae, D., … Lee, I. (2018). TRRUST v2: An expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research, 46(D1), D380–D386. http://doi.org/10.1093/nar/gkx1013

Hänzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7. http://doi.org/10.1186/1471-2105-14-7

Hegde, M., Strand, C., Hanna, R. E., & Doench, J. G. (2018). Uncoupling of sgRNAs from their associated barcodes during PCR amplification of combinatorial CRISPR screens. Plos One, 13(5), e0197547. http://doi.org/10.1371/journal.pone.0197547

Henderson, N. C., Rieder, F., & Wynn, T. A. (2020). Fibrosis: From mechanisms to medicines. Nature, 587(7835), 555–566. http://doi.org/10.1038/s41586-020-2938-9

Hernandez-Armenta, C., Ochoa, D., Gonçalves, E., Saez-Rodriguez, J., & Beltrao, P. (2017). Benchmarking substrate-based kinase activity inference using phosphoproteomic data. Bioinformatics, 33(12), 1845–1851. http://doi.org/10.1093/bioinformatics/btx082

Hernansaiz-Ballesteros, R., Holland, C. H., Dugourd, A., & Saez-Rodriguez, J. (2022). FUNKI: Interactive functional footprint-based analysis of omics data. Bioinformatics. http://doi.org/10.1093/bioinformatics/btac055

Hidalgo, M. R., Cubuk, C., Amadoz, A., Salavert, F., Carbonell-Caballero, J., & Dopazo, J. (2017). High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. Oncotarget, 8(3), 5160–5178. http://doi.org/10.18632/oncotarget.14107

Hoang, S. A., Oseini, A., Feaver, R. E., Cole, B. K., Asgharpour, A., Vincent, R., … Sanyal, A. J. (2019). Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease. Scientific Reports, 9(1), 12541. http://doi.org/10.1038/s41598-019-48746-5

Hoheisel, J. D. (2006). Microarray technology: Beyond transcript profiling and genotype analysis. Nature Reviews. Genetics, 7(3), 200–210. http://doi.org/10.1038/nrg1809

Holland, Christian H., Ramirez Flores, R. O., Myllys, M., Hassan, R., Edlund, K., Hofmann, U., … Ghallab, A. (2021). Transcriptomic cross‐species analysis of chronic liver disease reveals consistent regulation between humans and mice. Hepatology Communications. http://doi.org/10.1002/hep4.1797

Holland, Christian H., Szalai, B., & Saez-Rodriguez, J. (2020). Transfer of regulatory knowledge from human to mouse for functional genomics analysis. Biochimica Et Biophysica Acta. Gene Regulatory Mechanisms, 1863(6), 194431. http://doi.org/10.1016/j.bbagrm.2019.194431

Holland, Christian H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., … Saez-Rodriguez, J. (2020). Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biology, 21(1), 36. http://doi.org/10.1186/s13059-020-1949-z

Horvath, S., Erhart, W., Brosch, M., Ammerpohl, O., Schönfels, W. von, Ahrens, M., … Hampe, J. (2014). Obesity accelerates epigenetic aging of human liver. Proceedings of the National Academy of Sciences of the United States of America, 111(43), 15538–15543. http://doi.org/10.1073/pnas.1412759111

Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1), 44–57. http://doi.org/10.1038/nprot.2008.211

Hung, J.-H., Yang, T.-H., Hu, Z., Weng, Z., & DeLisi, C. (2012). Gene set enrichment analysis: Performance evaluation and usage guidelines. Briefings in Bioinformatics, 13(3), 281–291. http://doi.org/10.1093/bib/bbr049

Ideker, T., Thorsson, V., Ranish, J. A., Christmas, R., Buhler, J., Eng, J. K., … Hood, L. (2001). Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science, 292(5518), 929–934. http://doi.org/10.1126/science.292.5518.929

J, S., B, T., L, C., & Parkinson, H. (2015). A new ontology lookup service at EMBL-EBI. In SWAT4LS.

Jansen, P. L. M., Ghallab, A., Vartak, N., Reif, R., Schaap, F. G., Hampe, J., & Hengstler, J. G. (2017). The ascending pathophysiology of cholestatic liver disease. Hepatology, 65(2), 722–738. http://doi.org/10.1002/hep.28965

Jassal, B., Matthews, L., Viteri, G., Gong, C., Lorente, P., Fabregat, A., … D’Eustachio, P. (2020). The reactome pathway knowledgebase. Nucleic Acids Research, 48(D1), D498–D503. http://doi.org/10.1093/nar/gkz1031

Johnson, D. G. (2000). The paradox of E2F1: Oncogene and tumor suppressor gene. Molecular Carcinogenesis, 27(3), 151–157. http://doi.org/10.1002/({SICI})1098-2744(200003)27:3\textless151::{AID}-{MC1\textgreater3}.0.{CO};2-C

Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30. http://doi.org/10.1093/nar/28.1.27

Kauffmann, A., Rayner, T. F., Parkinson, H., Kapushesky, M., Lukk, M., Brazma, A., & Huber, W. (2009). Importing ArrayExpress datasets into r/bioconductor. Bioinformatics, 25(16), 2092–2094. http://doi.org/10.1093/bioinformatics/btp354

Keenan, A. B., Torre, D., Lachmann, A., Leong, A. K., Wojciechowicz, M. L., Utti, V., … Ma’ayan, A. (2019). ChEA3: Transcription factor enrichment analysis by orthogonal omics integration. Nucleic Acids Research, 47(W1), W212–W224. http://doi.org/10.1093/nar/gkz446

Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014). Bayesian approach to single-cell differential expression analysis. Nature Methods, 11(7), 740–742. http://doi.org/10.1038/nmeth.2967

Khatri, P., Sirota, M., & Butte, A. J. (2012). Ten years of pathway analysis: Current approaches and outstanding challenges. PLoS Computational Biology, 8(2), e1002375. http://doi.org/10.1371/journal.pcbi.1002375

Kim, A., Wu, X., Allende, D. S., & Nagy, L. E. (2021). Gene deconvolution reveals aberrant liver regeneration and immune cell infiltration in alcohol-associated hepatitis. Hepatology. http://doi.org/10.1002/hep.31759

Koussounadis, A., Langdon, S. P., Um, I. H., Harrison, D. J., & Smith, V. A. (2015). Relationship between differentially expressed mRNA and mRNA-protein correlations in a xenograft model system. Scientific Reports, 5, 10775. http://doi.org/10.1038/srep10775

Krämer, A., Green, J., Pollard, J., & Tugendreich, S. (2014). Causal analysis approaches in ingenuity pathway analysis. Bioinformatics, 30(4), 523–530. http://doi.org/10.1093/bioinformatics/btt703

Krenkel, O., Hundertmark, J., Ritz, T. P., Weiskirchen, R., & Tacke, F. (2019). Single cell RNA sequencing identifies subsets of hepatic stellate cells and myofibroblasts in liver fibrosis. Cells, 8(5). http://doi.org/10.3390/cells8050503

Kuppe, C., Ramirez Flores, R. O., Li, Z., Hannani, M. T., Tanevski, J., Halder, M., … Kramann, R. (2020). Spatial multi-omic map of human myocardial infarction. BioRxiv. http://doi.org/10.1101/2020.12.08.411686

Kwon, A. T., Arenillas, D. J., Worsley Hunt, R., & Wasserman, W. W. (2012). oPO-3: Advanced analysis of regulatory motif over-representation across genes or ChIP-seq datasets. G3 (Bethesda, Md.), 2(9), 987–1002. http://doi.org/10.1534/g3.112.003202

Lachmann, A., Torre, D., Keenan, A. B., Jagodnik, K. M., Lee, H. J., Wang, L., … Ma’ayan, A. (2018). Massive mining of publicly available RNA-seq data from human and mouse. Nature Communications, 9(1), 1366. http://doi.org/10.1038/s41467-018-03751-6

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., … Golub, T. R. (2006). The connectivity map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935. http://doi.org/10.1126/science.1132939

Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., … Consortium, I. H. G. S. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860–921. http://doi.org/10.1038/35057062

Lee, E., Chuang, H.-Y., Kim, J.-W., Ideker, T., & Lee, D. (2008). Inferring pathway activity toward precise disease classification. PLoS Computational Biology, 4(11), e1000217. http://doi.org/10.1371/journal.pcbi.1000217

Leist, M., & Hartung, T. (2013). Inflammatory findings on species extrapolations: Humans are definitely no 70-kg mice. Archives of Toxicology, 87(4), 563–567. http://doi.org/10.1007/s00204-013-1038-0

Li, L., & Clevers, H. (2010). Coexistence of quiescent and active adult stem cells in mammals. Science, 327(5965), 542–545. http://doi.org/10.1126/science.1180794

Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011). Molecular signatures database (MSigDB) 3.0. Bioinformatics, 27(12), 1739–1740. http://doi.org/10.1093/bioinformatics/btr260

Liu, A., Trairatphisan, P., Gjerga, E., Didangelos, A., Barratt, J., & Saez-Rodriguez, J. (2019). From expression footprints to causal pathways: Contextualizing large signaling networks with CARNIVAL. NPJ Systems Biology and Applications, 5, 40. http://doi.org/10.1038/s41540-019-0118-z

Liu, T., Zhang, L., Joo, D., & Sun, S.-C. (2017). NF- signaling in inflammation. Signal Transduction and Targeted Therapy, 2. http://doi.org/10.1038/sigtrans.2017.23

Llovet, J. M., Kelley, R. K., Villanueva, A., Singal, A. G., Pikarsky, E., Roayaie, S., … Finn, R. S. (2021). Hepatocellular carcinoma. Nature Reviews. Disease Primers, 7(1), 6. http://doi.org/10.1038/s41572-020-00240-3

Lopez-Dominguez, R., Toro-Dominguez, D., Martorell-Marugan, J., Garcia-Moreno, A., Holland, C. H., Saez-Rodriguez, J., … Carmona-Saez, P. (2021). Transcription factor activity inference in systemic lupus erythematosus. Life (Chicago, Ill. : 1978), 11(4), 299. http://doi.org/10.3390/life11040299

Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016). A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Research, 5, 2122. http://doi.org/10.12688/f1000research.9501.2

Mahi, N. A., Najafabadi, M. F., Pilarczyk, M., Kouril, M., & Medvedovic, M. (2019). GREIN: An interactive web platform for re-analyzing GEO RNA-seq data. Scientific Reports, 9(1), 7580. http://doi.org/10.1038/s41598-019-43935-8

Mann, M., & Jensen, O. N. (2003). Proteomic analysis of post-translational modifications. Nature Biotechnology, 21(3), 255–261. http://doi.org/10.1038/nbt0303-255

Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., & Califano, A. (2006). ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7 Suppl 1, S7. http://doi.org/10.1186/1471-2105-7-S1-S7

Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., … Heyn, H. (2020). Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nature Biotechnology, 38(6), 747–755. http://doi.org/10.1038/s41587-020-0469-4

Method of the year 2013. (2014). Nature Methods, 11(1), 1. http://doi.org/10.1038/nmeth.2801

Method of the year 2020: Spatially resolved transcriptomics. (n.d.). Nature Methods, 18(1), 1. http://doi.org/10.1038/s41592-020-01042-x

Mi, H., Muruganujan, A., Ebert, D., Huang, X., & Thomas, P. D. (2019). PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Research, 47(D1), D419–D426. http://doi.org/10.1093/nar/gky1038

Michel, K., Roth, S., Trautwein, C., Gong, W., Flemming, P., & Gressner, A. M. (1998). Analysis of the expression pattern of the latent transforming growth factor beta binding protein isoforms in normal and diseased human liver reveals a new splice variant missing the proteinase-sensitive hinge region. Hepatology, 27(6), 1592–1599. http://doi.org/10.1002/hep.510270619

Mitchell, C., & Willenbring, H. (2008). A reproducible and well-tolerated method for 2/3 partial hepatectomy in mice. Nature Protocols, 3(7), 1167–1170. http://doi.org/10.1038/nprot.2008.80

Mohs, A., Otto, T., Schneider, K. M., Peltzer, M., Boekschoten, M., Holland, C. H., … Trautwein, C. (2020). Hepatocyte-specific NRF2 activation controls fibrogenesis and carcinogenesis in steatohepatitis. Journal of Hepatology. http://doi.org/10.1016/j.jhep.2020.09.037

Moylan, C. A., Pang, H., Dellinger, A., Suzuki, A., Garrett, M. E., Guy, C. D., … Diehl, A. M. (2014). Hepatic gene expression profiles differentiate presymptomatic patients with mild versus severe nonalcoholic fatty liver disease. Hepatology, 59(2), 471–482. http://doi.org/10.1002/hep.26661

Network, C. G. A. R., Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., … Stuart, J. M. (2013). The cancer genome atlas pan-cancer analysis project. Nature Genetics, 45(10), 1113–1120. http://doi.org/10.1038/ng.2764

Nguyen, T.-M., Shafi, A., Nguyen, T., & Draghici, S. (2019). Identifying significantly impacted pathways: A comprehensive review and assessment. Genome Biology, 20(1), 203. http://doi.org/10.1186/s13059-019-1790-4

Normand, R., Du, W., Briller, M., Gaujoux, R., Starosvetsky, E., Ziv-Kenet, A., … Shen-Orr, S. S. (2018). Found in translation: A machine learning model for mouse-to-human inference. Nature Methods, 15(12), 1067–1073. http://doi.org/10.1038/s41592-018-0214-9

Pang, H., Lin, A., Holford, M., Enerson, B. E., Lu, B., Lawton, M. P., … Zhao, H. (2006). Pathway analysis using random forests classification and regression. Bioinformatics, 22(16), 2028–2036. http://doi.org/10.1093/bioinformatics/btl344

Parikh, J. R., Klinger, B., Xia, Y., Marto, J. A., & Blüthgen, N. (2010). Discovering causal signaling pathways through gene-expression patterns. Nucleic Acids Research, 38(Web Server issue), W109–17. http://doi.org/10.1093/nar/gkq424

Patti, G. J., Yanes, O., & Siuzdak, G. (2012). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews. Molecular Cell Biology, 13(4), 263–269. http://doi.org/10.1038/nrm3314

Pellicoro, A., Ramachandran, P., Iredale, J. P., & Fallowfield, J. A. (2014). Liver fibrosis and repair: Immune regulation of wound healing in a solid organ. Nature Reviews. Immunology, 14(3), 181–194. http://doi.org/10.1038/nri3623

Peng, T., Zhu, Q., Yin, P., & Tan, K. (2019). SCRABBLE: Single-cell RNA-seq imputation constrained by bulk RNA-seq data. Genome Biology, 20(1), 88. http://doi.org/10.1186/s13059-019-1681-8

Phipson, B., Lee, S., Majewski, I. J., Alexander, W. S., & Smyth, G. K. (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. The Annals of Applied Statistics, 10(2), 946–963. http://doi.org/10.1214/16-{AOAS920}

Puente-Santamaria, L., Wasserman, W. W., & Del Peso, L. (2019). TFEA.ChIP: A tool kit for transcription factor binding site enrichment analysis capitalizing on ChIP-seq datasets. Bioinformatics, 35(24), 5339–5340. http://doi.org/10.1093/bioinformatics/btz573

Qiu, P. (2020). Embracing the dropouts in single-cell RNA-seq analysis. Nature Communications, 11(1), 1169. http://doi.org/10.1038/s41467-020-14976-9

Quiñonez-Flores, C. M., González-Chávez, S. A., & Pacheco-Tena, C. (2016). Hypoxia and its implications in rheumatoid arthritis. Journal of Biomedical Science, 23(1), 62. http://doi.org/10.1186/s12929-016-0281-0

R Core Team, R. C. T. (2020). R: A language and environment for statistical computing. {SOFTWARE}.{COMPUTER\_SOFTWARE}, R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

Ramachandran, P., Dobie, R., Wilson-Kanamori, J. R., Dora, E. F., Henderson, B. E. P., Luu, N. T., … Henderson, N. C. (2019). Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature, 575(7783), 512–518. http://doi.org/10.1038/s41586-019-1631-3

Ramirez Flores, R. O., Lanzer, J. D., Holland, C. H., Leuschner, F., Most, P., Schultz, J., … Saez‐Rodriguez, J. (2021). Consensus transcriptional landscape of human end‐stage heart failure. JOURNAL OF ANCIENT HISTORY AND ARCHAEOLOGY. http://doi.org/10.1161/{JAHA}.120.019667

Ramnath, D., Irvine, K. M., Lukowski, S. W., Horsfall, L. U., Loh, Z., Clouston, A. D., … Sweet, M. J. (2018). Hepatic expression profiling identifies steatosis-independent and steatosis-driven advanced fibrosis genes. JCI Insight. Retrieved from https://insight.jci.org/articles/view/120274

Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., … Participants, H. C. A. M. (2017). The human cell atlas. eLife, 6. http://doi.org/10.7554/{eLife}.27041

Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research, 43(7), e47. http://doi.org/10.1093/nar/gkv007

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011). pROC: An open-source package for r and s+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77. http://doi.org/10.1186/1471-2105-12-77

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. http://doi.org/10.1093/bioinformatics/btp616

Robrahn, L., Dupont, A., Jumpertz, S., Zhang, K., Holland, C. H., Guillaume, J., … Cramer, T. (2022). Stabilization but no functional influence of HIF-1expression in the intestinal epithelium during salmonella typhimurium infection. Infection and Immunity, iai0022221. http://doi.org/10.1128/iai.00222-21

Roopra, A. (2020). MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data. PLoS Computational Biology, 16(4), e1007800. http://doi.org/10.1371/journal.pcbi.1007800

Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., … Seelig, G. (2018). Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science, 360(6385), 176–182. http://doi.org/10.1126/science.aam8999

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. http://doi.org/10.1016/0377-0427(87)90125-7

Rydenfelt, M., Klinger, B., Klünemann, M., & Blüthgen, N. (2020). SPEED2: Inferring upstream pathway activity from differential gene expression. Nucleic Acids Research, 48(W1), W307–W312. http://doi.org/10.1093/nar/gkaa236

Salviato, E., Djordjilović, V., Chiogna, M., & Romualdi, C. (2019). SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways. PLoS Computational Biology, 15(10), e1007357. http://doi.org/10.1371/journal.pcbi.1007357

Schneider, K. M., Mohs, A., Gui, W., Galvez, E. J. C., Candels, L. S., Hoenicke, L., … Trautwein, C. (2022). Imbalanced gut microbiota fuels hepatocellular carcinoma development by shaping the hepatic inflammatory microenvironment. Nature Communications, 13(1), 3964. http://doi.org/10.1038/s41467-022-31312-5

Schubert, M., Klinger, B., Klünemann, M., Sieber, A., Uhlitz, F., Sauer, S., … Saez-Rodriguez, J. (2018). Perturbation-response genes reveal signaling footprints in cancer gene expression. Nature Communications, 9(1), 20. http://doi.org/10.1038/s41467-017-02391-6

Segal, J. M., Kent, D., Wesche, D. J., Ng, S. S., Serra, M., Oulès, B., … Rashid, S. T. (2019). Single cell analysis of human foetal liver captures the transcriptional profile of hepatobiliary hybrid progenitors. Nature Communications, 10(1), 3350. http://doi.org/10.1038/s41467-019-11266-x

Sergushichev, A. (2016). An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. BioRxiv. http://doi.org/10.1101/060012

Sezgin, S., Hassan, R., Zühlke, S., Kuepfer, L., Hengstler, J. G., Spiteller, M., & Ghallab, A. (2018). Spatio-temporal visualization of the distribution of acetaminophen as well as its metabolites and adducts in mouse livers by MALDI MSI. Archives of Toxicology, 92(9), 2963–2977. http://doi.org/10.1007/s00204-018-2271-3

Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., … Regev, A. (2014). Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 510(7505), 363–369. http://doi.org/10.1038/nature13437

Silge, J., & Robinson, D. (2016). Tidytext: Text mining and analysis using tidy data principles in r. The Journal of Open Source Software, 1(3). http://doi.org/10.21105/joss.00037

Slenter, D. N., Kutmon, M., Hanspers, K., Riutta, A., Windsor, J., Nunes, N., … Willighagen, E. L. (2018). WikiPathways: A multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Research, 46(D1), D661–D667. http://doi.org/10.1093/nar/gkx1064

Smits, A. H., Ziebell, F., Joberty, G., Zinn, N., Mueller, W. F., Clauder-Münster, S., … Huber, W. (2019). Biological plasticity rescues target activity in CRISPR knock outs. Nature Methods, 16(11), 1087–1093. http://doi.org/10.1038/s41592-019-0614-5

Sousa Abreu, R. de, Penalva, L. O., Marcotte, E. M., & Vogel, C. (2009). Global signatures of protein and mRNA expression levels. Molecular Biosystems, 5(12), 1512–1526. http://doi.org/10.1039/b908315d

Staniek, J., Lorenzetti, R., Heller, B., Janowska, I., Schneider, P., Unger, S., … Rizzi, M. (2019). TRAIL-R1 and TRAIL-R2 mediate TRAIL-dependent apoptosis in activated primary human b lymphocytes. Frontiers in Immunology, 10, 951. http://doi.org/10.3389/fimmu.2019.00951

Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015). Computational and analytical challenges in single-cell transcriptomics. Nature Reviews. Genetics, 16(3), 133–145. http://doi.org/10.1038/nrg3833

Stricker, S. H., Köferle, A., & Beck, S. (2017). From profiles to function in epigenomics. Nature Reviews. Genetics, 18(1), 51–66. http://doi.org/10.1038/nrg.2016.138

Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., … Golub, T. R. (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 171(6), 1437–1452.e17. http://doi.org/10.1016/j.cell.2017.10.049

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., … Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15545–15550. http://doi.org/10.1073/pnas.0506580102

Svensson, V., Vento-Tormo, R., & Teichmann, S. A. (2018). Exponential scaling of single-cell RNA-seq in the past decade. Nature Protocols, 13(4), 599–604. http://doi.org/10.1038/nprot.2017.149

Szalai, B., & Saez-Rodriguez, J. (2020). Why do pathway methods work better than they should? FEBS Letters, 594(24), 4189–4200. http://doi.org/10.1002/1873-3468.14011

Szalai, B., Subramanian, V., Holland, C. H., Alföldi, R., Puskás, L. G., & Saez-Rodriguez, J. (2019). Signatures of cell death and proliferation in perturbation transcriptomics data-from confounding factor to effective prediction. Nucleic Acids Research, 47(19), 10010–10026. http://doi.org/10.1093/nar/gkz805

Tajti, F., Kuppe, C., Antoranz, A., Ibrahim, M. M., Kim, H., Ceccarelli, F., … Saez-Rodriguez, J. (2020). A functional landscape of CKD entities from public transcriptomic data. Kidney International Reports, 5(2), 211–224. http://doi.org/10.1016/j.ekir.2019.11.005

Tak, P. P., & Firestein, G. S. (2001). NF-kappaB: A key role in inflammatory diseases. The Journal of Clinical Investigation, 107(1), 7–11. http://doi.org/10.1172/{JCI11830}

Tanevski, J., Ramirez Flores, R. O., Gabor, A., Schapiro, D., & Saez-Rodriguez, J. (2020). Explainable multi-view framework for dissecting inter-cellular signaling from highly multiplexed spatial data. BioRxiv. http://doi.org/10.1101/2020.05.08.084145

Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., … Surani, M. A. (2009). mRNA-seq whole-transcriptome analysis of a single cell. Nature Methods, 6(5), 377–382. http://doi.org/10.1038/nmeth.1315

Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J.-S., … Romero, R. (2009). A novel signaling pathway impact analysis. Bioinformatics, 25(1), 75–82. http://doi.org/10.1093/bioinformatics/btn577

Tenenbaum, J. D., Walker, M. G., Utz, P. J., & Butte, A. J. (2008). Expression-based pathway signature analysis (EPSA): Mining publicly available microarray data for insight into human disease. BMC Medical Genomics, 1, 51. http://doi.org/10.1186/1755-8794-1-51

Teske, B. F., Wek, S. A., Bunpo, P., Cundiff, J. K., McClintick, J. N., Anthony, T. G., & Wek, R. C. (2011). The eIF2 kinase PERK and the integrated stress response facilitate activation of ATF6 during endoplasmic reticulum stress. Molecular Biology of the Cell, 22(22), 4390–4405. Retrieved from http://dx.doi.org/10.1091/mbc.E11-06-0510

Teufel, A., Itzel, T., Erhart, W., Brosch, M., Wang, X. Y., Kim, Y. O., … Hampe, J. (2016). Comparison of gene expression patterns between mouse models of nonalcoholic fatty liver disease and liver tissues from patients. Gastroenterology, 151(3), 513–525.e0. http://doi.org/10.1053/j.gastro.2016.05.051

Tomfohr, J., Lu, J., & Kepler, T. B. (2005). Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics, 6, 225. http://doi.org/10.1186/1471-2105-6-225

Torre, D., Lachmann, A., & Ma’ayan, A. (2018). BioJupies: Automated generation of interactive notebooks for RNA-seq data analysis in the cloud. Cell Systems, 7(5), 556–561.e3. http://doi.org/10.1016/j.cels.2018.10.007

Trescher, S., Münchmeyer, J., & Leser, U. (2017). Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization. BMC Systems Biology, 11(1), 41. http://doi.org/10.1186/s12918-017-0419-z

Väremo, L., Nielsen, J., & Nookaew, I. (2013). Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Research, 41(8), 4378–4391. http://doi.org/10.1093/nar/gkt111

Wang, Zhenjia, Civelek, M., Miller, C. L., Sheffield, N. C., Guertin, M. J., & Zang, C. (2018). BART: A transcription factor prediction tool with query gene sets or epigenomic profiles. Bioinformatics, 34(16), 2867–2869. http://doi.org/10.1093/bioinformatics/bty194

Wang, Zhong, Gerstein, M., & Snyder, M. (2009). RNA-seq: A revolutionary tool for transcriptomics. Nature Reviews. Genetics, 10(1), 57–63. http://doi.org/10.1038/nrg2484

Wang, Zichen, Monteiro, C. D., Jagodnik, K. M., Fernandez, N. F., Gundersen, G. W., Rouillard, A. D., … Ma’ayan, A. (2016). Extraction and analysis of signatures from the gene expression omnibus by the crowd. Nature Communications, 7, 12846. http://doi.org/10.1038/ncomms12846

Washburn, M. P., Koller, A., Oshiro, G., Ulaszek, R. R., Plouffe, D., Deciu, C., … Yates, J. R. (2003). Protein pathway and complex clustering of correlated mRNA and protein expression analyses in saccharomyces cerevisiae. Proceedings of the National Academy of Sciences of the United States of America, 100(6), 3107–3112. http://doi.org/10.1073/pnas.0634629100

Wenk, M. R. (2005). The emerging field of lipidomics. Nature Reviews. Drug Discovery, 4(7), 594–610. http://doi.org/10.1038/nrd1776

Wickham, H. (2016). ggplot2 - elegant graphics for data analysis (2nd ed.). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-24277-4

Wilhelm, B. T., & Landry, J.-R. (2009). RNA-seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods, 48(3), 249–257. http://doi.org/10.1016/j.ymeth.2009.03.016

Wingender, E., Schoeps, T., Haubrock, M., Krull, M., & Dönitz, J. (2018). TFClass: Expanding the classification of human transcription factors to their mammalian orthologs. Nucleic Acids Research, 46(D1), D343–D347. http://doi.org/10.1093/nar/gkx987

Wiredja, D. D., Koyutürk, M., & Chance, M. R. (2017). The KSEA app: A web-based tool for kinase activity inference from quantitative phosphoproteomics. Bioinformatics, 33(21), 3489–3491. http://doi.org/10.1093/bioinformatics/btx415

Younossi, Z. M., Stepanova, M., Ong, J., Trimble, G., AlQahtani, S., Younossi, I., … Henry, L. (2020). Nonalcoholic steatohepatitis is the most rapidly increasing indication for liver transplantation in the united states. Clinical Gastroenterology and Hepatology. http://doi.org/10.1016/j.cgh.2020.05.064

Zakrzewska, A., Cui, C., Stockhammer, O. W., Benard, E. L., Spaink, H. P., & Meijer, A. H. (2010). Macrophage-specific gene functions in Spi1-directed innate immunity. Blood, 116(3), e1–11. http://doi.org/10.1182/blood-2010-01-262873

Zappia, L., Phipson, B., & Oshlack, A. (2017). Splatter: Simulation of single-cell RNA sequencing data. Genome Biology, 18(1), 174. http://doi.org/10.1186/s13059-017-1305-0

Zardi, E. M., Navarini, L., Sambataro, G., Piccinni, P., Sambataro, F. M., Spina, C., & Dobrina, A. (2013). Hepatic PPARs: Their role in liver physiology, fibrosis and treatment. Current Medicinal Chemistry, 20(27), 3370–3396. http://doi.org/10.2174/09298673113209990136

Zhang, K., Hocker, J. D., Miller, M., Hou, X., Chiou, J., Poirion, O. B., … Ren, B. (2021). A cell atlas of chromatin accessibility across 25 adult human tissues. BioRxiv. http://doi.org/10.1101/2021.02.17.431699

Zhao, S., Fung-Leung, W.-P., Bittner, A., Ngo, K., & Liu, X. (2014). Comparison of RNA-seq and microarray in transcriptome profiling of activated t cells. Plos One, 9(1), e78644. http://doi.org/10.1371/journal.pone.0078644