@Article{Schubert2018,
author={Schubert, Michael
and Klinger, Bertram
and Kl{\"u}nemann, Martina
and Sieber, Anja
and Uhlitz, Florian
and Sauer, Sascha
and Garnett, Mathew J.
and Bl{\"u}thgen, Nils
and Saez-Rodriguez, Julio},
title={Perturbation-response genes reveal signaling footprints in cancer gene expression},
journal={Nature Communications},
year={2018},
volume={9},
number={1},
pages={20},
abstract={Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.},
issn={2041-1723},
doi={10.1038/s41467-017-02391-6},
url={https://doi.org/10.1038/s41467-017-02391-6}
}

@article{dugourd2019, 
title={Footprint-based functional analysis of multiomic data}, 
volume={15}, 
DOI={10.1016/j.coisb.2019.04.002}, 
journal={Current Opinion in Systems Biology}, 
author={Dugourd, Aurelien and Saez-Rodriguez, Julio}, 
year={2019}, 
pages={82–90}}

@article{HOLLAND2019194431,
title = "Transfer of regulatory knowledge from human to mouse for functional genomics analysis",
journal = "Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms",
pages = "194431",
year = "2019",
issn = "1874-9399",
doi = "https://doi.org/10.1016/j.bbagrm.2019.194431",
url = "http://www.sciencedirect.com/science/article/pii/S1874939919302287",
author = "Christian H. Holland and Bence Szalai and Julio Saez-Rodriguez",
keywords = "Functional genomics, Signaling footprints, Pathway activity, Transcription factor activity",
abstract = "Transcriptome profiling followed by differential gene expression analysis often leads to lists of genes that are hard to analyze and interpret. Functional genomics tools are powerful approaches for downstream analysis, as they summarize the large and noisy gene expression space into a smaller number of biological meaningful features. In particular, methods that estimate the activity of processes by mapping transcripts level to process members are popular. However, footprints of either a pathway or transcription factor (TF) on gene expression show superior performance over mapping-based gene sets. These footprints are largely developed for humans and their usability in the broadly-used model organism Mus musculus is uncertain. Evolutionary conservation of the gene regulatory system suggests that footprints of human pathways and TFs can functionally characterize mice data. In this paper we analyze this hypothesis. We perform a comprehensive benchmark study exploiting two state-of-the-art footprint methods, DoRothEA and an extended version of PROGENy. These methods infer TF and pathway activity, respectively. Our results show that both can recover mouse perturbations, confirming our hypothesis that footprints are conserved between mice and humans. Subsequently, we illustrate the usability of PROGENy and DoRothEA by recovering pathway/TF-disease associations from newly generated disease sets. Additionally, we provide pathway and TF activity scores for a large collection of human and mouse perturbation and disease experiments (2374). We believe that this resource, available for interactive exploration and download (https://saezlab.shinyapps.io/footprint_scores/), can have broad applications including the study of diseases and therapeutics."
}

@article {Holland2019,
	author = {Holland, Christian H. and Tanevski, Jovan and Gleixner, Jan and Kumar, Manu P. and Mereu, Elisabetta and Perales-Pat{\'o}n, Javier and Joughin, Brian A. and Stegle, Oliver and Lauffenburger, Douglas A. and Heyn, Holger and Szalai, Bence and Saez-Rodriguez, Julio},
	title = {Robustness and applicability of functional genomics tools on scRNA-seq data},
	elocation-id = {753319},
	year = {2019},
	doi = {10.1101/753319},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Many tools have been developed to extract functional and mechanistic insight from bulk transcriptome profiling data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events, low library sizes and a comparatively large number of samples/cells. It is thus not clear if functional genomics tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. To address this question, we performed benchmark studies on in silico and in vitro single-cell RNA-seq data. We included the bulk-RNA tools PROGENy, GO enrichment and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compared them against the tools AUCell and metaVIPER, designed for scRNA-seq. For the in silico study we simulated single cells from TF/pathway perturbation bulk RNA-seq experiments. Our simulation strategy guarantees that the information of the original perturbation is preserved while resembling the characteristics of scRNA-seq data. We complemented the in silico data with in vitro scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on both the simulated and real data revealed comparable performance to the original bulk data. Additionally, we showed that the TF and pathway activities preserve cell-type specific variability by analysing a mixture sample sequenced with 13 scRNA-seq different protocols. Our analyses suggest that bulk functional genomics tools can be applied to scRNA-seq data, outperforming dedicated single cell tools. Furthermore we provide a benchmark for further methods development by the community.},
	URL = {https://www.biorxiv.org/content/early/2019/09/01/753319},
	eprint = {https://www.biorxiv.org/content/early/2019/09/01/753319.full.pdf},
	journal = {bioRxiv}
}

@Article{Stuart2019,
author={Stuart, Tim
and Butler, Andrew
and Hoffman, Paul
and Hafemeister, Christoph
and Papalexi, Efthymia
and Mauck, William M.  III
and Hao, Yuhan
and Stoeckius, Marlon
and Smibert, Peter
and Satija, Rahul},
title={Comprehensive Integration of Single-Cell Data},
journal={Cell},
year={2019},
month={Jun},
day={13},
publisher={Elsevier},
volume={177},
number={7},
pages={1888-1902.e21},
issn={0092-8674},
doi={10.1016/j.cell.2019.05.031},
url={https://doi.org/10.1016/j.cell.2019.05.031}
}
