A Gillis lab guide to papers in functional genomics (and beyond)

What we talk about when we talk about ...

Co-expression

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
The Eisen Paper	Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95, 14863-14868. http://www.pnas.org/content/95/25/14863	- in expression data, genes cluster according to their function = can infer functions for unannotated genes -	Coexpression, microarray, gene function
Paul’s coexpression meta-analysis Paper	Lee, H.K., Hsu, A.K., Sajdak, J., Qin, J., and Pavlidis, P. (2004). Coexpression analysis of human genes across many microarray data sets. Genome research 14, 1085-1094. https://www.ncbi.nlm.nih.gov/pubmed/15173114	- confirmation of coexpression in multiple data sets is correlated with functional relatedness - cluster analysis of the aggregate network reveals functionally coherent groups of genes	Paul, coexpression, meta-analysis, microarray, human, GO
	Gaiteri C, Ding Y, French B, Tseng GC, Sibille E. Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders. Genes, brain, and behavior. 2014;13(1):13-24. Epub 2013/12/11. doi: 10.1111/gbb.12106. PubMed PMID: 24320616; PubMed Central PMCID: PMCPMC3896950 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896950	- Review on sources of co-expression and use in brain specific analyses	Co-expression, brainspan, brain
That Speed Paper	Freytag S, Gagnon-Bartsch J, Speed TP, Bahlo M. Systematic noise degrades gene co-expression signals but can be corrected. BMC Bioinformatics. 2015;16(1):309. doi: 10.1186/s12859-015-0745-3 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0745-3	- Assessment of the effects of RUV on gene coexpression - Claims that RUV corrected data is better in generating good coexpression networks - but true effect of removing variation is not always clear (biology versus technical)	Gene co-expression, Data cleaning, Removal of unwanted variation (RUV)

RNA-seq

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
MAQC paper	SEQC/MAQC-III Consortium. A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nat Biotech. 2014;32(9):903-14. doi: 10.1038/nbt.2957 https://www.nature.com/articles/nbt.2957	- Low expressing genes are unreliable in RNA-seq due to technical and methodological biases - Filter away bottom third of genes (low expressing) and fold changes > 2 - STAR performs well	RNA-seq, biases, expression, STAR
SEQC collection	https://www.nature.com/collections/ppgrhzcwpf
STAR paper	Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. doi: 10.1093/bioinformatics/bts635. PubMed PMID: PMC3530905 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3530905/	- STAR is awesome/useful/practical and fast. - Also, funny ROC.	STAR, software, RNA-seq alignment
MAD scores paper	Teng M, Love MI, Davis CA, Djebali S, Dobin A, Graveley BR, et al. A benchmark for RNA-seq quantification pipelines. Genome Biology. 2016;17(1):74. doi: 10.1186/s13059-016-0940-1 https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0940-1	- Correlations are a bad metric when dynamic range is high (eg. RNA-seq expression data)	Metrics, expression, benchmarking
The Leek Batch Effects Paper	Leek, J.T., Scharpf, R.B., Bravo, H.C., Simcha, D., Langmead, B., Johnson, W.E., Geman, D., Baggerly, K., and Irizarry, R.A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11, 733-739. https://www.ncbi.nlm.nih.gov/pubmed/20838408	- Batch effects are common and pernicious in high throughput biological data	Batch effects, Irizarry

Single cell

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
Monocle	Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N.J., Livak, K.J., Mikkelsen, T.S., and Rinn, J.L. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotech 32, 381-386. https://www.nature.com/articles/nbt.2859	- cells can ordered in ‘pseudotime’ for developmental trajectory inference
The Hicks Paper	Hicks, S.C., Townes, F.W., Teng, M., and Irizarry, R.A. (2017). Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics (Oxford, England). https://www.ncbi.nlm.nih.gov/pubmed/29121214	- there are huge batch effects in single cell RNA-seq data which can be misinterpreted as biological variability - PC1 is usually correlated with the number of genes detected per cell

Human genetics

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
AJHG skew paper	Amos-Landgraf JM, Cottle A, Plenge RM, Friez M, Schwartz CE, Longshore J, et al. X Chromosome–Inactivation Patterns of 1,005 Phenotypically Unaffected Females. American Journal of Human Genetics. 2006;79(3):493-9. PubMed PMID: PMC1559535 http://www.cell.com/ajhg/fulltext/S0002-9297(07)62748-7	- Skew ratios within a population of women are normally distributed - First “large” scale XCI analysis using the AR X-inactivation assay (HUMARA)	X-inactivation, X-skew, tissue dependent
MacArthur XCI paper	Tukiainen T, Villani A-C, Yen A, Rivas MA, Marshall JL, Satija R, et al. Landscape of X chromosome inactivation across human tissues. Nature. 2017;550:244. doi: 10.1038/nature24265 https://www.nature.com/articles/nature24265	- Assessment of GTEx data for XCI and incomplete inactivation (escapers) - First “large” scale use of RNA-seq data for XCI	Dosage compensation, GTEx, X-inactivation, X-escapers
GWAs	Hirschhorn JN, Daly MJ. Genome-wide association studies for common diseases and complex traits. Nature Reviews Genetics. 2005;6:95. doi: 10.1038/nrg1521 https://www.nature.com/articles/nrg1521	- Key paper on the thoughts/ideas behind genome wide association studies for complex disorders	GWAs, SNPs, LD
Disease networks (Barabasi paper)	Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L. The human disease network. Proceedings of the National Academy of Sciences. 2007;104(21):8685-90. doi: 10.1073/pnas.0701361104. http://www.pnas.org/content/104/21/8685.long
Polygenic risk scores	The International Schizophrenia C. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748. doi: 10.1038/nature08185 https://www.nature.com/articles/nature08185	- Scores to explain distributed risk of disease	GWAs, risk scores
Wigler paper	Ronemus M, Iossifov I, Levy D, Wigler M. The role of de novo mutations in the genetics of autism spectrum disorders. Nature Reviews Genetics. 2014;15:133. doi: 10.1038/nrg3585 https://www.nature.com/articles/nrg3585	- De novo mutations	Autism, de novo, recurrence
Ivan’s paper	Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515(7526):216-21. doi: 10.1038/nature13908. PubMed PMID: PMC4313871 https://www.nature.com/articles/nature13908	- Recurrent genes in autism - LGD (likely gene disruptive)	Autism, de novo, recurrence
PGC paper	Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421. doi: 10.1038/nature13595 https://www.nature.com/articles/nature13595	- Schizophrenia loci are still mostly in non-coding regions of the genome	Schizophrenia, GWAs
ExAC paper	Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536:285. doi: 10.1038/nature19057 https://www.nature.com/articles/nature19057	- Background variation in the human population - Knockout variants via depletion - PLI scores for genes (probability of being loss-of-function (LoF) intolerant)	Variant scores, WES
RVIS paper	Petrovski S, Wang Q, Heinzen EL, Allen AS, Goldstein DB. Genic Intolerance to Functional Variation and the Interpretation of Personal Genomes. PLOS Genetics. 2013;9(8):e1003709. doi: 10.1371/journal.pgen.1003709 http://journals.plos.org/plosgenetics/article/citation?id=10.1371/journal.pgen.1003709	- Scores for variation intolerance - Precedes the ExAC PLI scores - genes responsible for Mendelian diseases are significantly more intolerant to functional genetic variation than genes that do not cause any known disease, but with striking variation in intolerance among genes causing different classes of genetic disease	Variant scores, WES

Network analysis

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
The first WGCNA Paper	Zhang, B., and Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology 4, Article17. https://www.ncbi.nlm.nih.gov/pubmed/16646834	- Argue for weighted networks over binary networks - Weighted networks lead to more cohesive modules	Coexpression, network analysis, clustering, WGCNA
The Dynamic Tree Cutting Paper	Langfelder, P., Zhang, B., and Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics (Oxford, England) 24, 719-720. https://www.ncbi.nlm.nih.gov/pubmed/18024473	- dynamic tree cutting can yield more intuitive clusters than picking a cut height on a dendrograms	Clustering, WGCNA, R, Bioconductor
The WGCNA Module Preservation Paper	Langfelder, P., Luo, R., Oldham, M.C., and Horvath, S. (2011). Is My Network Module Preserved and Reproducible? PLOS Computational Biology 7, e1001057. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1001057	- Applying cluster stability/validation methods to gene networks - “We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics” - “Cluster validation statistics may not be appropriate when modules are not defined as clusters. In general, assessing module preservation is a different task from assessing cluster preservation.”	Clustering, replicability, network analysis, WGCNA, metrics

Machine learning

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
Hand paper	Hand DJ. Classifier technology and the illusion of progress. Statistical science. 2006:1-14 https://arxiv.org/pdf/math/0606441.pdf	- simple methods are often surprisingly effective	Machine learning
GeneMania / MouseFunc	Peña-Castillo, L., Tasan, M., Myers, C.L., Lee, H., Joshi, T., Zhang, C., Guan, Y., Leone, M., Pagnani, A., Kim, W.K., et al. (2008). A critical assessment of Mus musculus gene function prediction using integrated genomic evidence. Genome biology 9, S2-S2. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2008-9-s1-s2	- Gene function prediction is hard	Machine learning, gene function, critical assessment
CAFA	Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, et al. A large-scale evaluation of computational protein function prediction. Nature Methods. 2013;10:221. doi: 10.1038/nmeth.2340 https://www.nature.com/articles/nmeth.2340	- BLAST works okay compared to “naïve” methods	Competitions, machine learning

Gene set enrichment analysis

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
Goeman 2007	Goeman, J.J., and Buhlmann, P. (2007). Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics (Oxford, England) 23, 980-987. https://academic.oup.com/bioinformatics/article-abstract/23/8/980/198511	- For enrichment analysis, sample permutation and gene permutation have different underlying assumptions - Gene permutation does not take coexpression into account, making p-values easily misinterpreted/”anti-conservative” - Competitive gene set testing “creates an unnecessary rift between single gene testing and gene set testing”	Functional enrichment, commentary
Irizarry’s Enrichment Paper (The Anti-GSEA Paper)	Irizarry, R.A., Wang, C., Zhou, Y., and Speed, T.P. (2009). Gene set enrichment analysis made simple. Statistical methods in medical research 18, 565-575. https://www.ncbi.nlm.nih.gov/pubmed/20048385	- GSEA is unnecessarily complicated	Functional enrichment, expression analysis, Irizarry, simple methods
Tamayo 2012	Tamayo, P., Steinhardt, G., Liberzon, A., and Mesirov, J.P. (2016). The limitations of simple gene set enrichment analysis assuming gene independence. Statistical methods in medical research 25, 472-487. https://www.ncbi.nlm.nih.gov/pubmed/23070592	- Gene coexpression invalidates simple gene set enrichment approaches	Coexpression, functional enrichment

Statistics

Nickname(s)	Citation and URL	Main Takeaways/Comments	Keywords
FDR	Noble WS. How does multiple testing correction work? Nature biotechnology. 2009;27(12):1135-7. doi: 10.1038/nbt1209-1135. PubMed PMID: PMC2907892. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2907892/		Multiple test correction
The Gap Statistic Paper	Tibshirani, R., Walther, G., and Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63, 411-423. https://statweb.stanford.edu/~gwalther/gap	- Propose the ‘gap statistic’ for estimating the number of clusters in a set of data - “The technique uses the output of any clustering algorithm, comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution.”	Clustering, Tibshirani, metrics
Tibshirani 2005	Tibshirani, R., and Walther, G. (2005). Cluster validation by prediction strength. Journal of Computational and Graphical Statistics 14, 511-528. http://statweb.stanford.edu/~gwalther/predictionstrength.pdf	- “This article proposes a new quantity for assessing the number of groups or clusters in a dataset. The key idea is to view clustering as a supervised classification problem, in which we must also estimate the “true” class labels. The resulting “prediction strength” measure assesses how many groups can be predicted from the data, and how well.”	Clustering, Tibshirani, replicability, metrics