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 |
| 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 |
| Perils of batch correction 1 (Jaffe) | Jaffe, A.E., et al., Practical impacts of genomic data “cleaning” on biological discovery using surrogate variable analysis. BMC Bioinformatics, 2015. 16(1): p. 1-10. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0808-5 | - It’s easy to remove real signal if you aren’t careful with your batch correction | Batch effects |
| Perils of batch correction 2 (Nygaard) | Nygaard, V., Rødland, E.A. & Hovig, E. Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses. Biostatistics (Oxford, England) 17, 29-39 (2015). https://academic.oup.com/biostatistics/article/17/1/29/1744261 | - It’s easy to over-estimate confidence after batch correction, particularly with unbalanced experimental designs | Batch effects |
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 | Single cell, pseudotime |
| 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 | Batch effects, single cell, Irizarry |
| Single cell experimental design | Tung, P.-Y., Blischak, J.D., Hsiao, C.J., Knowles, D.A., Burnett, J.E., Pritchard, J.K., and Gilad, Y. (2017). Batch effects and the effective design of single-cell gene expression studies. Scientific Reports 7, 39921. https://www.nature.com/articles/srep39921 | - higher correlation of samples from the same individual within batches than across - ERCCs are affected by biological variation in batches so can’t be used to get rid of technical variation - Recommend combining individuals in one technical replicate as a way to save money - 75 cells with 1.5 million reads is sufficient | Batch effects, single cell, experimental design |
| The Macosko paper; The Drop-seq paper; The retina paper | Macosko, E.Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A.R., Kamitaki, N., Martersteck, E.M., et al. (2015). Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202-1214. https://www.cell.com/abstract/S0092-8674(15)00549-8 | - first demonstration of Drop-seq - 39 cell type clusters identified using 7 replicates (multiple animals per replicate) - first to use tSNE and projection (of held-out cells) onto the reduced dimensional space - first to use Louvain clustering in single cell RNA-seq (had previously been used for cytof by Dana Pe’er’s group) - lots of interesting stuff to go through in the supplement | Single cell, drop-seq, tSNE, clustering, replicability |
| The Shekhar paper; The bipolar cell paper | Shekhar, K., et al. (2016). “Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.” Cell 166(5): 1308-1323.e1330. https://www.cell.com/cell/abstract/S0092-8674(16)31007-8 | - Drop-seq and Smart-seq2, two Cre-driver lines - Random forest classifier from clusters, re-classified data from Macosko - More cells with few genes is better than few cells with many genes for defining clusters - Tried many different dimensionality reduction and clustering techniques | Single cell, drop-seq |
| The Heimberg paper | Heimberg, G., et al. (2016). “Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing.” Cell Syst 2(4): 239-250 https://www.cell.com/fulltext/S2405-4712(16)30109-0 | - Shallow sequencing is sufficient for cell typing because cell types differ across many genes that co-vary | Single cell, theory, dimensionality |
| RNA velocity | La Manno, G., et al. (2018). “RNA velocity of single cells.” Nature 560(7719): 494 https://www.nature.com/articles/s41586-018-0414-6 | - The fraction of unspliced to spliced mRNA for certain genes provides temporal information about cell differentiation (expansion of Zeisel 2011) - Requires kNN smoothing (5-10 samples) - Commentary from Pachter and Svennson here: https://doi.org/10.1016/j.molcel.2018.09.026 | Single cell, trajectory, pseudotime |
| The Soneson/Robinson DE paper | Soneson, C. and M. D. Robinson (2018). “Bias, robustness and scalability in single-cell differential expression analysis.” Nat Methods 15(4): 255-261. https://www.nature.com/articles/nmeth.4612 | - T-test and Mann-Whitney are better for scDE than tests with more distributional assumptions | Single cell, DE, comparative assessment |
| The Soneson/Robinson clustering paper | Duò, A., et al. (2018). “A systematic performance evaluation of clustering methods for single-cell RNA-seq data [version 2; referees: 2 approved].” F1000Research 7(1141). https://f1000research.com/articles/7-1141/v1 | - Clustering algorithms have different results - Seurat and SC3 generally good. Seurat very stable across different gene selection strategies (high expression, HVG, M3Drop) - Combining two methods into an ensemble did not improve the performance compared to the best of the individual methods - “We also investigated whether the number of detected features per cell differed between the clusters, using a Kruskal-Wallis test. No strong association was found for the simulated data sets indicating that there is low inherent bias in the clustering algorithms. For most of the real data sets, we found highly significant differences in the number of detected features between cells in different clusters. However, it is unclear whether this represents a technical effect or a biological difference between the cell populations.” | Single cell, clustering, comparative assessment |
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 |