Week 1

Transformation and robust summaries

Robust statistics

Robust Statistics, Peter. J. Huber and Elvezio M. Ronchetti, Wiley, 2009.

Introduction to Robust Estimation and Hypothesis Testing, Rand R. Wilcox, 2012.

Robust Statistics: The Approach Based on Influence Functions, Frank R. Hampel, Elvezio M. Ronchetti, Peter J. Rousseeuw, Werner A. Stahel

Week 2

Basic Bioconductor infrastructure

For more information about the GenomicRanges package, check out the PLOS Comp Bio paper, which the authors of GenomicRanges published:

http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118

Also the software vignettes have a lot of details about the functionality. Check out “An Introduction to Genomic Ranges Classes”:

http://www.bioconductor.org/packages/release/bioc/vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf

All of the vignette PDFs are available here:

http://www.bioconductor.org/packages/release/bioc/html/GenomicRanges.html

Week 3

Basic inference for microarray data

Smyth GK, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat Appl Genet Mol Biol. 2004 http://www.ncbi.nlm.nih.gov/pubmed/16646809

Statistical inference

C. Kendziorski, R. A. Irizarry, K.-S. Chen, J. D. Haag, and M. N. Gould, “On the utility of pooling biological samples in microarray experiments”, PNAS, 2005. http://www.pnas.org/content/102/12/4252.long

Smyth GK, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat Appl Genet Mol Biol. 2004 http://www.ncbi.nlm.nih.gov/pubmed/16646809

Rank tests

Week 4

Modeling

Smyth GK, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat Appl Genet Mol Biol. 2004 http://www.ncbi.nlm.nih.gov/pubmed/16646809

Normalization

loess

W. S. Cleveland, E. Grosse and W. M. Shyu (1992) Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole.

Quantile normalization

Bolstad BM, Irizarry RA, Astrand M, Speed TP. “A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.” Bioinformatics. 2003. http://www.ncbi.nlm.nih.gov/pubmed/12538238

Variance stabilization

For microarray:

Wolfgang Huber, Anja von Heydebreck, Holger Sültmann, Annemarie Poustka and Martin Vingron, “Variance stabilization applied to microarray data calibration and to the quantification of differential expression” Bioinformatics, 2002. http://bioinformatics.oxfordjournals.org/content/18/suppl_1/S96.short

B.P. Durbin, J.S. Hardin, D.M. Hawkins and D.M. Rocke, “A variance-stabilizing transformation for gene-expression microarray data”, Bioinformatics. 2002. http://bioinformatics.oxfordjournals.org/content/18/suppl_1/S105

Wolfgang Huber, Anja von Heydebreck, Holger Sueltmann, Annemarie Poustka, Martin Vingron, “Parameter estimation for the calibration and variance stabilization of microarray data” Stat Appl Mol Biol Genet, 2003. http://dx.doi.org/10.2202/1544-6115.1008

For NGS read counts:

Simon Anders and Wolfgang Huber, “Differential expression analysis for sequence count data”, Genome Biology, 2010. http://genomebiology.com/2010/11/10/r106

General discussion of variance stabilization:

Robert Tibshirani, “Variance Stabilization and the Bootstrap” Biometrika, 1988. http://www.jstor.org/discover/10.2307/2336593

Week 5

Distance lecture

MDS references

Wikipedia: http://en.wikipedia.org/wiki/Multidimensional_scaling

Cox, T. F. and Cox, M. A. A. (2001) Multidimensional Scaling. Second edition. Chapman and Hall.
Gower, J. C. (1966) Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–328.
Torgerson, W. S. (1958). Theory and Methods of Scaling. New York: Wiley.

Hierarchical clustering

Wikipedia: http://en.wikipedia.org/wiki/Hierarchical_clustering

a subset of the most recent references from ?hclust:

Legendre, P. and L. Legendre (2012). Numerical Ecology, 3rd English ed. Amsterdam: Elsevier Science BV.
Murtagh, F. and Legendre, P. (2013). Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? Journal of Classification (in press).

Week 6

Batch adjustment

Principal Components Analysis (PCA)

Jolliffe, Ian. Principal component analysis. John Wiley & Sons, Ltd, 2005.

Dunteman, George H. Principal components analysis. No. 69. Sage, 1989.

Week 7

Gene set testing

Methods within the limma package

Wu D, Lim E, Vaillant F, Asselin-Labat ML, Visvader JE, Smyth GK. “ROAST: rotation gene set tests for complex microarray experiments”. Bioinformatics. 2010.

http://www.ncbi.nlm.nih.gov/pubmed/20610611

Di Wu and Gordon K. Smyth, “Camera: a competitive gene set test accounting for inter-gene correlation” Nucleic Acids Research, 2012.

http://nar.oxfordjournals.org/content/40/17/e133

GSEA

Subramanian A1, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP, “Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles” Proc Natl Acad Sci U S A. 2005.

http://www.ncbi.nlm.nih.gov/pubmed/16199517

Correlation within gene sets

William T. Barry, Andrew B. Nobel, and Fred A. Wright, “A statistical framework for testing functional categories in microarray data” Ann. Appl. Stat, 2008.

http://projecteuclid.org/euclid.aoas/1206367822

William Barry has a package safe in Bioconductor for gene set testing with resampling.

http://www.bioconductor.org/packages/release/bioc/html/safe.html

Daniel M Gatti, William T Barry, Andrew B Nobel, Ivan Rusyn and Fred A Wright, “Heading Down the Wrong Pathway: on the Influence of Correlation within Gene Sets”, BMC Genomics, 2010.

http://www.biomedcentral.com/1471-2164/11/574#B24

Gene sets and power

The following article points out an issue with gene set testing: the power to detect differential expression for an individual gene depends on the number of NGS reads which align to that gene, which depends on the transcript length among other factors.

Alicia Oshlack* and Matthew J Wakefield, “Transcript length bias in RNA-seq data confounds systems biology”, Biology Direct, 2009.

http://www.biologydirect.com/content/4/1/14

The dataset used in this lab

Masuno K, Haldar SM, Jeyaraj D, Mailloux CM, Huang X, Panettieri RA Jr, Jain MK, Gerber AN., “Expression profiling identifies Klf15 as a glucocorticoid target that regulates airway hyperresponsiveness”. Am J Respir Cell Mol Biol. 2011.

http://www.ncbi.nlm.nih.gov/pubmed/21257922

Gene set analysis

Virtaneva K, “Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics.” PNAS 2001

http://www.ncbi.nlm.nih.gov/pubmed/?term=11158605

Jelle J. Goeman and Peter Bühlmann, “Analyzing gene expression data in terms of gene sets: methodological issues” Bioinformatics 2006.

http://bioinformatics.oxfordjournals.org/content/23/8/980

Multiple testing

Yoav Benjamini and Yosef Hochberg, “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing”. Journal of the Royal Statistical Society. 1995.

http://www.jstor.org/discover/10.2307/2346101

Using limma for microarray analysis

Smyth GK, “Linear models and empirical bayes methods for assessing differential expression in microarray experiments”. Stat Appl Genet Mol Biol. 2004 http://www.ncbi.nlm.nih.gov/pubmed/16646809

Week 8

ChIP-seq analysis

Model-based Analysis for ChIP-Seq (MACS)

Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. “Model-based Analysis of ChIP-Seq (MACS)”. Genome Biol. 2008.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2592715/

Software:

http://liulab.dfci.harvard.edu/MACS/

Motif finding

Wikipedia’s article on DNA sequence motifs: http://en.wikipedia.org/wiki/Sequence_motif

A non-comprehensive list of software for motif finding:

A survey of motif finding algorithms: http://www.biomedcentral.com/1471-2105/8/S7/S21/

NGS read counting

Methods for counting reads which overlap features

Bioconductor packages:

summarizeOverlaps in the GenomicAlignments package

http://www.bioconductor.org/packages/devel/bioc/html/GenomicAlignments.html

featureCounts in the Rsubread package

Liao Y, Smyth GK, Shi W., “featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.” Bioinformatics. 2014

http://www.ncbi.nlm.nih.gov/pubmed/24227677

http://bioinf.wehi.edu.au/featureCounts/

easyRNAseq package

Delhomme N1, Padioleau I, Furlong EE, Steinmetz LM. “easyRNASeq: a bioconductor package for processing RNA-Seq data.” Bioinformatics. 2012.

http://www.ncbi.nlm.nih.gov/pubmed/22847932

http://www.bioconductor.org/packages/release/bioc/html/easyRNASeq.html

Command line tools:

htseq-count, a program in the htseq Python package

Simon Anders, Paul Theodor Pyl, Wolfgang Huber.

HTSeq — A Python framework to work with high-throughput sequencing data

bioRxiv preprint (2014), doi: 10.1101/002824

http://www-huber.embl.de/users/anders/HTSeq/doc/count.html

bedtools https://code.google.com/p/bedtools/
bedops https://code.google.com/p/bedops/

RNA-seq analysis

Introduction

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B., “Mapping and quantifying mammalian transcriptomes by RNA-Seq”, Nat Methods. 2008.

http://www.nature.com/nmeth/journal/v5/n7/full/nmeth.1226.html

John C. Marioni, Christopher E. Mason, Shrikant M. Mane, Matthew Stephens, and Yoav Gilad, “RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays” Genome Res. 2008.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2527709/

Trapnell C, Williams BA, Pertea G, Mortazavi AM, Kwan G, van Baren MJ, Salzberg SL, Wold B, Pachter L., “Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation”, Nature Biotechnology, 2010.

http://www.nature.com/nbt/journal/v28/n5/full/nbt.1621.html

http://cufflinks.cbcb.umd.edu/

Hammer et al

Hammer P, Banck MS, Amberg R, Wang C, Petznick G, Luo S, Khrebtukova I, Schroth GP, Beyerlein P, Beutler AS. “mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain.” Genome Res. 2010

http://www.ncbi.nlm.nih.gov/pubmed?term=20452967

ReCount

Frazee AC, Langmead B, Leek JT. “ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets”. BMC Bioinformatics 12:449 http://www.ncbi.nlm.nih.gov/pubmed/22087737

Negative Binomial methods for differential expression of count data

All the following methods are available on Bioconductor:

edgeR

Mark D. Robinson, Davis J. McCarthy, and Gordon K. Smyth, “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data” Bioinformatics 2010.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2796818/

DESeq (the latest version is a separate package, DESeq2)

Simon Anders and Wolfgang Huber, “Differential expression analysis for sequence count data”, Genome Biology 2010.

http://genomebiology.com/2010/11/10/r106

DSS

Hao Wu, Chi Wang, Zhijin Wu, “A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data” Biostatistics 2013.

http://biostatistics.oxfordjournals.org/content/14/2/232

Transformation followed by linear model methods

voom in the limma Bioconductor package

Charity W Law, Yunshun Chen, Wei Shi and Gordon K Smyth, “voom: precision weights unlock linear model analysis tools for RNA-seq read counts”, Genome Biology. 2014.

http://genomebiology.com/2014/15/2/R29

Resampling-based methods

SAMseq in the samr package on CRAN

Jun Li and Robert Tibshirani, “Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data”, Stat Methods Med Res. 2013.

http://smm.sagepub.com/content/22/5/519.short

Incorporating isoform-abundance

Cuffdiff (the latest version is Cuffdiff2)

Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L., “Differential analysis of gene regulation at transcript resolution with RNA-seq” Nat Biotechnol. 2013.

http://www.ncbi.nlm.nih.gov/pubmed/23222703

BitSeq

Peter Glaus, Antti Honkela, and Magnus Rattray, “Identifying differentially expressed transcripts from RNA-seq data with biological variation”, Bioinformatics. 2012.

http://bioinformatics.oxfordjournals.org/content/28/13/1721