Ballgown. flexible RNA-seq differential expression analysis. Alyssa Frazee Johns Hopkins
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1 Ballgown flexible RNA-seq differential expression analysis Alyssa Frazee Johns Hopkins
2 RNA-seq data Reads ( bases) Transcripts (RNA) Genome (DNA)
3 [use tool of your choice] align RNA-seq data analysis
4 [use tool of your choice] align RNA-seq data analysis (or not: de novo assembly)
5 [use tool of your choice] RNA-seq data analysis align assemble
6 RNA-seq data analysis [use tool of your choice] align assemble estimate abundances
7 RNA-seq data analysis [use tool of your choice] align assemble estimate abundances differential expression analysis
8 RNA-seq data analysis [use tool of your choice] align assemble estimate abundances differential expression analysis Is transcript X expressed differently in population A vs. population B?
9 RNA-seq data analysis [use tool of your choice] align assemble estimate abundances differential expression analysis Is transcript X expressed differently in population A vs. population B vs. population C?
10 RNA-seq data analysis [use tool of your choice] align assemble estimate abundances differential expression analysis Is transcript X expressed constantly over the course of time in the study?
11 [use tool of your choice] align Ballgown assemble estimate abundances organize output differential expression analysis visualization postprocessing
12 organize output: Tablemaker top_dir/! sample01/! e2t.ctab! e_data.ctab! i2t.ctab! i_data.ctab! t_data.ctab! sample02/! e2t.ctab! e_data.ctab! i2t.ctab! i_data.ctab! t_data.ctab! sample03/! e2t.ctab! e_data.ctab! i2t.ctab! i_data.ctab! t_data.ctab!
13 S4 class for transcript assemblies library(ballgown) my_assembly = ballgown(datadir='top_dir', samplepattern= 'sample') my_assembly ## ballgown instance with transcripts and 3 samples my_rsem_assembly = ballgownrsem(dir='top_dir', samples= c('sample01', 'sample02', 'sample03'), gtf='hg19_genes.gtf') my_rsem_assembly ## ballgown instance with transcripts and 3 samples
14 S4 class for transcript assemblies ballgown object expr data indexes structure exon intron transcript exon intron transcript pdata bamfiles e2t i2t t2g
15 convenient visualization plottranscripts(gene='xloc_000454', gown=my_assembly, samples='sample12', meas='fpkm', colorby='transcript', main='transcripts from gene XLOC_000454: sample 12, FPKM') transcript ID expression by transcript genomic position
16 fast, flexible statistical analysis stat_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='group ) head(stat_results) ## feature id pval qval ## transcript ## transcript ## transcript ## transcript ## transcript ## transcript drop-in replacement for Cuffdiff
17 fast, flexible statistical analysis stat_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='group ) head(stat_results) ## feature id pval qval ## transcript ## transcript ## transcript ## transcript ## transcript ## transcript drop-in replacement for Cuffdiff bonus: handles more experimental designs!
18 timecourse stat_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='group ) timecourse_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='time', timecourse=true) models expression over time as smooth curve; compares to model where expression is assumed constant over time
19 custom models stat_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='group ) timecourse_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='time', timecourse=true) mod = model.matrix(~ time + library_size) mod0 = model.matrix(~ library_size) my_timecourse_results = stattest(my_assembly, feature='transcript', meas='fpkm', mod=mod, mod0=mod0)
20 easy integration with other packages stat_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='group ) timecourse_results = stattest(my_assembly, feature='transcript', meas='fpkm', covariate='time', timecourse=true) mod = model.matrix(~ time + library_size) mod0 = model.matrix(~ library_size) my_timecourse_results = stattest(my_assembly, feature='transcript', meas='fpkm', mod=mod, mod0=mod0) library(limma) expression_mat = log2(texpr(my_assembly, 'FPKM') + 1) fit = lmfit(y, mod) fit = ebayes(fit, trend=true)
21 wrangling large datasets reanalysis of GEUVADIS data: eqtl
22 wrangling large datasets reanalysis / processing of GEUVADIS data Ballgown objects: figshare TopHat read alignments: ArrayExpress (E-GEUV-6) analysis code: GitHub,
23 thanks! Collaborators: Jeff Leek (advisor), Geo Pertea, Andrew Jaffe, Ben Langmead, Steven Salzberg References: manuscript: /03/30/ limma: Linear models for Microarray Data by Gordon K Smyth, in Bioinformatics and Computational Biology Solutions using R and Bioconductor, Springer 2005 Cufflinks: PMID (Trapnell et al 2010) RSEM: PMID (Li & Dewey 2011) Software: Ballgown is under Bioconductor review; currently available and fully documented at alyssafrazee/ballgown
24 differential expression model for each transcript, compare the fits of the following models using an F-test. Null hypothesis is that the fits of model (a) and model (b) are equally good; alternative is that (a) fits better. (a) (b) expression i = + 0 group i + expression i = + PX p=1 PX p=1 pconfounder ip + noise ip pconfounder ip + noise ip X X
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