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regularization and batch correction of samples run through cufflinks, followed by GMM annotation of genes and plotting with pheatmap
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source("https://bioconductor.org/biocLite.R") | |
# biocLite("sva") | |
# biocLite("ballgown") | |
library(ballgown) | |
library(sva) | |
library(pheatmap) | |
library(mixtools) | |
# Extract expression estimates | |
# data_directory = "/some/data/dir" | |
# bg = ballgown(dataDir=data_directory, samplePattern='^SCLC', meas='all') | |
# save(bg, file='bg.rda') | |
bg = load(file='bg.rda') | |
gene_fpkm = gexpr(bg) | |
genes = rownames(gene_fpkm) | |
samples = colnames(gene_fpkm) | |
# Annotate by library prep | |
relapse = samples[!grepl('tumor', samples)] | |
relapse.numbers = sub('FPKM.SCLC([[:digit:]]+)_.*','\\1', relapse, perl=TRUE) | |
relapse.first = relapse[as.integer(relapse.numbers) < 19] | |
relapse.second = relapse[as.integer(relapse.numbers) > 18] | |
# Filter by expressed status | |
relapse.first.expressed = apply(gene_fpkm[,relapse.first] > 1, 1, (function (x) table(x)['TRUE'])) | |
relapse.first.expressed[is.na(relapse.first.expressed)] = 0 | |
relapse.second.expressed = apply(gene_fpkm[,relapse.second] > 1, 1, (function (x) table(x)['TRUE'])) | |
relapse.second.expressed[is.na(relapse.second.expressed)] = 0 | |
expressed_genes = relapse.first.expressed > 1 & relapse.second.expressed > 1 | |
expressed_gene_fpkm = log2(gene_fpkm[expressed_genes,] + 1) | |
# Normalize across batches | |
m = matrix(nrow=18, ncol=1) | |
batch = data.frame(m, row.names = relapse) | |
batch[relapse.first, 1] = 1 | |
batch[relapse.second, 1] = 2 | |
batch.colnames = c('batch') | |
modcombat = model.matrix(~1, data=batch) | |
combat_edata = ComBat(dat=expressed_gene_fpkm, batch=batch$m, mod=modcombat, par.prior=TRUE, prior.plots=FALSE) | |
save(combat_edata, file = 'corrected_expr.rda') | |
# ENSG IDs for DOI: 10.1016/j.ccell.2016.12.005 | |
neuro.marker.genes = data.frame( | |
ensembl = c( | |
'ENSG00000139352', | |
'ENSG00000162992', | |
'ENSG00000102003', | |
'ENSG00000173404', | |
'ENSG00000100604', | |
'ENSG00000089199', | |
'ENSG00000171951', | |
'ENSG00000134443', | |
'ENSG00000149294', | |
'ENSG00000154277', | |
'ENSG00000110680', | |
'ENSG00000175868' | |
), | |
HGNC = c( | |
'ASCL1', | |
'NEUROD1', | |
'SYP', | |
'INSM1', | |
'CHGA', | |
'CHGB', | |
'SCG2', | |
'GRP', | |
'NCAM1', | |
'UCHL1', | |
'CALCA', | |
'CALCB' | |
) | |
) | |
neuro.marker.genes.filtered = neuro.marker.genes[neuro.marker.genes$ensembl %in% rownames(combat_edata),] | |
neuro.marker.fpkm = combat_edata[neuro.marker.genes.filtered$ensembl,] | |
colnames(neuro.marker.fpkm) = gsub('^FPKM.', '', colnames(neuro.marker.fpkm)) | |
rownames(neuro.marker.fpkm) = neuro.marker.genes.filtered$HGNC | |
intersect <- function(m1, s1, m2, s2, prop1, prop2){ | |
B <- (m1/s1^2 - m2/s2^2) | |
A <- 0.5*(1/s2^2 - 1/s1^2) | |
C <- 0.5*(m2^2/s2^2 - m1^2/s1^2) - log((s1/s2)*(prop2/prop1)) | |
(-B + c(1,-1)*sqrt(B^2 - 4*A*C))/(2*A) | |
} | |
m.apc = normalmixEM(combat_edata['ENSG00000134982', ], k = 2) # APC | |
m.myc = normalmixEM(combat_edata['ENSG00000136997', ], k = 2) # MYC | |
i.apc = intersect(m.apc$mu[1], m.apc$sigma[1], m.apc$mu[2], m.apc$sigma[2], m.apc$lambda[1], m.apc$lambda[2]) | |
i.myc = intersect(m.myc$mu[1], m.myc$sigma[1], m.myc$mu[2], m.myc$sigma[2], m.myc$lambda[1], m.myc$lambda[2]) | |
x.apc = ifelse(m.apc$x > i.apc[2], 'High', 'Low') | |
x.myc = ifelse(m.myc$x > i.myc[2], 'High', 'Low') | |
annotation_col = data.frame( | |
MycStatus = x.myc, | |
ApcStatus = x.apc | |
) | |
rownames(annotation_col) = colnames(neuro.marker.fpkm) | |
pheatmap(neuro.marker.fpkm, annotation_col = annotation_col, cluster_rows = FALSE, cutree_cols = 3) |
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