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Neuroblastroma_codes
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Neuroblastroma_codes
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[root@owl a-h]# cd cluster
[root@owl cluster]# ls
ce_all_c1.rda ceCancerCells_afterClustExp.rda ceHMYCN2.rda clusterAll.R Rplots.pdf seAll.rda seHMYCN.rda
ce_all_c2.rda ceCancerCells.rda ceHMYCN3.rda clusterCancerCells.R seAll_allgenes.rda seCancerCells_afterZINB.rda
ce_all_c3.rda ceHMYCN1.rda clusterAll_lessFiltering.R cluster_hmycn.R se_all_K50.rda seCancerCells.rda
[root@owl cluster]# head -2000 cluster_hmycn.R
#!/usr/bin/env Rscript
library(RColorBrewer)
library(BiocParallel)
library(doParallel)
library(Matrix)
library(ggplot2)
library(gplots)
library(matrixStats)
library(zinbwave)
library(data.table)
library(SummarizedExperiment)
library(clusterExperiment)
library(dplyr)
set.seed(20) #set seed, just in case
NCORES = 7
mysystem = Sys.info()[['sysname']]
if (mysystem == 'Darwin'){
registerDoParallel(NCORES)
register(DoparParam())
}else if (mysystem == 'Linux'){
register(bpstart(MulticoreParam(workers=NCORES)))
}else{
print('Please change this to allow parallel computing')
register(SerialParam())
}
mypalette = colorRampPalette(rev(brewer.pal(11, "RdYlBu")))(51)
samples.names = c("WT", "X", "Y", "A", "B", "C", "D", "E", "F", "G", "H")
pal = brewer.pal(length(samples.names), "Set3")
names(pal) = samples.names
load('../../../data/rawCounts.rda')
se = se[, colData(se)$hmycn == T]
se = se[, !colData(se)$samples %in% c('WT', 'X', 'Y')]
################
# Filter cells
################
# Coverage
cell.tots = Matrix::colSums(assay(se))
names(cell.tots) = NULL
par(lty = 0)
barplot(cell.tots, col = pal[colData(se)$samples], las = 2, space = 0,
main = 'Coverage', xlab = 'Cells', ylab = 'Number of reads per cell')
legend(x = 'bottomright', legend = names(pal), cex = .5, fill = pal, title = 'samples')
par(lty = 1)
plot(density(Matrix::colSums(assay(se))), main='Distribution of coverage')
dim(se)
coverage = Matrix::colSums(assay(se))
filt = coverage < 10000
se = se[, filt]
dim(se)
# Percentage of zeros
par(lty = 0)
zeroprop = Matrix::colMeans(assay(se) == 0)
names(zeroprop) = NULL
barplot(zeroprop, col = pal[colData(se)$samples], las = 2, space=0, main='Percentage of zeros',
xlab = 'Cells', ylab = 'Percentage of genes with zero counts')
legend(x = 'bottomright', legend = names(pal), cex=.5, fill = pal, title = 'samples')
par(lty = 1)
plot(density(zeroprop), main='Distribution of percentage of zeros')
abline(v = 0.98)
abline(v = 0.82)
dim(se)
filt = zeroprop > 0.82 & zeroprop < 0.98
se = se[, filt]
dim(se)
################
# Filter genes
################
genecov = Matrix::rowSums(assay(se))
se = se[genecov > 0, ]
dim(se)
filt = Matrix::rowSums(assay(se) > 1) > 5
se = se[filt, ]
dim(se)
genecov = Matrix::rowSums(assay(se))
par(mfrow = c(1,2))
plot(density(log10(1+genecov)), main='Distribution of percentage of zeros, log')
abline(v=log10(5000))
plot(density(genecov),main='Distribution of percentage of zeros')
abline(v=5000)
par(mfrow = c(1,1))
##############
# subset
##############
vars = rowVars(log1p(as.matrix(assay(se))))
names(vars) = rownames(se)
summary(vars)
vars = sort(vars, decreasing = TRUE)
dim(se)
se = se[names(vars)[1:1000], ]
dim(se)
plot(density(vars), 'Density of variance for log(counts+1)')
abline(v = vars[1000])
vars[1:10]
assay(se) = as.matrix(assay(se))
###################
# round 1
###################
print(system.time(se <- zinbDimRed(se, K = 50, epsilon = 5000)))
assays(se)$normalizedValues_hmcyn1 = assays(se)$normalizedValues
W = as.matrix(colData(se)[, grep('^W', colnames(colData(se)), value = T)])
head(W)
fit = cmdscale(dist(W), eig = TRUE, k = 2)
plot(fit$points, col = alpha(pal[colData(se)$samples], .5),
main = 'MDS of W', pch = 20)
legend(x = 'bottomright', legend = names(pal), cex=.5, fill = pal, title = 'samples')
for (x in unique(colData(se)$samples)){
mycol = ifelse(colData(se)$samples == x, pal[x], 'gray')
ord = order(mycol, decreasing = T)
plot(fit$points[ord,], col = alpha(mycol[ord], .5), pch = 20)
legend('bottomright', fill = unique(mycol),
legend = c('others', x))
}
# clustering
set.seed(3939)
seW <- SummarizedExperiment(t(W), colData = colData(se))
ce <- clusterSingle(seW, subsample = TRUE,
dimReduce = 'none',
clusterFunction = "hierarchical01",
subsampleArgs = list(resamp.num=100,
clusterFunction="kmeans",
clusterArgs=list(nstart=10),
k = 2),
clusterDArgs = list(minSize = 15, alpha = 0.8))
ce <- makeDendrogram(ce, whichCluster = "primaryCluster", dimReduce = "none")
save(ce, file = 'ceHMYCN1.rda')
clust = primaryCluster(ce)
table(clust)
table(clust, colData(se)$samples)
par(mfrow = c(1,2))
plot(fit$points, col = clust + 2, pch = 20)
legend('bottomright', fill = unique(clust+2), legend = unique(clust))
plot(fit$points, col = alpha(pal[colData(se)$samples], .2), pch = 20)
par(mfrow = c(1,1))
# DE
fq = EDASeq::betweenLaneNormalization(assay(se), which = 'full')
fq = log1p(fq)
bf = getBestFeatures(fq, clust, contrastType = 'Pairs', number = nrow(se))
bf = bf[,c('Feature', 'adj.P.Val')]
bf = bf %>% group_by(Feature) %>% summarise(adj.P.Val = min(adj.P.Val)) %>%
arrange(adj.P.Val) %>% ungroup() %>% as.data.frame()
de = bf[bf$adj.P.Val < 0.01, ]
dim(de)
plot(log(de$adj.P.Val))
de = de[1:50, ]
# visualize
normValues = assays(se)$normalizedValues
mat = normValues[de$Feature,]
breaks = setBreaks(mat, breaks = .99, makeSymmetric = T)
heatmap.2(mat, breaks = breaks,
ColSideColors = as.character(clust+2),
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
heatmap.2(mat, breaks = breaks,
ColSideColors = pal[colData(se)$samples],
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
colData(se)$hmycn1 = clust
###################
# round 2
###################
print(system.time(se <- zinbDimRed(se, K = 50, epsilon = 5000,
X = '~ hmycn1')))
colnames(colData(se))
assays(se)$normalizedValues_hmcyn2 = assays(se)$normalizedValues
W = as.matrix(colData(se)[, grep('^W', colnames(colData(se)), value = T)])
W = W[, 51:100]
head(W)
fit = cmdscale(dist(W), eig = TRUE, k = 2)
plot(fit$points, col = alpha(pal[colData(se)$samples], .5),
main = 'MDS of W', pch = 20)
legend(x = 'bottomright', legend = names(pal), cex=.5, fill = pal, title = 'samples')
for (x in unique(colData(se)$samples)){
mycol = ifelse(colData(se)$samples == x, pal[x], 'gray')
ord = order(mycol, decreasing = T)
plot(fit$points[ord,], col = alpha(mycol[ord], .5), pch = 20)
legend('bottomright', fill = unique(mycol),
legend = c('others', x))
}
# clustering
set.seed(3939)
seW <- SummarizedExperiment(t(W), colData = colData(se))
ce <- clusterSingle(seW, subsample = TRUE,
dimReduce = 'none',
clusterFunction = "hierarchical01",
subsampleArgs = list(resamp.num=100,
clusterFunction="kmeans",
clusterArgs=list(nstart=10),
k = 2),
clusterDArgs = list(minSize = 15, alpha = 0.9))
ce <- makeDendrogram(ce, whichCluster = "primaryCluster", dimReduce = "none")
save(ce, file = 'ceHMYCN2.rda')
clust = primaryCluster(ce)
table(clust)
table(clust, colData(se)$samples)
par(mfrow = c(1,2))
plot(fit$points, col = clust + 2, pch = 20)
legend('bottomright', fill = unique(clust+2), legend = unique(clust))
plot(fit$points, col = alpha(pal[colData(se)$samples], .2), pch = 20)
par(mfrow = c(1,1))
# DE
fq = EDASeq::betweenLaneNormalization(assay(se), which = 'full')
fq = log1p(fq)
bf = getBestFeatures(fq, clust, contrastType = 'Pairs', number = nrow(se))
bf = bf[,c('Feature', 'adj.P.Val')]
bf = bf %>% group_by(Feature) %>% summarise(adj.P.Val = min(adj.P.Val)) %>%
arrange(adj.P.Val) %>% ungroup() %>% as.data.frame()
de = bf[bf$adj.P.Val < 0.01, ]
dim(de)
plot(log(de$adj.P.Val))
de = de[1:25, ]
# visualize
normValues = assays(se)$normalizedValues
mat = normValues[de$Feature,]
breaks = setBreaks(mat, breaks = .99, makeSymmetric = T)
heatmap.2(mat, breaks = breaks,
ColSideColors = as.character(clust+2),
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
heatmap.2(mat, breaks = breaks,
ColSideColors = pal[colData(se)$samples],
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
colData(se)$hmycn2 = clust
###################
# round 3
###################
print(system.time(se <- zinbDimRed(se, K = 50, epsilon = 5000,
X = '~ hmycn1 + hmycn2')))
colnames(colData(se))
assays(se)$normalizedValues_hmcyn3 = assays(se)$normalizedValues
W = as.matrix(colData(se)[, grep('^W', colnames(colData(se)), value = T)])
W = W[, 101:150]
head(W,2)
fit = cmdscale(dist(W), eig = TRUE, k = 2)
plot(fit$points, col = alpha(pal[colData(se)$samples], .5),
main = 'MDS of W', pch = 20)
legend(x = 'bottomright', legend = names(pal), cex=.5, fill = pal, title = 'samples')
for (x in unique(colData(se)$samples)){
mycol = ifelse(colData(se)$samples == x, pal[x], 'gray')
ord = order(mycol, decreasing = T)
plot(fit$points[ord,], col = alpha(mycol[ord], .5), pch = 20)
legend('bottomright', fill = unique(mycol),
legend = c('others', x))
}
# clustering
set.seed(3939)
seW <- SummarizedExperiment(t(W), colData = colData(se))
ce <- clusterSingle(seW, subsample = TRUE,
dimReduce = 'none',
clusterFunction = "hierarchical01",
subsampleArgs = list(resamp.num=100,
clusterFunction="kmeans",
clusterArgs=list(nstart=10),
k = 2),
clusterDArgs = list(minSize = 15, alpha = 0.9))
ce <- makeDendrogram(ce, whichCluster = "primaryCluster", dimReduce = "none")
save(ce, file = 'ceHMYCN3.rda')
clust = primaryCluster(ce)
table(clust)
par(mfrow = c(1,2))
plot(fit$points, col = clust + 2, pch = 20)
legend('bottomright', fill = unique(clust+2), legend = unique(clust))
plot(fit$points, col = alpha(pal[colData(se)$samples], .2), pch = 20)
par(mfrow = c(1,1))
# DE
fq = EDASeq::betweenLaneNormalization(assay(se), which = 'full')
fq = log1p(fq)
bf = getBestFeatures(fq, clust, contrastType = 'Pairs', number = nrow(se))
bf = bf[,c('Feature', 'adj.P.Val')]
bf = bf %>% group_by(Feature) %>% summarise(adj.P.Val = min(adj.P.Val)) %>%
arrange(adj.P.Val) %>% ungroup() %>% as.data.frame()
de = bf[bf$adj.P.Val < 0.01, ]
dim(de)
plot(log(de$adj.P.Val))
de = de[1:50, ]
# visualize
normValues = assays(se)$normalizedValues
mat = normValues[de$Feature,]
breaks = setBreaks(mat, breaks = .99, makeSymmetric = T)
heatmap.2(mat, breaks = breaks,
ColSideColors = as.character(clust+2),
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
heatmap.2(mat, breaks = breaks,
ColSideColors = pal[colData(se)$samples],
trace = 'none', labCol = '',cexRow = 0.8,
col = mypalette, Colv = ce@dendro_samples)
colData(se)$hmycn3 = clust
##############
# save
##############
save(se, file = 'seHMYCN.rda')
[root@owl cluster]#