###Assess complexity under varying parameters
###NB: The paths are relative to the root of the scphaser git repo
###
###SYNOPSIS
###library('devtools')
###devtools::load_all()
###source("ignore/code/analysis/mousehybrid/ncells2ngenes.downsample_vars.R")
##Params
sys = 'rs13'
sys = 'dna'
##*###
##Dirs
##*###
if(sys == 'dna'){
cloud.dir = '/mnt/kauffman/edsgard/cloud/btsync/work/rspd'
}
if(sys == 'rs13'){
cloud.dir = '/Volumes/Data/cloud/btsync/work/rspd'
}
ac.dir = file.path(cloud.dir, 'projects/scphaser/nogit/data/mousehybrid/all_snps/downsampled')
##OUT
out_rds_dir = '../nogit/data/mousehybrid/all_snps/downsampled'
out_pdf_dir = './ignore/res/mousehybrid/all_snps/downsampled/perf/pdf'
##*###
##Files
##*###
##IN
ref.counts.rds = file.path(ac.dir, paste('c57', '.counts.rds', sep = ''))
alt.counts.rds = file.path(ac.dir, paste('cast', '.counts.rds', sep = ''))
snp.annot.rds = file.path(ac.dir, 'snp.annot.rds')
##OUT
filt_acset_rds = file.path(out_rds_dir, 'acset_filt.rds')
ncells2ngenes_rds = file.path(out_rds_dir, 'ncells2ngenes.replace_no.rds')
##Libs
source('./ignore/code/analysis/performance.R')
library('BiocParallel')
library('dplyr')
library('tidyr')
main <- function(){
##Read data
altcount = readRDS(alt.counts.rds)
refcount = readRDS(ref.counts.rds)
featdata = readRDS(snp.annot.rds)
##create acset
acset = new_acset(featdata, refcount, altcount)
nrow(featdata) #314,036
##create acset
lapply(acset, dim) #314,036 x 336
length(unique(acset[['featdata']][, 'feat'])) #17,271
feat2nvars = table(acset[['featdata']][, 'feat'])
table(feat2nvars) #
##Filter vars with 0 counts
acset = filter_zerorow(acset)
lapply(acset, dim) #313,255
##Call gt
min_acount = 3
fc = 3 #75/25
acset = call_gt(acset, min_acount, fc)
##Filter variants on number of cells where ASE towards the same allele
alpha = 0.1
mono.ase = 0.1
if(!(mono.ase == 0)){
acset = filter_homovars(acset, alpha = alpha, mono.ase = mono.ase)
}
lapply(acset, dim) #289,481 x 336
##Filter variants on n.cells monoallelic and feats with < 2 j.vars
nmincells = 5
nminvar = 2
acset = filter_acset(acset, nmincells, nminvar)
lapply(acset, dim) #8,870 x 336
length(unique(acset[['featdata']][, 'feat'])) #2,479
##Dump
dir.create(dirname(filt_acset_rds), recursive = TRUE)
saveRDS(acset, file = filt_acset_rds)
##*###
##Randomly sample n cells
##*###
acset = readRDS(filt_acset_rds)
##permutation iterations
npermiter = 10 #10
perm_iter = 1:npermiter
##number of cells
ncells = sort(c(seq(25, 336, 25), 163, 336))
##specify paramset as all possible combinations of the params
paramset = expand.grid(perm_iter, ncells, stringsAsFactors = FALSE)
colnames(paramset) = c('perm_iter', 'ncells')
##parallelization
ncores = 80
bp_param = BiocParallel::MulticoreParam(workers = ncores)
##Filter vars and feats
nparamset = nrow(paramset)
acset_list = BiocParallel::bplapply(1:nparamset, filter_acset_par, BPPARAM = bp_param, paramset = paramset, acset = acset)
##status: sub (15.24 -> 15.25, 80 cores)
##get number of genes
ngenes = unlist(lapply(acset_list, function(j.acset){length(unique(j.acset[['featdata']][, 'feat']))}))
ncells2ngenes = cbind(paramset, ngenes)
##get number of variants
nvars = unlist(lapply(acset_list, function(j.acset){nrow(j.acset[['featdata']])}))
ncells2ngenes = cbind(ncells2ngenes, nvars)
##get fraction of genes
n.bg.genes = 20268 ##see log.sh
frac.genes = ncells2ngenes[, 'ngenes'] / n.bg.genes
ncells2ngenes = cbind(ncells2ngenes, frac.genes)
##Dump
saveRDS(ncells2ngenes, file = ncells2ngenes_rds)
##status: fin
ncells2ngenes[which(ncells2ngenes[, 'ncells'] == 163), ]
summary(ncells2ngenes[which(ncells2ngenes[, 'ncells'] == 163), 'ngenes'])
##median: 1,546
##*###
##Plot
##*###
ncells2ngenes = readRDS(ncells2ngenes_rds)
library('ggplot2')
gg = ggplot(ncells2ngenes, aes_string(x = 'ncells', y = 'ngenes'))
gg = gg + geom_line(stat = 'summary', fun.y = 'mean')
gg = gg + geom_errorbar(stat = 'summary', fun.data = mean_se) #width = errbar.w
##tick breaks
##gg = gg + coord_cartesian(ylim = c(800, 3050))
##gg = gg + scale_y_continuous(breaks = seq(1000, 3000, 500))
gg = gg + scale_x_continuous(breaks = c(seq(50, 336, 50), 336))
##Background
gg = gg + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) + theme(panel.background = element_blank())
gg = gg + theme(axis.text = element_text(colour="black"), axis.ticks = element_line(colour = 'black'))
gg = gg + xlab('Number of sequenced cells')
gg = gg + ylab('Number of phasable genes')
j.pdf = file.path(out_pdf_dir, 'ncells2ngenes.replace_no.pdf')
dir.create(dirname(j.pdf), recursive = TRUE)
pdf(j.pdf)
plot(gg)
dev.off()
gg = ggplot(ncells2ngenes, aes_string(x = 'ncells', y = 'frac.genes'))
gg = gg + geom_line(stat = 'summary', fun.y = 'mean')
gg = gg + geom_errorbar(stat = 'summary', fun.data = mean_se) #width = errbar.w
##tick breaks
##gg = gg + coord_cartesian(ylim = c(800, 3050))
##gg = gg + scale_y_continuous(breaks = seq(1000, 3000, 500))
gg = gg + scale_x_continuous(breaks = c(seq(50, 336, 50), 336))
##Background
gg = gg + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) + theme(panel.background = element_blank())
gg = gg + theme(axis.text = element_text(colour="black"), axis.ticks = element_line(colour = 'black'))
gg = gg + xlab('Number of sequenced cells')
gg = gg + ylab('Number of phasable genes')
gg = gg + theme(axis.line.x = element_line(color="black", size = 0.5), axis.line.y = element_line(color="black", size = 0.5))
j.pdf = file.path(out_pdf_dir, 'ncells2fracgenes.replace_no.pdf')
dir.create(dirname(j.pdf), recursive = TRUE)
pdf(j.pdf)
plot(gg)
dev.off()
}
filter_acset_par <- function(j.param, paramset, acset, nmincells = 5, nminvar = 2){
j_ncells = paramset[j.param, 'ncells']
##subset cells
phenodata = acset[['phenodata']]
samples = phenodata[, 'sample']
j_samples = sample(samples, j_ncells, replace = FALSE)
acset_filt = subset_cols(acset, j_samples)
##filter vars and genes
acset_filt = filter_acset(acset_filt, nmincells, nminvar)
return(acset_filt)
}
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