Mercurial > repos > artbio > gsc_scran_normalize

comparison scran-normalize.R @ 0:252eded61848 draft

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit ddcf915dd9b690d7f3876e08b939adde36cbb8dd"
author artbio
date Thu, 26 Sep 2019 10:50:55 -0400
parents
children fb2f1b8b0013
comparison
equal deleted inserted replaced
-1:000000000000 0:252eded61848
1 # load packages that are provided in the conda env
2 options( show.error.messages=F,
3 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
4 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
5 warnings()
6
7 library(optparse)
8 library(scran)
9
10 #Arguments
11 option_list = list(
12 make_option(
13 c("-d", "--data"),
14 default = NA,
15 type = 'character',
16 help = "Input file that contains count values to transform"
17 ),
18 make_option(
19 c("-s", "--sep"),
20 default = '\t',
21 type = 'character',
22 help = "File separator [default : '%default' ]"
23 ),
24 make_option(
25 "--cluster",
26 default=FALSE,
27 action="store_true",
28 type = 'logical',
29 help = "Whether to calculate the size factor per cluster or on all cell"
30 ),
31 make_option(
32 c("-m", "--method"),
33 default = 'hclust',
34 type = 'character',
35 help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]"
36 ),
37 make_option(
38 "--size",
39 default = 100,
40 type = 'integer',
41 help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]"
42 ),
43 make_option(
44 c("-o", "--out"),
45 default = "res.tab",
46 type = 'character',
47 help = "Output name [default : '%default' ]"
48 )
49 )
50
51 opt = parse_args(OptionParser(option_list = option_list),
52 args = commandArgs(trailingOnly = TRUE))
53
54 if (opt$sep == "tab") {opt$sep = "\t"}
55
56 data = read.table(
57 opt$data,
58 check.names = FALSE,
59 header = TRUE,
60 row.names = 1,
61 sep = opt$sep
62 )
63
64 ## Import data as a SingleCellExperiment object
65 sce <- SingleCellExperiment(list(counts=as.matrix(data)))
66
67
68 if(opt$cluster){
69 clusters <- quickCluster(sce, min.size = opt$size, method = opt$method)
70
71 ## Compute sum factors
72 sce <- computeSumFactors(sce, cluster = clusters)
73 } else {
74
75 ## Compute sum factors
76 sce <- computeSumFactors(sce)
77 }
78
79 sce <- normalize(sce)
80
81 logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits=5), check.names = F)
82
83
84 write.table(
85 logcounts,
86 opt$out,
87 col.names = T,
88 row.names = F,
89 quote = F,
90 sep = "\t"
91 )