Mercurial > repos > artbio > gsc_scran_normalize

comparison scran-normalize.R @ 1:fb2f1b8b0013 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit e0357f07fdabee1ec6614aca6f7b51095111e0d5
author artbio
date Sun, 04 Dec 2022 01:01:01 +0000
parents 252eded61848
children 6864acb21714
comparison
equal deleted inserted replaced
0:252eded61848 1:fb2f1b8b0013
1 # load packages that are provided in the conda env 1 # load packages that are provided in the conda env
2 options( show.error.messages=F, 2 options(show.error.messages = FALSE,
3 error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } ) 3 error = function() {
4 cat(geterrmessage(), file = stderr())
5 q("no", 1, FALSE)
6 }
7 )
4 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") 8 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
5 warnings() 9 warnings()
6 10
7 library(optparse) 11 library(optparse)
8 library(scran) 12 library(scran)
9 13
10 #Arguments 14 # Arguments
11 option_list = list( 15 option_list <- list(
12 make_option( 16 make_option(
13 c("-d", "--data"), 17 c("-d", "--data"),
14 default = NA, 18 default = NA,
15 type = 'character', 19 type = "character",
16 help = "Input file that contains count values to transform" 20 help = "Input file that contains count values to transform"
17 ), 21 ),
18 make_option( 22 make_option(
19 c("-s", "--sep"), 23 c("-s", "--sep"),
20 default = '\t', 24 default = "\t",
21 type = 'character', 25 type = "character",
22 help = "File separator [default : '%default' ]" 26 help = "File separator [default : '%default' ]"
23 ), 27 ),
24 make_option( 28 make_option(
25 "--cluster", 29 "--cluster",
26 default=FALSE, 30 default = FALSE,
27 action="store_true", 31 action = "store_true",
28 type = 'logical', 32 type = "logical",
29 help = "Whether to calculate the size factor per cluster or on all cell" 33 help = "Whether to calculate the size factor per cluster or on all cell"
30 ), 34 ),
31 make_option( 35 make_option(
32 c("-m", "--method"), 36 c("-m", "--method"),
33 default = 'hclust', 37 default = "hclust",
34 type = 'character', 38 type = "character",
35 help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]" 39 help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]"
36 ), 40 ),
37 make_option( 41 make_option(
38 "--size", 42 "--size",
39 default = 100, 43 default = 100,
40 type = 'integer', 44 type = "integer",
41 help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]" 45 help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]"
42 ), 46 ),
43 make_option( 47 make_option(
44 c("-o", "--out"), 48 c("-o", "--out"),
45 default = "res.tab", 49 default = "res.tab",
46 type = 'character', 50 type = "character",
47 help = "Output name [default : '%default' ]" 51 help = "Output name [default : '%default' ]"
48 ) 52 )
49 ) 53 )
50 54
51 opt = parse_args(OptionParser(option_list = option_list), 55 opt <- parse_args(OptionParser(option_list = option_list),
52 args = commandArgs(trailingOnly = TRUE)) 56 args = commandArgs(trailingOnly = TRUE))
53 57
54 if (opt$sep == "tab") {opt$sep = "\t"} 58 if (opt$sep == "tab") {
59 opt$sep <- "\t"
60 }
55 61
56 data = read.table( 62 data <- read.table(
57 opt$data, 63 opt$data,
58 check.names = FALSE, 64 check.names = FALSE,
59 header = TRUE, 65 header = TRUE,
60 row.names = 1, 66 row.names = 1,
61 sep = opt$sep 67 sep = opt$sep
62 ) 68 )
63 69
64 ## Import data as a SingleCellExperiment object 70 ## Import data as a SingleCellExperiment object
65 sce <- SingleCellExperiment(list(counts=as.matrix(data))) 71 sce <- SingleCellExperiment(list(counts = as.matrix(data)))
66 72
67 73 if (opt$cluster) {
68 if(opt$cluster){
69 clusters <- quickCluster(sce, min.size = opt$size, method = opt$method) 74 clusters <- quickCluster(sce, min.size = opt$size, method = opt$method)
70 75
71 ## Compute sum factors 76 ## Compute sum factors
72 sce <- computeSumFactors(sce, cluster = clusters) 77 sce <- computeSumFactors(sce, cluster = clusters)
73 } else { 78 } else {
76 sce <- computeSumFactors(sce) 81 sce <- computeSumFactors(sce)
77 } 82 }
78 83
79 sce <- normalize(sce) 84 sce <- normalize(sce)
80 85
81 logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits=5), check.names = F) 86 logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits = 5), check.names = FALSE)
82 87
83 88
84 write.table( 89 write.table(
85 logcounts, 90 logcounts,
86 opt$out, 91 opt$out,
87 col.names = T, 92 col.names = TRUE,
88 row.names = F, 93 row.names = FALSE,
89 quote = F, 94 quote = FALSE,
90 sep = "\t" 95 sep = "\t"
91 ) 96 )