Mercurial > repos > artbio > gsc_scran_normalize

annotate scran-normalize.R @ 2:6864acb21714 draft

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/main/tools/gsc_scran_normalize commit a14fb3d106b647c4f1dea2c8d3ac7c1e8848b21c
author artbio
date Sun, 10 Dec 2023 00:27:45 +0000
parents fb2f1b8b0013
children cc768b0f41cf
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fb2f1b8b0013 planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit e0357f07fdabee1ec6614aca6f7b51095111e0d5
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1 options(show.error.messages = FALSE,
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2 error = function() {
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3 cat(geterrmessage(), file = stderr())
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4 q("no", 1, FALSE)
2
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5 }
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6 )
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7 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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8 warnings()
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9
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10 library(optparse)
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11 library(scran)
2
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12 library(dynamicTreeCut)
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1
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14 # Arguments
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15 option_list <- list(
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16 make_option(
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17 c("-d", "--data"),
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18 default = NA,
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19 type = "character",
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20 help = "Input file that contains count values to transform"
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21 ),
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22 make_option(
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23 "--cluster",
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24 default = FALSE,
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25 action = "store_true",
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26 type = "logical",
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27 help = "Whether to calculate the size factor per cluster or on all cell"
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28 ),
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29 make_option(
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30 c("-m", "--method"),
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31 default = "hclust",
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32 type = "character",
0
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33 help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]"
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34 ),
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35 make_option(
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36 "--size",
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37 default = 100,
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38 type = "integer",
0
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39 help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]"
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40 ),
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41 make_option(
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42 c("-o", "--out"),
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43 default = "res.tab",
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44 type = "character",
0
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45 help = "Output name [default : '%default' ]"
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46 )
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47 )
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48
1
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49 opt <- parse_args(OptionParser(option_list = option_list),
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50 args = commandArgs(trailingOnly = TRUE))
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1
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53 data <- read.table(
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54 opt$data,
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55 check.names = FALSE,
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56 header = TRUE,
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57 row.names = 1,
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58 sep = "\t"
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59 )
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61 ## Import data as a SingleCellExperiment object
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62 sce <- SingleCellExperiment(list(counts = as.matrix(data)))
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64 if (opt$cluster) {
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65 clusters <- quickCluster(sce, min.size = opt$size, method = opt$method)
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67 ## Compute sum factors
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68 sce <- computeSumFactors(sce, cluster = clusters)
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69 } else {
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71 ## Compute sum factors
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72 sce <- computeSumFactors(sce)
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73 }
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75 sce <- logNormCounts(sce)
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77 logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits = 5), check.names = FALSE)
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80 write.table(
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81 logcounts,
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82 opt$out,
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83 col.names = TRUE,
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84 row.names = FALSE,
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85 quote = FALSE,
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86 sep = "\t"
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87 )