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R version 4.0.4 (2021-02-15) -- "Lost Library Book"
Copyright (C) 2021 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
> # Author: Almut Luetge, Anthony Sonrel
>
> ## Instructions:
> # Modify here the steps of processing that will be applied to the raw data
>
> ### ---------- Normalization and preprocessing ----------- ####
>
> args <- (commandArgs(trailingOnly = TRUE))
> for (i in seq_len(length(args))) {
+ eval(parse(text = args[[i]]))
+ }
>
> set.seed(1234)
>
> ## Input Arguments
> # counts in mtx format
> print(count_file)
[1] "data/some_data_test/counts_some_data_test.mtx.gz"
> # cells metadata in json format
> print(meta_file)
[1] "data/some_data_test/meta_some_data_test.json"
> # dataset name used to naming the outputs
> print(dataset_name)
[1] "some_data_test"
> # output path
> print(out_path)
[1] "data/some_data_test_process"
>
> ## Libraries
> library(utils)
> library(SingleCellExperiment)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats
Attaching package: ‘MatrixGenerics’
The following objects are masked from ‘package:matrixStats’:
colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
colWeightedMeans, colWeightedMedians, colWeightedSds,
colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
rowWeightedSds, rowWeightedVars
Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Loading required package: S4Vectors
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Attaching package: ‘Biobase’
The following object is masked from ‘package:MatrixGenerics’:
rowMedians
The following objects are masked from ‘package:matrixStats’:
anyMissing, rowMedians
> library(scater)
Loading required package: ggplot2
> library(scran)
> library(R.utils)
Loading required package: R.oo
Loading required package: R.methodsS3
R.methodsS3 v1.8.1 (2020-08-26 16:20:06 UTC) successfully loaded. See ?R.methodsS3 for help.
R.oo v1.24.0 (2020-08-26 16:11:58 UTC) successfully loaded. See ?R.oo for help.
Attaching package: ‘R.oo’
The following object is masked from ‘package:R.methodsS3’:
throw
The following object is masked from ‘package:SummarizedExperiment’:
trim
The following object is masked from ‘package:GenomicRanges’:
trim
The following object is masked from ‘package:IRanges’:
trim
The following objects are masked from ‘package:methods’:
getClasses, getMethods
The following objects are masked from ‘package:base’:
attach, detach, load, save
R.utils v2.10.1 (2020-08-26 22:50:31 UTC) successfully loaded. See ?R.utils for help.
Attaching package: ‘R.utils’
The following object is masked from ‘package:utils’:
timestamp
The following objects are masked from ‘package:base’:
cat, commandArgs, getOption, inherits, isOpen, nullfile, parse,
warnings
> library(Matrix)
Attaching package: ‘Matrix’
The following object is masked from ‘package:S4Vectors’:
expand
> library(jsonlite)
Attaching package: ‘jsonlite’
The following object is masked from ‘package:R.utils’:
validate
> library(uwot)
>
> ## Load data
> counts <- readMM(count_file)
> meta <- fromJSON(meta_file)
> colnames(counts) <- meta$cell_id
>
> ## Generate sce
> sce <- SingleCellExperiment(list(counts = counts),
+ colData = DataFrame(meta))
>
>
> ## Normalize
> clusters <- quickCluster(sce, use.ranks=FALSE)
Warning in (function (A, nv = 5, nu = nv, maxit = 1000, work = nv + 7, reorth = TRUE, :
You're computing too large a percentage of total singular values, use a standard svd instead.
Warning message:
In check_numbers(k = k, nu = nu, nv = nv, limit = min(dim(x)) - :
more singular values/vectors requested than available
> table(clusters)
clusters
1
200
> sce <- computeSumFactors(sce, min.mean=0.1, cluster=clusters)
> sce <- logNormCounts(sce)
>
>
> ## Select highly variable genes
> dec <- modelGeneVar(sce)
> dec <- dec[order(dec$bio, decreasing = TRUE),]
> hvg_sig <- getTopHVGs(dec, fdr.threshold=0.05)
> hvg <- getTopHVGs(dec, var.threshold = 0)
> length(hvg)
[1] 45
> length(hvg_sig)
[1] 7
> hvg_tab <- data.frame("all" = hvg,
+ "sig" = hvg %in% hvg_sig)
>
>
> ## Run dimensional reduction
> sce <- runPCA(sce, ncomponents = 20, ntop = length(hvg))
> sce <- runUMAP(sce)
>
>
> ## Save normalized counts as gziped mtx file
> mtx <- as.matrix(logcounts(sce))
> mtx <- Matrix(mtx)
> mtx <- as(mtx, "dgTMatrix")
> matrix_out <- paste0(out_path, "/norm_counts_", dataset_name, ".mtx")
> writeMM(obj = mtx, matrix_out)
NULL
> gzip(matrix_out, overwrite=TRUE)
>
>
>
> ## Save reduced Dimensions as gziped mtx
> colnames(reducedDims(sce)[["UMAP"]]) <- c("UMAP1", "UMAP2")
> red_tab <- cbind(reducedDims(sce)[["PCA"]], reducedDims(sce)[["UMAP"]])
> red_mtx <- as.matrix(red_tab)
> red_mtx <- Matrix(red_mtx)
> red_mtx <- as(red_mtx, "dgTMatrix")
> red_out <- paste0(out_path, "/dim_red_", dataset_name, ".mtx")
> writeMM(obj = red_mtx, red_out)
NULL
> gzip(red_out, overwrite=TRUE)
>
>
> ## Save highly variable genes as json
> jsonlite::write_json(hvg_tab, paste0(out_path, "/hvg_", dataset_name, ".json"),
+ matrix = "columnmajor")
>
> sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] uwot_0.1.10 jsonlite_1.7.2
[3] Matrix_1.3-2 R.utils_2.10.1
[5] R.oo_1.24.0 R.methodsS3_1.8.1
[7] scran_1.18.7 scater_1.18.6
[9] ggplot2_3.3.3 SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 Biobase_2.50.0
[13] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[15] IRanges_2.24.1 S4Vectors_0.28.1
[17] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[19] matrixStats_0.58.0
loaded via a namespace (and not attached):
[1] locfit_1.5-9.4 Rcpp_1.0.6
[3] rsvd_1.0.3 lattice_0.20-41
[5] FNN_1.1.3 assertthat_0.2.1
[7] utf8_1.2.1 RSpectra_0.16-0
[9] R6_2.5.0 bluster_1.0.0
[11] pillar_1.5.1 sparseMatrixStats_1.2.1
[13] zlibbioc_1.36.0 rlang_0.4.10
[15] irlba_2.3.3 BiocNeighbors_1.8.2
[17] statmod_1.4.35 BiocParallel_1.24.1
[19] igraph_1.2.6 RCurl_1.98-1.3
[21] munsell_0.5.0 beachmat_2.6.4
[23] DelayedArray_0.16.3 compiler_4.0.4
[25] vipor_0.4.5 BiocSingular_1.6.0
[27] pkgconfig_2.0.3 ggbeeswarm_0.6.0
[29] tidyselect_1.1.0 tibble_3.1.0
[31] gridExtra_2.3 GenomeInfoDbData_1.2.4
[33] edgeR_3.32.1 fansi_0.4.2
[35] viridisLite_0.3.0 crayon_1.4.1
[37] dplyr_1.0.5 withr_2.4.1
[39] bitops_1.0-6 grid_4.0.4
[41] gtable_0.3.0 lifecycle_1.0.0
[43] DBI_1.1.1 magrittr_2.0.1
[45] scales_1.1.1 dqrng_0.2.1
[47] scuttle_1.0.4 XVector_0.30.0
[49] viridis_0.5.1 limma_3.46.0
[51] DelayedMatrixStats_1.12.3 ellipsis_0.3.1
[53] generics_0.1.0 vctrs_0.3.7
[55] tools_4.0.4 glue_1.4.2
[57] beeswarm_0.3.1 purrr_0.3.4
[59] colorspace_2.0-0
>
> proc.time()
user system elapsed