Last updated: 2020-01-16
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Knit directory: misc/
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Reproduce the experimental data used in Van den Berge et al., 2018 for creating PBMC null datasets.
Data includes 2,638 samples and 13,713 genes.
Same steps as in https://github.com/statOmics/zinbwaveZinger/blob/master/realdata/createdata/createDataObject.Rmd.
Reading in data.
library(Seurat)
library(dplyr)
library(Matrix)
# Load the PBMC dataset
pbmc.data <- Read10X(data.dir = "~/Downloads/pbmc3k_filtered_gene_bc_matrices")
pbmc <- CreateSeuratObject(counts = pbmc.data, min.cells = 3, min.features = 200, project = "10X_PBMC")QC and selecting cells for further analysis
pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)Normalizing the data
pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)Detection of variable genes across the single cells
pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)Scaling the data and removing unwanted sources of variation
all.genes <- rownames(pbmc)
pbmc <- ScaleData(pbmc, features = all.genes, vars.to.regress = "percent.mt")Perform linear dimensional reduction
pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))Cluster the cells
pbmc <- FindNeighbors(pbmc, dims = 1:10)
pbmc <- FindClusters(pbmc, resolution = 0.5)Run Non-linear dimensional reduction (tSNE)
pbmc <- RunUMAP(pbmc, dims = 1:10)new.cluster.ids <- c("Naive CD4 T", "Memory CD4 T", "CD14+ Mono", "B", "CD8 T", "FCGR3A+ Mono",
"NK", "DC", "Platelet")
names(new.cluster.ids) <- levels(pbmc)
pbmc <- RenameIdents(pbmc, new.cluster.ids)
#DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()Create SE object
library(SingleCellExperiment)
# use raw data as input for zinbwave but keep only non filtered cells
# and most variable genes indentified by seurat
pbmc2 <- CreateSeuratObject(counts = pbmc.data, min.cells = 3, min.features = 200, project = "10X_PBMC")
pbmc2[["percent.mt"]] <- PercentageFeatureSet(pbmc2, pattern = "^MT-")
pbmc2 <- subset(pbmc2, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
keepcells = colnames(pbmc@data)
counts = pbmc2@assays$RNA[, keepcells]
# zinbwave does not want dgTMatrix as input
counts = as.matrix(counts)
counts = counts[rowSums(counts) > 0, ]
#keepcells = as.integer(pbmc@ident) %in% 1:2
#counts = counts[, keepcells]
# coldata
clusters = as.integer(pbmc@active.ident)
#clusters = clusters[clusters %in% 1:2]
cData = data.frame(seurat = clusters)
rownames(cData) = colnames(counts)
# rowdata
rData = data.frame(seuratVarGenes = rownames(counts) %in% rownames(pbmc@assays$RNA))
rownames(rData) = rownames(counts)
# create sce object
core = SingleCellExperiment(assays = list(counts = counts),
colData = cData, rowData = rData)
unloadNamespace("Seurat")
saveRDS(core, file = '~/Downloads/pbmc.rds')
saveRDS(assay(pbmc2), file = '~/Downloads/pbmc_counts.rds')Cluster ID Markers Cell Type 0 IL7R, CCR7 Naive CD4+ T 1 IL7R, S100A4 Memory CD4+ 2 CD14, LYZ CD14+ Mono 3 MS4A1 B 4 CD8A CD8+ T 5 FCGR3A, MS4A7 FCGR3A+ Mono 6 GNLY, NKG7 NK 7 FCER1A, CST3 DC 8 PPBP Platelet
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] workflowr_1.5.0 Rcpp_1.0.2 rprojroot_1.3-2 digest_0.6.21
[5] later_1.0.0 R6_2.4.0 backports_1.1.5 git2r_0.26.1
[9] magrittr_1.5 evaluate_0.14 stringi_1.4.3 rlang_0.4.0
[13] fs_1.3.1 promises_1.1.0 whisker_0.4 rmarkdown_1.16
[17] tools_3.6.1 stringr_1.4.0 glue_1.3.1 httpuv_1.5.2
[21] xfun_0.10 yaml_2.2.0 compiler_3.6.1 htmltools_0.4.0
[25] knitr_1.25