Load libraries
library(SingleCellExperiment)
library(scran)
library(dplyr)
library(batchelor)
library(scater)
library(BiocSingular)
Initial Settings
# Metadata info
WT_sample <- c(1,3,4)
Input_samples <- WT_sample
cond = rep('WT', 3)
# Input parameters
cluster_num <- 15
cut_off_ptprc <- 1
cut_off_Epcam <- 4
Setting up the data
all.sce <- list(
'sce.MS001' = list(),
'sce.MS003' = list(),
'sce.MS004' = list()
)
# Dataset
for (n in 1:length(all.sce)) {
all.sce[[n]] <-
read.table(
paste(
'./aomdss/rawdata_AOMDSS/counts/',
dir(path = './aomdss/rawdata_AOMDSS/counts/', pattern = 'TranscriptCounts')[Input_samples[n]],
sep = "/"
),
header = TRUE,
sep = "\t",
stringsAsFactors = FALSE
)
row.names(all.sce[[n]]) <- all.sce[[n]][, 1]
all.sce[[n]] <- as.matrix(all.sce[[n]][, -1])
colnames(all.sce[[n]]) <-
paste(sub("_counts_matrix", "", names(all.sce)[n]), colnames(all.sce[[n]]), sep = "_")
# Extract ERCC spike from counts_matrix
MS00X_spike_counts <-
all.sce[[n]][grep('ERCC-', rownames(all.sce[[n]])),]
MS00X_spike_se <-
SummarizedExperiment(list(counts = MS00X_spike_counts))
all.sce[[n]] <-
all.sce[[n]][-grep('ERCC-', rownames(all.sce[[n]])),]
# Metadata
MS00X_cell_metadata <-
data.frame(sample = c(rep(
sub("_counts_matrix", "", names(all.sce)[n]), ncol(all.sce[[n]])
)), condition = c(rep(cond[n], (ncol(
all.sce[[n]]
)))))
# Single cell experiment object
all.sce[[n]] <-
SingleCellExperiment(assays = list(counts = all.sce[[n]]),
colData = MS00X_cell_metadata)
altExp(all.sce[[n]], "ERCC") <- MS00X_spike_se
}
Quality control and normalization
stats <-
high.mito <-
high.Ptprc <-
high.Epcam <-
high.ercc <-
low.lib <-
low.nexprs <- list()
for (n in names(all.sce)) {
current <- all.sce[[n]]
is.mito <- grep("_chrM", rownames(current))
is.Ptprc <- grep('Ptprc__*', rownames(current))
is.Epcam <- grep('Epcam__*', rownames(current))
stats[[n]] <-
perCellQCMetrics(current,
subsets = list(
Mito = is.mito,
Ptprc = is.Ptprc,
Epcam = is.Epcam
))
high.mito[[n]] <-
isOutlier(stats[[n]]$subsets_Mito_percent, type = "higher")
high.Ptprc[[n]] <- stats[[n]]$subsets_Ptprc_sum > cut_off_ptprc
high.Epcam[[n]] <- stats[[n]]$subsets_Epcam_sum > cut_off_Epcam
high.ercc[[n]] <-
isOutlier(stats[[n]]$altexps_ERCC_percent, type = "higher")
low.lib[[n]] <-
isOutlier(stats[[n]]$sum, log = TRUE, type = "lower") # Log transformation
low.nexprs[[n]] <-
isOutlier(stats[[n]]$detected, log = TRUE, type = "lower") # Log transformation
all.sce[[n]] <-
current[, !(high.mito[[n]] | high.Ptprc[[n]] | high.Epcam[[n]])]
all.sce[[n]] <-
addPerCellQC(all.sce[[n]],
subsets = list(
Mito = is.mito,
Ptprc = is.Ptprc,
Epcam = is.Epcam
))
}
# Normalization
all.sce <- lapply(all.sce, logNormCounts, pseudo_count = 1)
Integrating datasets
# Create common features
# Create common features
common_features <- Reduce(intersect, (lapply(all.sce, rownames)))
common_features_ercc <-
Reduce(intersect, (lapply(lapply(all.sce, altExp), rownames)))
# Subsetting SingleCellExperiment
all.sce <- lapply(all.sce, function(x)
x[common_features])
for (sce_names in names(all.sce)) {
altExp(all.sce[[sce_names]]) <- altExp(all.sce[[sce_names]])[common_features_ercc]
}
combined_sce = cbind(all.sce$sce.MS001,
all.sce$sce.MS003,
all.sce$sce.MS004)
# Feature selection
dec.block <- modelGeneVar(combined_sce, block = combined_sce$sample)
chosen.hvgs.block <- dec.block$bio > 0
# Batch correction
set.seed(01001001)
sce.data <- correctExperiments(
combined_sce,
batch = combined_sce$sample,
subset.row = chosen.hvgs.block,
correct.all = TRUE,
PARAM = FastMnnParam(
d = 50,
k = 20,
BSPARAM = RandomParam(deferred = TRUE)
)
)
Clustering
graph_snn <-
buildSNNGraph(sce.data,
k = cluster_num,
use.dimred = 'corrected')
clusters.mnn <-
igraph::cluster_walktrap(graph_snn)$membership
colLabels(sce.data) <-
factor(clusters.mnn)
set.seed(0010101010)
sce.data <- runTSNE(sce.data, dimred = "corrected")
plotReducedDim(sce.data, "TSNE", colour_by = "label")
Marker gene
markers <-
findMarkers(sce.data, direction = "up", lfc = 1)
markers_all <-
findMarkers(sce.data, direction = "any", lfc = 1)
Write results
save(sce.data,file='./aomdss/out_AOMDSS/WT/sce.data.RData')
print(sessioninfo::session_info())
## - Session info ---------------------------------------------------------------
## setting value
## version R version 4.0.4 (2021-02-15)
## os Windows 10 x64
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate German_Germany.1252
## ctype German_Germany.1252
## tz Europe/Berlin
## date 2024-01-23
##
## - Packages -------------------------------------------------------------------
## package * version date lib source
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## batchelor * 1.6.2 2020-11-26 [1] Bioconductor
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## Biobase * 2.50.0 2020-10-27 [1] Bioconductor
## BiocGenerics * 0.36.0 2020-10-27 [1] Bioconductor
## BiocNeighbors 1.8.2 2020-12-07 [1] Bioconductor
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