In csoneson/DuoClustering2018: Data, Clustering Results and Visualization Functions From Duò et al (2018)

suppressPackageStartupMessages({
  library(MultiAssayExperiment)
  library(SingleCellExperiment)
  library(scater)
  library(scran)
  library(dplyr)
  library(ggplot2)
})

Load raw data

b.cells <- read10XResults("../../data/data_raw/zheng/b_cells/filtered_matrices_mex/hg19") 
naive.cytotoxic <- read10XResults("../../data/data_raw/zheng/naive_cytotoxic/filtered_matrices_mex/hg19")
cd14.monocytes <- read10XResults("../../data/data_raw/zheng/cd14_monocytes/filtered_matrices_mex/hg19")
regulatory.t <- read10XResults("../../data/data_raw/zheng/regulatory_t/filtered_matrices_mex/hg19")
cd4.t.helper <- read10XResults("../../data/data_raw/zheng/cd4_t_helper/filtered_matrices_mex/hg19")
cd56.nk <- read10XResults("../../data/data_raw/zheng/cd56_nk/filtered_matrices_mex/hg19")
memory.t <- read10XResults("../../data/data_raw/zheng/memory_t/filtered_matrices_mex/hg19")
naive.t <- read10XResults("../../data/data_raw/zheng/naive_t/filtered_matrices_mex/hg19")

colnames(b.cells) <-  paste0("b.cells", seq_len(ncol(b.cells)))
colnames(naive.cytotoxic) <- paste0("naive.cytotoxic", seq_len(ncol(naive.cytotoxic)))
colnames(cd14.monocytes) <- paste0("cd14.monocytes", seq_len(ncol(cd14.monocytes)))
colnames(regulatory.t) <- paste0("regulatory.t", seq_len(ncol(regulatory.t)))
colnames(cd4.t.helper) <- paste0("cd4.t.helper", seq_len(ncol(cd4.t.helper)))
colnames(cd56.nk) <- paste0("cd56.nk", seq_len(ncol(cd56.nk)))
colnames(memory.t) <- paste0("memory.t", seq_len(ncol(memory.t)))
colnames(naive.t) <- paste0("naive.t", seq_len(ncol(naive.t)))

colData(b.cells)$phenoid <- "b.cells"
colData(naive.cytotoxic)$phenoid <- "naive.cytotoxic"
colData(cd14.monocytes)$phenoid <- "cd14.monocytes"
colData(regulatory.t)$phenoid <- "regulatory.t"
colData(cd4.t.helper)$phenoid <- "cd4.t.helper"
colData(cd56.nk)$phenoid <- "cd56.nk"
colData(memory.t)$phenoid <- "memory.t"
colData(naive.t)$phenoid <- "naive.t"

## Subsample
set.seed(1234)
b.cells <- b.cells[, sample(colnames(b.cells), 500, replace = FALSE)]
naive.cytotoxic <- naive.cytotoxic[, sample(colnames(naive.cytotoxic), 400, replace = FALSE)]
cd14.monocytes <- cd14.monocytes[, sample(colnames(cd14.monocytes), 600, replace = FALSE)]
regulatory.t <- regulatory.t[, sample(colnames(regulatory.t), 500, replace = FALSE)]
cd4.t.helper <- cd4.t.helper[, sample(colnames(cd4.t.helper), 400, replace = FALSE)]
cd56.nk <- cd56.nk[, sample(colnames(cd56.nk), 600, replace = FALSE)]
memory.t <- memory.t[, sample(colnames(memory.t), 500, replace = FALSE)]
naive.t <- naive.t[, sample(colnames(naive.t), 500, replace = FALSE)]

Create a SingleCellExperiment object

sce <- cbind(b.cells, naive.cytotoxic, cd14.monocytes, regulatory.t, 
             cd4.t.helper, cd56.nk, memory.t, naive.t)
counts(sce) <-  as.matrix(counts(sce))
sce <- normalise(sce, exprs_values = "counts", return_log = TRUE, 
                 return_norm_as_exprs = TRUE) ## generates logcounts(sce)

Exclude features that are not expressed

keep_features <- rowSums(counts(sce) > 0) > 0
table(keep_features)
sce <- sce[keep_features, ]
dim(sce)

Identify the remaining ERCC spike-ins.

is.spike <- grepl("^ERCC", rowData(sce)$symbol)
table(is.spike)
summary(colSums(counts(sce[is.spike, ])))

Calculate QC metrics

sce <- calculateQCMetrics(sce)

Quality control using PCA on column data

We create a PCA plot based the quality metrics for each cell, e.g., the total number of reads, the total number of features and the proportion of spike-in reads.

sce <- scater::runPCA(sce, pca_data_input = "coldata")
scater::plotPCA(sce, colour_by = "phenoid")

Filter cells

We remove cells with log-library sizes (or total features) that are more than 3 median absolute deviations (MADs) below the median log-library size (or total features).

colData(sce)$libsize.drop <- isOutlier(sce$total_counts, nmads = 3, type = "lower", log = TRUE)
ggplot(as.data.frame(colData(sce)), aes(x = total_counts)) + 
  geom_histogram(bins = 20, fill = "grey80") + xlab("Total count") + 
  ylab("Number of cells") + 
  geom_vline(xintercept = min(sce$total_counts[!sce$libsize.drop]), 
             color = "red", linetype = "dashed") + 
  theme_bw()

colData(sce)$feature.drop <- isOutlier(sce$total_features, nmads = 3, type = "lower", log = TRUE)
ggplot(as.data.frame(colData(sce)), aes(x = total_features)) + 
  geom_histogram(bins = 20, fill = "grey80") + xlab("Number of detected features") + 
  ylab("Number of cells") + 
  geom_vline(xintercept = min(sce$total_features[!sce$feature.drop]), 
             color = "red", linetype = "dashed") + 
  theme_bw()

table(libsize = sce$libsize.drop, feature = sce$feature.drop)
sce <- sce[, !(sce$libsize.drop | sce$feature.drop)]
dim(sce)

Quality control using highest expressed genes

plotQC(sce, type = "highest-expression", n = 50)

Data normalization

sce <- normalise(sce, exprs_values = "counts", return_log = TRUE, 
                 return_norm_as_exprs = TRUE)
sce <- normalise(sce, exprs_values = "counts", return_log = FALSE, 
                 return_norm_as_exprs = FALSE)

Plot the proportion of explained variances

expl_vars <- c("phenoid", "log10_total_counts", "log10_total_features", "pct_dropout",
               "pct_counts_top_200_features", "log10_counts_feature_controls",
               "pct_counts_feature_controls")
plotQC(sce, type = "explanatory-variables", variables = expl_vars)

Plot t-SNE representations

set.seed(1234)
sce <- runTSNE(sce, exprs_values = "logcounts", perplexity = 10)
plotTSNE(sce, colour_by = "phenoid")
plotTSNE(sce, colour_by = "total_features", size_by = "total_counts")

Save the normalized and cell filtered dataset

sce <- sce[!grepl("^ERCC", rownames(sce)), ]
dim(sce)
table(sce$phenoid)
saveRDS(sce, file = "../../data/sce_full/sce_full_Zhengmix8eq.rds")

Session info

date()
sessionInfo()

csoneson/DuoClustering2018 documentation built on May 18, 2024, 7:13 a.m.