TDP-43-HA expression in unfiltered cells
Katharina Hembach
10/15/2020
Last updated: 2020-10-15
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6d7e3c3 | khembach | 2020-10-15 | TDP-43-HA expression in filtered cells |
Load packages
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(tximport)
library(scater)
library(LSD)
library(dplyr)
library(ggridges)Load data
We combine the quantification of the plasmid transcript and the endogenous TDP-43 with the CellRanger count matrix.
sce <- readRDS(file.path("output", "sce_TDP_02_quality_control.rds"))
## we only keep the four samples of the TDP-43 experiment
sample_ids <- c("TDP2wON", "TDP4wOFF", "TDP4wONa", "TDP4wONb")
sce <- sce[,colData(sce)$sample_id %in% sample_ids]
sce$sample_id <- droplevels(sce$sample_id)samples <- c("no1_Neural_cuture_d_96_TDP-43-HA_4w_DOXoff",
"no2_Neural_cuture_d_96_TDP-43-HA_2w_DOXON",
"no3_Neural_cuture_d_96_TDP-43-HA_4w_DOXONa",
"no4_Neural_cuture_d_96_TDP-43-HA_4w_DOXONb")
txi <- matrix(NA, nrow = 2)
for (i in 1:4) {
fi <- file.path("data", "Sep2020", "alevin_TDP43", samples[i],
"alevin/quants_mat.gz")
# import alevin quants
a <- tximport(fi, type="alevin")$counts
## match the alevin and CellRanger cell IDs
colnames(a) <- paste0(colnames(a), "-1.", sample_ids[i])
txi <- cbind(txi, a)
}
txi <- txi[,colnames(txi) != ""]
rownames(txi) <- c("ENSG00000120948.TARDBP-alevin", "TDP43-HA")We add the alevin counts to the CellRanger matrix.
## add two new rows to counts matrix and replace the counts for matching
## barcodes with the alevin counts
counts <- rbind(counts(sce), DelayedArray(matrix(0, nrow = 2,
ncol = ncol(counts(sce)))))
rownames(counts) <- c(rownames(sce), rownames(txi))
## match the barcodes
m <- match(colnames(txi), colnames(sce))
counts[rownames(txi),m[!is.na(m)]] <- txi[,which(!is.na(m))]
# adjust rowData
rd <- rbind(rowData(sce), data.frame(ensembl_id = c("ENSG00000120948", ""),
symbol = c("TARDBP_alevin", "TDP43-HA")))
rownames(rd) <- rownames(counts)
sce <- SingleCellExperiment(list(counts=counts),
colData = colData(sce),
rowData = rd)Identification of outlier cells
Based on the QC metrics, we now identify outlier cells:
cols <- c("sum", "detected", "subsets_Mt_percent")
log <- c(TRUE, TRUE, FALSE)
type <- c("both", "both", "higher")
drop_cols <- paste0(cols, "_drop")
for (i in seq_along(cols))
colData(sce)[[drop_cols[i]]] <- isOutlier(sce[[cols[i]]],
nmads = 3, type = type[i], log = log[i], batch = sce$sample_id)
# Overlap of outlier cells from two metrics
sapply(drop_cols, function(i)
sapply(drop_cols, function(j)
sum(sce[[i]] & sce[[j]]))) sum_drop detected_drop subsets_Mt_percent_drop
sum_drop 3644 3644 221
detected_drop 3644 7701 686
subsets_Mt_percent_drop 221 686 2849colData(sce)$discard <- rowSums(data.frame(colData(sce)[,drop_cols])) > 0
table(colData(sce)$discard)
FALSE TRUE
36281 9864 ## Plot the metrics and highlight the discarded cells
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
We decided to additionally filter the cells in the TDP experiment samples. We use the same cutoffs as for the 96 days old neural cultures from the first experiment. We also remove the cell population with low number of UMIs and detected genes from the old neural cultures (223 days).
## filter the cells with less than 5000 UMIs in the TDP experiment samples
tdp_samples <- c("TDP2wON", "TDP4wOFF", "TDP4wONa", "TDP4wONb")
colData(sce)$manual_discard_sum <- colData(sce)$sum < 5000 &
colData(sce)$sample_id %in% tdp_samples
## filter the cells with less than 2500 detected genes
colData(sce)$manual_discard_detected <- colData(sce)$detected < 2500 &
colData(sce)$sample_id %in% tdp_samples
## day 223
colData(sce)$manual_discard_sum <- colData(sce)$manual_discard_sum |
colData(sce)$sum < 2000 &
colData(sce)$sample_id %in% c("NC223a", "NC223b")
colData(sce)$manual_discard_detected <- colData(sce)$manual_discard_detected |
colData(sce)$detected < 1500 &
colData(sce)$sample_id %in% c("NC223a", "NC223b")
## highlight all manually discarded cells
colData(sce)$manual_discard <- colData(sce)$manual_discard_sum |
colData(sce)$manual_discard_detected
plotColData(sce, x = "sample_id", y = "sum", colour_by = "manual_discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "detected", colour_by = "manual_discard") +
scale_y_log10()
## highlight all discarded cells
colData(sce)$discard <- colData(sce)$manual_discard |
colData(sce)$discard
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
TDP-43 expression per cell.
gene_ids <- c("ENSG00000120948.TARDBP", "ENSG00000120948.TARDBP-alevin", "TDP43-HA")
plotExpression(sce, gene_ids,
x = "sample_id", exprs_values = "counts",
colour = "discard")
plotExpression(sce, gene_ids,
x = "discard", exprs_values = "counts",
colour = "sample_id")
Ridge plot
Do the filtered cells express TDP-43 and TDP-43-HA?
df <- colData(sce) %>% as.data.frame() %>%
dplyr::select(sample_id, discard, detected, sum, subsets_Mt_detected,
discard) %>%
dplyr::mutate(TARDBP = as.vector(counts(sce["ENSG00000120948.TARDBP"])),
TARDBP_alevin = as.vector(counts(sce["ENSG00000120948.TARDBP-alevin"])),
TDP43_HA = as.vector(counts(sce["TDP43-HA"])))
for (g in c("TARDBP", "TARDBP_alevin", "TDP43_HA")){
cat("#### ", g, "\n")
p <- df %>%
ggplot(aes(x = get(g), y = sample_id, fill = discard)) +
geom_density_ridges(panel_scaling = FALSE, show.legend = TRUE,
alpha = 0.5, color = "white", scale = 0.95,
rel_min_height = 0.01) +
# facet_wrap(~group_id, nrow = 1) +
theme_ridges(center_axis_labels = TRUE) +
scale_x_continuous(expand = c(0, 0)) +
xlab(g) + ggtitle("all cells")
print(p)
## number of cells with gene count > 0
table(df$sample_id, df[,g] > 0)
p <- df %>% dplyr::filter(get(g) > 0) %>%
ggplot(aes(x = get(g), y = sample_id, fill = discard)) +
geom_density_ridges(panel_scaling = FALSE, show.legend = TRUE,
alpha = 0.5, color = "white", scale = 0.95,
rel_min_height = 0.01) +
# facet_wrap(~group_id, nrow = 1) +
theme_ridges(center_axis_labels = TRUE) +
scale_x_continuous(expand = c(0, 0)) +
xlab(g) + ggtitle(paste0("cells with ", g, " count > 0"))
print(p)
cat("\n\n")
}TARDBP
TARDBP_alevin
TDP43_HA
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/lib/libRlapack.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=en_US.UTF-8
[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] HDF5Array_1.16.1 rhdf5_2.32.2
[3] ggridges_0.5.2 dplyr_1.0.2
[5] LSD_4.1-0 scater_1.16.2
[7] tximport_1.16.1 SingleCellExperiment_1.10.1
[9] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[11] matrixStats_0.56.0 Biobase_2.48.0
[13] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[15] IRanges_2.22.2 S4Vectors_0.26.1
[17] BiocGenerics_0.34.0 Seurat_3.1.5
[19] ggplot2_3.3.2 cowplot_1.0.0
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
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