Plasmid expression
Katharina Hembach
10/14/2020
Last updated: 2020-11-11
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 09364dd | khembach | 2020-11-11 | use corrected alevin counts and fix cell barcode matching |
| html | 4cda302 | khembach | 2020-10-14 | Build site. |
| Rmd | 10a7762 | khembach | 2020-10-14 | include alevin quants for plasmid |
Load packages
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(tximport)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_03_filtering.rds"))
## we only keep the four samples of the TDP-43 experiment
sample_ids <- c("TDP4wOFF", "TDP2wON", "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))]
so <- CreateSeuratObject(
counts = counts,
meta.data = data.frame(colData(sce)),
project = "TDP_experiment")Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')Normalization
# split by sample
cells_by_sample <- split(colnames(sce), sce$sample_id)
so <- lapply(cells_by_sample, function(i) subset(so, cells = i))
## log normalize the data using a scaling factor of 10000
so <- lapply(so, NormalizeData, verbose = FALSE, scale.factor = 10000,
normalization.method = "LogNormalize")We merge the normalized and data of the six samples into a combined Seurat object and compute variable features.
so_list <- so
## merge the individial Seurat objects and conserve the normalized and scaled data
so <- merge(so[[1]], y = so[2:length(so)], project = "TDP_experiment",
merge.data = TRUE)so <- FindVariableFeatures(so, nfeatures = 2000,
selection.method = "vst", verbose = FALSE)
so <- ScaleData(so, verbose = FALSE, vars.to.regress = c("sum",
"subsets_Mt_percent"))Dimension reduction
We perform dimension reduction with t-SNE and UMAP based on PCA results.
so <- RunPCA(so, npcs = 30, verbose = FALSE)
so <- RunTSNE(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, do.fast = TRUE, verbose = FALSE)
so <- RunUMAP(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, verbose = FALSE)Plot PCA results
# top genes that are associated with the first two PCs
VizDimLoadings(so, dims = 1:2, reduction = "pca")
| Version | Author | Date |
|---|---|---|
| 4cda302 | khembach | 2020-10-14 |
## PCA plot
DimPlot(so, reduction = "pca", group.by = "sample_id")
| Version | Author | Date |
|---|---|---|
| 4cda302 | khembach | 2020-10-14 |
# elbow plot with the ranking of PCs based on the % of variance explained
ElbowPlot(so, ndims = 30)
| Version | Author | Date |
|---|---|---|
| 4cda302 | khembach | 2020-10-14 |
Clustering
We cluster the cells using the reduced PCA dimensions.
so <- FindNeighbors(so, reduction = "pca", dims = seq_len(20), verbose = FALSE)
for (res in c(0.2, 0.4, 0.8, 1))
so <- FindClusters(so, resolution = res, random.seed = 1, verbose = FALSE)Dimension reduction plots
We plot the dimension reduction (DR) and color by sample, group and cluster ID
thm <- theme(aspect.ratio = 1, legend.position = "none")
ps <- lapply(c("sample_id", "ident"), function(u) {
p1 <- DimPlot(so, reduction = "tsne", group.by = u) + thm
p2 <- DimPlot(so, reduction = "umap", group.by = u)
lgd <- get_legend(p2)
p2 <- p2 + thm
list(p1, p2, lgd)
plot_grid(p1, p2, lgd, nrow = 1,
rel_widths = c(1, 1, 0.5))
})
plot_grid(plotlist = ps, ncol = 1)
| Version | Author | Date |
|---|---|---|
| 4cda302 | khembach | 2020-10-14 |
QC on DR plots
cs <- sample(colnames(so), 1e4) ## subsample cells
.plot_features <- function(so, dr, id) {
FeaturePlot(so, cells = cs, features = id, reduction = dr, pt.size = 0.4,
cols = c("grey", "blue")) +
guides(col = guide_colourbar()) +
theme_void() + theme(aspect.ratio = 1)
}
ids <- c("sum", "detected", "subsets_Mt_percent", "ENSG00000120948.TARDBP",
"ENSG00000120948.TARDBP-alevin", "TDP43-HA")
for (id in ids) {
cat("### ", id, "\n")
p1 <- .plot_features(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none") + ggtitle("tSNE")
p2 <- .plot_features(so, "umap", id) + theme(legend.position = "none") +
ggtitle("UMAP")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
print(p)
cat("\n\n")
}
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_TDP_05_plasmid_expression.rds"))
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] tximport_1.16.1 SingleCellExperiment_1.10.1
[5] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[7] matrixStats_0.56.0 Biobase_2.48.0
[9] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[11] IRanges_2.22.2 S4Vectors_0.26.1
[13] BiocGenerics_0.34.0 Seurat_3.1.5
[15] ggplot2_3.3.2 cowplot_1.0.0
[17] workflowr_1.6.2
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[4] fs_1.4.2 RcppAnnoy_0.0.16 RColorBrewer_1.1-2
[7] httr_1.4.1 rprojroot_1.3-2 sctransform_0.2.1
[10] tools_4.0.0 backports_1.1.9 R6_2.4.1
[13] irlba_2.3.3 KernSmooth_2.23-17 uwot_0.1.8
[16] lazyeval_0.2.2 colorspace_1.4-1 withr_2.2.0
[19] tidyselect_1.1.0 gridExtra_2.3 compiler_4.0.0
[22] git2r_0.27.1 plotly_4.9.2.1 labeling_0.3
[25] scales_1.1.1 lmtest_0.9-37 ggridges_0.5.2
[28] pbapply_1.4-2 rappdirs_0.3.1 stringr_1.4.0
[31] digest_0.6.25 rmarkdown_2.3 XVector_0.28.0
[34] pkgconfig_2.0.3 htmltools_0.5.0 htmlwidgets_1.5.1
[37] rlang_0.4.7 farver_2.0.3 generics_0.0.2
[40] zoo_1.8-8 jsonlite_1.7.0 ica_1.0-2
[43] dplyr_1.0.2 RCurl_1.98-1.2 magrittr_1.5
[46] GenomeInfoDbData_1.2.3 patchwork_1.0.1 Matrix_1.2-18
[49] Rhdf5lib_1.10.0 Rcpp_1.0.5 munsell_0.5.0
[52] ape_5.4 reticulate_1.16 lifecycle_0.2.0
[55] stringi_1.4.6 whisker_0.4 yaml_2.2.1
[58] zlibbioc_1.34.0 MASS_7.3-51.6 Rtsne_0.15
[61] plyr_1.8.6 grid_4.0.0 listenv_0.8.0
[64] promises_1.1.1 ggrepel_0.8.2 crayon_1.3.4
[67] lattice_0.20-41 splines_4.0.0 knitr_1.29
[70] pillar_1.4.6 igraph_1.2.5 future.apply_1.6.0
[73] reshape2_1.4.4 codetools_0.2-16 leiden_0.3.3
[76] glue_1.4.2 evaluate_0.14 data.table_1.12.8
[79] vctrs_0.3.4 png_0.1-7 httpuv_1.5.4
[82] gtable_0.3.0 RANN_2.6.1 purrr_0.3.4
[85] tidyr_1.1.0 future_1.17.0 xfun_0.15
[88] rsvd_1.0.3 RSpectra_0.16-0 later_1.1.0.1
[91] survival_3.2-3 viridisLite_0.3.0 tibble_3.0.3
[94] cluster_2.1.0 globals_0.12.5 fitdistrplus_1.1-1
[97] ellipsis_0.3.1 ROCR_1.0-11