Clustering of timeline and TDP-HA experiments
Katharina Hembach
21/01/2021
Last updated: 2021-01-21
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 299088e | khembach | 2021-01-21 | Build site. |
| Rmd | 417e7b2 | khembach | 2021-01-21 | cluster TDP-HA samples together with timeline samples |
Load packages
library(BiocParallel)
library(ggplot2)
library(dplyr)
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(future)Load data
## Seurat objects with normalized data
so_tdp <- readRDS(file.path("output", "so_TDP_05_plasmid_expression.rds"))
so_tdp$group_id <- "D96"
so_timeline <- readRDS(file.path("output", "so_06-clustering_all_timepoints.rds"))We merge the samples from the two data sets into a Seurat object.
## merge the two Seurat objects
so <- merge(so_tdp, y = so_timeline, add.cell.ids = c("tdp_ha", "time_line"),
project = "neural_cultures", merge.data = TRUE)
so$group_id <- factor(so$group_id, levels = c("P22", "D52", "D96", "D223"))Variable features
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")
## PCA plot
DimPlot(so, reduction = "pca", group.by = "sample_id")
# elbow plot with the ranking of PCs based on the % of variance explained
ElbowPlot(so, ndims = 30)
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", "group_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)
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")
}sum
detected
subsets_Mt_percent
ENSG00000120948.TARDBP
ENSG00000120948.TARDBP-alevin
TDP43-HA
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_08-clustering_timeline_HA.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] future_1.17.0 SingleCellExperiment_1.10.1
[3] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[5] matrixStats_0.56.0 Biobase_2.48.0
[7] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[9] IRanges_2.22.2 S4Vectors_0.26.1
[11] BiocGenerics_0.34.0 Seurat_3.1.5
[13] cowplot_1.0.0 dplyr_1.0.2
[15] ggplot2_3.3.2 BiocParallel_1.22.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-148 tsne_0.1-3 bitops_1.0-6
[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] RCurl_1.98-1.2 magrittr_1.5 GenomeInfoDbData_1.2.3
[46] patchwork_1.0.1 Matrix_1.2-18 Rcpp_1.0.5
[49] munsell_0.5.0 ape_5.4 reticulate_1.16
[52] lifecycle_0.2.0 stringi_1.4.6 whisker_0.4
[55] yaml_2.2.1 zlibbioc_1.34.0 MASS_7.3-51.6
[58] Rtsne_0.15 plyr_1.8.6 grid_4.0.0
[61] listenv_0.8.0 promises_1.1.1 ggrepel_0.8.2
[64] crayon_1.3.4 lattice_0.20-41 splines_4.0.0
[67] knitr_1.29 pillar_1.4.6 igraph_1.2.5
[70] future.apply_1.6.0 reshape2_1.4.4 codetools_0.2-16
[73] leiden_0.3.3 glue_1.4.2 evaluate_0.14
[76] data.table_1.12.8 vctrs_0.3.4 png_0.1-7
[79] httpuv_1.5.4 gtable_0.3.0 RANN_2.6.1
[82] purrr_0.3.4 tidyr_1.1.0 xfun_0.15
[85] rsvd_1.0.3 RSpectra_0.16-0 later_1.1.0.1
[88] survival_3.2-3 viridisLite_0.3.0 tibble_3.0.3
[91] cluster_2.1.0 globals_0.12.5 fitdistrplus_1.1-1
[94] ellipsis_0.3.1 ROCR_1.0-11