Clustering of TDP-HA experiment and the D96 samples
Katharina Hembach
28/01/2021
Last updated: 2021-03-11
Checks: 6 1
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 864cc2d | khembach | 2021-03-11 | DR with cells colored by individual clustering |
| html | d20fa77 | khembach | 2021-01-28 | Build site. |
| Rmd | e6766a6 | khembach | 2021-01-28 | cluster TDP experiment together with D96 samples |
Load packages
library(BiocParallel)
library(ggplot2)
library(dplyr)
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(future)
library(ComplexHeatmap)
library(RColorBrewer)
library(viridis)Load data
## Seurat objects with normalized data
so_tdp <- readRDS(file.path("output", "so_TDP_05_plasmid_expression.rds"))
so_tdp$group_id <- "TDP"
# so_timeline <- readRDS(file.path("output", "so_06-clustering_all_timepoints.rds"))
so_d96 <- readRDS(file.path("output", "so_04_clustering.rds"))
## select only the D96 cells
so_d96 <- subset(so_d96, subset = group_id == "D96")We merge the samples from the two data sets into a Seurat object.
## merge the two Seurat objects
so <- merge(so_tdp, y = so_d96, add.cell.ids = c("tdp_ha", "D96"),
project = "neural_cultures", merge.data = TRUE)
so$group_id <- factor(so$group_id, levels = c("D96", "TDP"))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
DR with cells colored by individial clustering
We show the cluster membership of the individual clustering in the combined UMAP.
## combined clustering so$RNA_snn_res.0.4
## individual clustering so$integrated_snn_res.0.4 (D96)
## so_tdp$RNA_snn_res.0.4 (TDP-HA)
# we lost the individual clustering of the TDP-HA samples,
# and will add them to the so object
so$TDPHA_snn_res.0.4 <- NA
## iterate throuth each sample and add the corresponding cluster ids
for( s in unique(so_tdp$sample_id)){
ind <- so$sample_id == s
ind_tdp <- so_tdp$sample_id == s
so$TDPHA_snn_res.0.4[ind] <- as.character(so_tdp$RNA_snn_res.0.4)[match(so$barcode[ind],
so_tdp$barcode[ind_tdp])]
}
so$TDPHA_snn_res.0.4 <- factor(so$TDPHA_snn_res.0.4,
levels = as.character(0:16))
so$integrated_snn_res.0.4 <- factor(so$integrated_snn_res.0.4,
levels = as.character(0:16))
cs <- sample(colnames(so), 1e4)
.plot_dr <- function(so, dr, id)
DimPlot(so, cells = cs, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("group_id", "sample_id", "ident", "TDPHA_snn_res.0.4", "integrated_snn_res.0.4")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
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")
}group_id
sample_id
ident
TDPHA_snn_res.0.4
integrated_snn_res.0.4
How are the individal clusters distributed in the combined clustering?
so$RNA_snn_res.0.4 %>% table.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
6178 6176 4782 3671 3470 3203 2232 2229 1971 1295 1248 1142 675 538 142 51 so$integrated_snn_res.0.4 %>% table.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
268 1023 29 703 1270 639 670 639 521 464 215 384 501 303 328 123
16
53 so$TDPHA_snn_res.0.4 %>% table.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
4974 4858 3809 3014 2586 2835 2370 1570 1859 1046 858 269 374 200 154 56
16
38 ## D96
## check if cells from the same cluster are still in the same cluster
(n_clusters <- table(so$RNA_snn_res.0.4, so$integrated_snn_res.0.4))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 186 7 0 46 1270 2 4 2 22 9 107 16 5 1 1
1 3 960 0 0 0 6 27 3 0 24 0 27 3 21 0
2 0 8 0 0 0 0 489 296 0 0 0 38 0 0 0
3 7 33 0 0 0 6 18 5 0 49 1 7 485 19 14
4 0 5 0 0 0 194 0 9 0 239 1 47 0 0 1
5 0 1 0 0 0 392 119 10 0 0 0 3 0 0 1
6 2 0 0 0 0 16 3 312 0 1 0 32 0 0 0
7 6 0 1 1 0 0 0 0 490 0 16 0 0 0 0
8 2 9 0 0 0 0 0 0 0 4 0 1 5 260 0
9 5 0 0 0 0 5 0 0 0 126 0 2 2 0 7
10 55 0 20 656 0 1 1 0 9 7 65 8 0 1 1
11 1 0 0 0 0 4 0 0 0 0 0 6 0 0 143
12 1 0 0 0 0 0 0 0 0 0 0 1 0 0 159
13 0 0 0 0 0 13 9 2 0 4 25 196 1 1 1
14 0 0 8 0 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
15 16
0 120 0
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 3 53
11 0 0
12 0 0
13 0 0
14 0 0
15 0 0fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "combined clusters",
column_title = "individual clusters",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
## TDP-HA experiment
(n_clusters <- table(so$RNA_snn_res.0.4, so$TDPHA_snn_res.0.4))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 531 1572 406 288 288 311 278 162 196 107 99 32 38 20 39
1 1783 587 508 396 340 349 313 204 239 133 101 32 57 29 19
2 510 469 1293 288 270 250 213 150 207 104 76 32 39 28 15
3 401 404 284 232 792 237 178 119 159 77 63 17 30 11 14
4 374 384 287 913 154 206 174 115 133 80 80 18 22 16 11
5 326 305 248 207 156 443 144 119 541 68 52 16 27 7 11
6 215 214 184 150 134 535 125 78 90 53 43 13 18 4 8
7 161 193 121 117 102 111 659 77 69 32 30 8 14 8 8
8 223 224 150 132 114 118 97 433 88 37 33 12 12 10 3
9 140 147 111 97 79 91 67 35 47 288 22 10 4 6 3
10 50 148 30 21 15 32 18 9 13 9 3 2 3 2 0
11 134 112 94 94 77 70 48 37 39 23 230 7 9 9 3
12 80 52 53 50 39 44 36 19 25 19 18 68 2 2 6
13 28 31 22 19 13 21 10 8 10 7 6 1 95 0 13
14 15 9 11 7 7 14 8 3 2 3 2 1 1 48 1
15 3 7 7 3 6 3 2 2 1 6 0 0 3 0 0
15 16
0 9 4
1 8 4
2 2 5
3 5 4
4 4 3
5 2 5
6 1 1
7 2 3
8 3 1
9 1 0
10 13 0
11 1 1
12 1 0
13 2 0
14 2 0
15 0 7fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "combined clusters",
column_title = "individual clusters",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_08-00_clustering_HA_D96.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