Clustering of TDP-HA experiment and the D96 samples
Katharina Hembach
28/01/2021
Last updated: 2021-03-31
Checks: 7 0
Knit directory: neural_scRNAseq/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/TDP-08-00-clustering-HA-D96.Rmd) and HTML (docs/TDP-08-00-clustering-HA-D96.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f7b27e3 | khembach | 2021-03-31 | use clustering from Figure 2B for DR plot |
| html | d888b55 | khembach | 2021-03-18 | Build site. |
| Rmd | 6c73bee | khembach | 2021-03-18 | highlight individual sample clustering in UMAP and heatmap |
| html | 1d6f57b | khembach | 2021-03-11 | Build site. |
| 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
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: we want to use the one from figure 2B
## --> so_06-clustering_all_timepoints.rds
## 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 through 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$RNA_snn_res.0.4[ind_tdp][match(so$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$integrated_snn_res.0.4,
levels = as.character(0:16))
## we also add the individual clustering of the D96 samples
so_ind <- readRDS(file.path("output", "so_06-clustering_all_timepoints.rds"))
so$D96_snn_res.0.4 <- NA
## iterate through each sample and add the corresponding cluster ids
for( s in c("5NC96", "6NC96")){
ind <- so$sample_id == s
ind_d96 <- so_ind$sample_id == s
so$D96_snn_res.0.4[ind] <- as.character(so_ind$RNA_snn_res.0.4[ind_d96][match(so$barcode[ind],
so_ind$barcode[ind_d96])])
}
so$D96_snn_res.0.4 <- factor(so$D96_snn_res.0.4,
levels = as.character(0:18)) %>% droplevels
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", "RNA_snn_res.0.4", "TDPHA_snn_res.0.4",
"D96_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")
}
How are the individal clusters distributed in the combined clustering?
so$RNA_snn_res.0.4 %>% table.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
6178 6176 4782 3671 3470 3203 2232 2229 1971 1295 1248 1142 675 538 142 51 so$D96_snn_res.0.4 %>% table.
0 2 3 4 5 6 7 8 9 10 11 12 13 15 16 17
1 301 78 19 266 3061 1042 9 2162 37 724 11 275 82 12 53 so$TDPHA_snn_res.0.4 %>% table.
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
5186 4670 3875 3044 2943 2914 1707 1699 1668 1130 939 501 224 137 101 78
16
54 ## D96
## check if cells from the same cluster are still in the same cluster
(n_clusters <- table(so$RNA_snn_res.0.4, so$D96_snn_res.0.4))
0 2 3 4 5 6 7 8 9 10 11 12 13 15 16
0 0 0 0 12 0 4 125 0 1614 15 21 1 0 0 6
1 0 6 43 0 8 1001 16 0 0 0 0 0 0 0 0
2 0 24 1 1 10 793 1 0 0 0 0 0 0 1 0
3 0 0 3 0 0 51 583 0 0 0 0 0 7 0 0
4 0 7 17 1 15 433 15 0 0 0 0 0 8 0 0
5 0 252 5 0 201 65 2 0 0 0 0 0 0 1 0
6 1 6 0 0 8 345 4 0 0 0 0 0 1 1 0
7 0 0 0 0 0 0 9 9 480 0 1 10 0 0 5
8 0 0 5 0 3 239 33 0 0 0 0 0 1 0 0
9 0 0 0 1 3 10 23 0 0 0 0 0 110 0 0
10 0 0 0 4 0 1 65 0 63 0 693 0 0 0 1
11 0 1 0 0 0 3 6 0 0 0 0 0 143 1 0
12 0 0 4 0 0 0 151 0 0 0 0 0 5 1 0
13 0 5 0 0 18 116 8 0 2 22 4 0 0 77 0
14 0 0 0 0 0 0 0 0 3 0 5 0 0 0 0
15 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
17
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 53
11 0
12 0
13 0
14 0
15 0fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "combined clusters",
column_title = "individual clusters",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
| Version | Author | Date |
|---|---|---|
| 1d6f57b | khembach | 2021-03-11 |
## which of the individual clusters contribute to each combined cluster?
(n_clusters <- table(so$D96_snn_res.0.4, so$RNA_snn_res.0.4))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
2 0 6 24 0 7 252 6 0 0 0 0 1 0 5 0
3 0 43 1 3 17 5 0 0 5 0 0 0 4 0 0
4 12 0 1 0 1 0 0 0 0 1 4 0 0 0 0
5 0 8 10 0 15 201 8 0 3 3 0 0 0 18 0
6 4 1001 793 51 433 65 345 0 239 10 1 3 0 116 0
7 125 16 1 583 15 2 4 9 33 23 65 6 151 8 0
8 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0
9 1614 0 0 0 0 0 0 480 0 0 63 0 0 2 3
10 15 0 0 0 0 0 0 0 0 0 0 0 0 22 0
11 21 0 0 0 0 0 0 1 0 0 693 0 0 4 5
12 1 0 0 0 0 0 0 10 0 0 0 0 0 0 0
13 0 0 0 7 8 0 1 0 1 110 0 143 5 0 0
15 0 0 1 0 0 1 1 0 0 0 0 1 1 77 0
16 6 0 0 0 0 0 0 5 0 0 1 0 0 0 0
17 0 0 0 0 0 0 0 0 0 0 53 0 0 0 0
15
0 0
2 0
3 0
4 0
5 0
6 0
7 1
8 0
9 0
10 0
11 0
12 0
13 0
15 0
16 0
17 0fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "individual clusters",
column_title = "combined clusters",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
## TDP-HA experiment
(n_clusters <- table(so$RNA_snn_res.0.4, so$TDPHA_snn_res.0.4))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 0 4304 0 0 2 0 6 0 0 0 0 0 0 3 63
1 5009 2 47 19 11 0 0 9 0 0 1 0 4 0 0
2 73 2 3754 2 5 14 0 0 99 0 0 0 2 0 0
3 48 31 0 0 2893 0 0 48 1 3 0 2 0 0 0
4 23 2 7 2889 3 24 0 0 1 20 4 0 1 0 0
5 1 0 12 6 0 1085 0 0 1566 0 0 0 7 0 0
6 1 2 20 63 1 1778 0 0 0 1 0 0 0 0 0
7 0 10 0 0 0 0 1700 0 0 0 0 0 1 0 3
8 23 2 0 9 13 0 0 1642 0 0 0 0 1 0 0
9 0 5 25 12 6 0 0 0 0 1095 4 1 0 0 0
10 0 286 0 0 0 0 1 0 0 0 0 0 0 3 0
11 0 0 0 41 0 1 0 0 0 9 929 3 3 0 0
12 6 0 1 0 7 1 0 0 0 2 1 495 1 0 0
13 2 20 9 3 1 11 0 0 1 0 0 0 204 0 35
14 0 3 0 0 0 0 0 0 0 0 0 0 0 131 0
15 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
15 16
0 0 2
1 0 0
2 0 0
3 0 1
4 0 0
5 0 0
6 0 0
7 0 1
8 0 0
9 0 0
10 78 0
11 0 2
12 0 0
13 0 0
14 0 0
15 0 48fqs <- prop.table(n_clusters, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "combined clusters",
column_title = "individual clusters",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
(n_clusters <- table(so$TDPHA_snn_res.0.4, so$RNA_snn_res.0.4))
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
0 0 5009 73 48 23 1 1 0 23 0 0 0 6 2 0
1 4304 2 2 31 2 0 2 10 2 5 286 0 0 20 3
2 0 47 3754 0 7 12 20 0 0 25 0 0 1 9 0
3 0 19 2 0 2889 6 63 0 9 12 0 41 0 3 0
4 2 11 5 2893 3 0 1 0 13 6 0 0 7 1 0
5 0 0 14 0 24 1085 1778 0 0 0 0 1 1 11 0
6 6 0 0 0 0 0 0 1700 0 0 1 0 0 0 0
7 0 9 0 48 0 0 0 0 1642 0 0 0 0 0 0
8 0 0 99 1 1 1566 0 0 0 0 0 0 0 1 0
9 0 0 0 3 20 0 1 0 0 1095 0 9 2 0 0
10 0 1 0 0 4 0 0 0 0 4 0 929 1 0 0
11 0 0 0 2 0 0 0 0 0 1 0 3 495 0 0
12 0 4 2 0 1 7 0 1 1 0 0 3 1 204 0
13 3 0 0 0 0 0 0 0 0 0 3 0 0 0 131
14 63 0 0 0 0 0 0 3 0 0 0 0 0 35 0
15 0 0 0 0 0 0 0 0 0 0 78 0 0 0 0
16 2 0 0 1 0 0 0 1 0 0 0 2 0 0 0
15
0 0
1 1
2 0
3 0
4 1
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 48fqs <- 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 = "individual clusters",
column_title = "combined 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 16.04.7 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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] viridis_0.5.1 viridisLite_0.3.0
[3] RColorBrewer_1.1-2 ComplexHeatmap_2.4.2
[5] future_1.17.0 SingleCellExperiment_1.10.1
[7] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[9] matrixStats_0.56.0 Biobase_2.48.0
[11] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[13] IRanges_2.22.2 S4Vectors_0.26.1
[15] BiocGenerics_0.34.0 Seurat_3.1.5
[17] cowplot_1.0.0 dplyr_1.0.2
[19] ggplot2_3.3.2 BiocParallel_1.22.0
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 rjson_0.2.20
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2
[7] circlize_0.4.10 XVector_0.28.0 GlobalOptions_0.1.2
[10] fs_1.4.2 clue_0.3-57 farver_2.0.3
[13] leiden_0.3.3 listenv_0.8.0 ggrepel_0.8.2
[16] RSpectra_0.16-0 codetools_0.2-16 splines_4.0.0
[19] knitr_1.29 jsonlite_1.7.0 ica_1.0-2
[22] cluster_2.1.0 png_0.1-7 uwot_0.1.8
[25] sctransform_0.2.1 compiler_4.0.0 httr_1.4.1
[28] backports_1.1.9 Matrix_1.2-18 lazyeval_0.2.2
[31] later_1.1.0.1 htmltools_0.5.0 tools_4.0.0
[34] rsvd_1.0.3 igraph_1.2.5 gtable_0.3.0
[37] glue_1.4.2 GenomeInfoDbData_1.2.3 RANN_2.6.1
[40] reshape2_1.4.4 rappdirs_0.3.1 Rcpp_1.0.5
[43] vctrs_0.3.4 ape_5.4 nlme_3.1-148
[46] lmtest_0.9-37 xfun_0.15 stringr_1.4.0
[49] globals_0.12.5 lifecycle_0.2.0 irlba_2.3.3
[52] MASS_7.3-51.6 zlibbioc_1.34.0 zoo_1.8-8
[55] scales_1.1.1 promises_1.1.1 yaml_2.2.1
[58] reticulate_1.16 pbapply_1.4-2 gridExtra_2.3
[61] stringi_1.4.6 shape_1.4.4 rlang_0.4.7
[64] pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14
[67] lattice_0.20-41 ROCR_1.0-11 purrr_0.3.4
[70] labeling_0.3 patchwork_1.0.1 htmlwidgets_1.5.1
[73] tidyselect_1.1.0 RcppAnnoy_0.0.16 plyr_1.8.6
[76] magrittr_1.5 R6_2.4.1 generics_0.0.2
[79] pillar_1.4.6 whisker_0.4 withr_2.2.0
[82] fitdistrplus_1.1-1 survival_3.2-3 RCurl_1.98-1.2
[85] tibble_3.0.3 future.apply_1.6.0 tsne_0.1-3
[88] crayon_1.3.4 KernSmooth_2.23-17 plotly_4.9.2.1
[91] rmarkdown_2.3 GetoptLong_1.0.1 data.table_1.12.8
[94] git2r_0.27.1 digest_0.6.25 tidyr_1.1.0
[97] httpuv_1.5.4 munsell_0.5.0