04_2_RNA_classification_subclustering
toobiwankenobi
2020-07-28
Last updated: 2022-02-10
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Knit directory: MelanomaIMC/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b24a994 | toobiwankenobi | 2022-02-08 | update code to work under R 4.1.2 |
| Rmd | f9a3a83 | toobiwankenobi | 2022-02-08 | clean repo for release |
| html | 4109ff1 | toobiwankenobi | 2021-07-07 | delete html files and adapt gitignore |
| Rmd | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| html | 3203891 | toobiwankenobi | 2021-02-19 | change celltype names |
| Rmd | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | ee1595d | toobiwankenobi | 2021-02-12 | clean repo and adapt files |
| html | 3f5af3f | toobiwankenobi | 2021-02-09 | add .html files |
| Rmd | 9442cb9 | toobiwankenobi | 2020-12-22 | add all new files |
| Rmd | d8819f2 | toobiwankenobi | 2020-10-08 | read new data (nuclei expansion) and adapt scripts |
| Rmd | a21c858 | toobiwankenobi | 2020-08-06 | adapt pipeline |
| Rmd | 4fc7560 | toobiwankenobi | 2020-08-05 | Update 04_2_RNA_classification_subclustering.rmd |
| Rmd | 402481d | toobiwankenobi | 2020-08-05 | Update 04_2_RNA_classification_subclustering.rmd |
| Rmd | 8babce7 | toobiwankenobi | 2020-08-05 | update subclustering |
| Rmd | f20c4ac | toobiwankenobi | 2020-08-05 | Update 04_2_RNA_classification_subclustering.rmd |
| Rmd | 2c11d5c | toobiwankenobi | 2020-08-05 | add new scripts |
| Rmd | 73881bb | toobiwankenobi | 2020-07-30 | add code to files from old repo |
| Rmd | cf46cfa | toobiwankenobi | 2020-07-28 | create files |
Introduction
In this script we try to set global thresholds for some key markers (CD3 etc) and then sub-cluster the respective cells with markers that we know are expressed on those cells.
Preparations
knitr::opts_chunk$set(echo = TRUE, message= FALSE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())Load libraries
sapply(list.files("code/helper_functions/", full.names = TRUE), source) code/helper_functions//calculateSummary.R
value ?
visible FALSE
code/helper_functions//censor_dat.R
value ?
visible FALSE
code/helper_functions//detect_mRNA_expression.R
value ?
visible FALSE
code/helper_functions//DistanceToClusterCenter.R
value ?
visible FALSE
code/helper_functions//findMilieu.R code/helper_functions//findPatch.R
value ? ?
visible FALSE FALSE
code/helper_functions//getInfoFromString.R
value ?
visible FALSE
code/helper_functions//getSpotnumber.R
value ?
visible FALSE
code/helper_functions//plotCellCounts.R
value ?
visible FALSE
code/helper_functions//plotCellFractions.R
value ?
visible FALSE
code/helper_functions//plotDist.R code/helper_functions//read_Data.R
value ? ?
visible FALSE FALSE
code/helper_functions//scatter_function.R
value ?
visible FALSE
code/helper_functions//sceChecks.R
value ?
visible FALSE
code/helper_functions//validityChecks.R
value ?
visible FALSE sapply(list.files("/Volumes/server_homes/daniels/Git/imcRtools/R/", full.names = TRUE), source)named list()library(LSD)
library(SingleCellExperiment)
library(ggplot2)
library(scater)
library(viridis)
library(igraph)
library(CATALYST)
library(workflowr)
library(gridExtra)
library(dplyr)Load data
sce <- readRDS(file = "data/data_for_analysis/sce_RNA.rds")Marker for Clustering
Define Superclasses for subclustering
# store labels from randomForest separately
sce$celltype_rf <- sce$celltype
# assign superclasses
sce[,sce$celltype_rf %in% c("exhausted Tcytotoxic", "Tcytotoxic")]$celltype <- "CD8+ T cell"
sce[,sce$celltype_rf %in% c("CD134+ Tcell", "CD134- Tcell")]$celltype <- "CD8- T cell"Definition of markers used for subclustering the classified cells
marker_list <- list()
marker_list$Stroma <- c("SMA", "CK5", "Cadherin11", "FAP","pRB","B2M","GLUT1","T5_CCL4","T7_CCL18","CCR2","T1_CXCL8",
"T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","CD31",
"T10_CCL19","cleavedPARP")
marker_list$Tumor <- c("HLADR", "S100","B2M","GLUT1","PDL1","SOX10","T5_CCL4","T7_CCL18","CCR2","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP",
"Mart1","pRB")
marker_list$Neutrophil <- c("CD15", "PDL1","GLUT1", "MPO","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$`CD8- T cell` <- c("CD3", "CD8", "PD1", "Lag3", "CCR2","CD38","PDL1", "HLADR", "CD134","T5_CCL4", "B2M", "CD31",
"T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9",
"T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$`CD8+ T cell` <- c("CD3", "CD8", "PD1", "Lag3", "CCR2","CD38","PDL1", "HLADR", "CD134","T5_CCL4", "B2M", "CD31",
"T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9",
"T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$Macrophage <- c("CD68", "CD163", "HLADR", "PDL1", "CD3", "CCR2","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$Vasculature <- c("CD31", "SMA","CK5","pRB", "Cadherin11","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$`HLA-DR` <- c("HLADR","CD68","CD3","CCR2","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$CD38 <- c("HLADR","CD38","CD3","CCR2","CD163","CD68","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$unknown <- rownames(sce[rowData(sce)$good_marker,])
# check if the names of the marker list match the names of the classified cells
all(names(marker_list) %in% unique(sce$celltype))[1] TRUEClustering
Sub-cluster the whole dataset with FlowSOM
FlowSOM first because it is faster
## the FlowSOM function from CATALYST needs an another column in the rowData of the sce to work properly:
rowData(sce)$marker_class <- "state"
# vector for clustering
fs_clustering <- vector(length = ncol(sce))
# Macrophage, CD8- T cell, CD8+ T cell, Tumor will be clustered for a total of 6 clustes each
set.seed(12345)
# 6 clusters
for(i in c("CD8- T cell","Macrophage","CD8+ T cell","Tumor")){
#subset cells for clustering
cur_sce <- sce[,sce$celltype == i]
names(assays(cur_sce)) <- c("counts", "exprs","scaled_counts", "scaled_asinh")
cur_sce <- CATALYST::cluster(cur_sce, features = marker_list[i][[1]], ydim = 2, xdim = 3, maxK = 4)
fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}
# 4 clusters
for(i in c("Neutrophil","CD38","Stroma","Vasculature","HLA-DR", "unknown")){
cur_sce <- sce[,sce$celltype == i]
names(assays(cur_sce)) <- c("counts", "exprs","scaled_counts", "scaled_asinh")
cur_sce <- CATALYST::cluster(cur_sce, features = marker_list[i][[1]], ydim = 2, xdim = 2, maxK = 3)
fs_clustering[sce$celltype == i] <- cur_sce$cluster_id
}
# Save in SCE object
colData(sce)$celltype_clustered <- as.factor(fs_clustering)
sce$celltype_clustered <- paste0(sce$celltype, "_", sce$celltype_clustered)Visualization
Visulalize clustering results for FlowSOM
# Aggregate the counts
mean_sce <- calculateSummary(sce, split_by = c("celltype_clustered", "celltype"),
exprs_values = "counts")
# Exclude bad markers
mean_sce <- mean_sce[rowData(sce)$good_marker,]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
# Scaled
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
features = rownames(sce)[rowData(sce)$good_marker],
colour_columns_by = "celltype",
labels_col = mean_sce$celltype_clustered,
show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
zlim = c(-4, 4),legend = TRUE)
Numbers of cells per cluster
celltype_counts <- sce$celltype_clustered
table(celltype_counts)celltype_counts
CD38_1 CD38_2 CD38_3 CD38_4 CD8- T cell_1
1703 2323 3115 4049 24011
CD8- T cell_2 CD8- T cell_3 CD8- T cell_4 CD8- T cell_5 CD8- T cell_6
14807 13230 12678 24222 8924
CD8+ T cell_1 CD8+ T cell_2 CD8+ T cell_3 CD8+ T cell_4 CD8+ T cell_5
6013 7272 5321 10216 5908
CD8+ T cell_6 HLA-DR_1 HLA-DR_2 HLA-DR_3 HLA-DR_4
10474 13176 4636 5941 12700
Macrophage_1 Macrophage_2 Macrophage_3 Macrophage_4 Macrophage_5
7513 13380 11788 13106 8945
Macrophage_6 Neutrophil_1 Neutrophil_2 Neutrophil_3 Neutrophil_4
10696 622 1223 1701 1508
Stroma_1 Stroma_2 Stroma_3 Stroma_4 Tumor_1
2325 3956 7841 10868 72776
Tumor_2 Tumor_3 Tumor_4 Tumor_5 Tumor_6
94020 69388 89337 108154 119002
unknown_1 unknown_2 unknown_3 unknown_4 Vasculature_1
1869 549 452 313 10360
Vasculature_2 Vasculature_3 Vasculature_4
5568 4860 1424 Cluster Names
Assign names to clusters
annotations <- sce$celltype_clustered
# add annotation if wanted
#annotations[annotations == "Tumor_1"] <- "Tumor1_"
#annotations[annotations == "Tumor_2"] <- "Tumor2_"
#annotations[annotations == "Tumor_3"] <- "Tumor3_"
#annotations[annotations == "Tumor_4"] <- "Tumor4_"
#annotations[annotations == "Tumor_5"] <- "Tumor5_"
#annotations[annotations == "Tumor_6"] <- "Tumor6_"
sce$cellAnnotation <- annotations
mean_sce <- calculateSummary(sce, split_by = c("celltype_clustered","celltype"),
exprs_values = "counts")
# Exclude bad markers
mean_sce <- mean_sce[rowData(sce)$good_marker,]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
features = rownames(sce)[rowData(sce)$good_marker],
colour_columns_by = c("celltype"),
labels_col = mean_sce$celltype,
show_colnames = TRUE, annotation_legend = TRUE, borders_color = NA,
color = colorRampPalette(c("dark blue", "white", "dark red"))(100),
zlim = c(-4, 4),legend = TRUE)
Clustering Quality Control
Marker expression density of clusters vs. manually gated - CD8+ T cell
# Density of Expression in different CD8+ T cell clusters vs. manually gated Lag3+ cells
cur_sce <- data.frame(colData(sce))
cur_exprs <- data.frame(t(assays(sce)[[2]]))
cur_exprs <- cbind(cur_exprs, cur_sce[,c("celltype", "celltype_rf", "celltype_clustered")])
cur_exprs$cellID <- rownames(cur_exprs)
cur_exprs <- cur_exprs %>%
reshape2::melt(id.vars = c("cellID", "celltype", "celltype_rf", "celltype_clustered"),
variable.name = "marker", value.name = "asinh")
# clustering
a <- cur_exprs %>%
filter(celltype == "CD8+ T cell" & marker %in% marker_list$`CD8+ T cell`) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
# manual gating
b <- cur_exprs %>%
filter(celltype == "CD8+ T cell" & marker %in% marker_list$`CD8+ T cell`) %>%
ggplot(data=., aes(asinh, color = celltype_rf)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
grid.arrange(a, b, ncol = 2)Warning: Removed 17847 rows containing non-finite values (stat_density).
Removed 17847 rows containing non-finite values (stat_density).
CD8- T cell
# clustering
a <- cur_exprs %>%
filter(celltype == "CD8- T cell" & marker %in% marker_list$`CD8- T cell`) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
# manual gating
b <- cur_exprs %>%
filter(celltype == "CD8- T cell" & marker %in% marker_list$`CD8- T cell`) %>%
ggplot(data=., aes(asinh, color = celltype_rf)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
grid.arrange(a, b, ncol = 2)Warning: Removed 33619 rows containing non-finite values (stat_density).
Removed 33619 rows containing non-finite values (stat_density).
Tumor
# clustering
cur_exprs %>%
filter(celltype == "Tumor" & marker %in% marker_list$Tumor) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)Warning: Removed 124307 rows containing non-finite values (stat_density).
Neutrophil
# clustering
cur_exprs %>%
filter(celltype == "Neutrophil" & marker %in% marker_list$Neutrophil) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)Warning: Removed 1029 rows containing non-finite values (stat_density).
Macrophage
# clustering
cur_exprs %>%
filter(celltype == "Macrophage" & marker %in% marker_list$Macrophage) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)Warning: Removed 47900 rows containing non-finite values (stat_density).
CD38
# clustering
cur_exprs %>%
filter(celltype == "CD38" & marker %in% marker_list$CD38) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)Warning: Removed 2477 rows containing non-finite values (stat_density).
unknown
# clustering
cur_exprs %>%
filter(celltype == "unknown" & marker %in% marker_list$unknown) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)Warning: Removed 36 rows containing non-finite values (stat_density).
SCE object
Save the single cell object
saveRDS(sce, "data/data_for_analysis/sce_RNA.rds")
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gridExtra_2.3 CATALYST_1.18.1
[3] igraph_1.2.11 viridis_0.6.2
[5] viridisLite_0.4.0 scater_1.22.0
[7] scuttle_1.4.0 ggplot2_3.3.5
[9] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[11] Biobase_2.54.0 GenomicRanges_1.46.1
[13] GenomeInfoDb_1.30.1 IRanges_2.28.0
[15] S4Vectors_0.32.3 BiocGenerics_0.40.0
[17] MatrixGenerics_1.6.0 matrixStats_0.61.0
[19] LSD_4.1-0 dplyr_1.0.7
[21] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 tidyselect_1.1.1
[3] grid_4.1.2 BiocParallel_1.28.3
[5] Rtsne_0.15 aws.signature_0.6.0
[7] flowCore_2.6.0 munsell_0.5.0
[9] ScaledMatrix_1.2.0 codetools_0.2-18
[11] withr_2.4.3 colorspace_2.0-2
[13] highr_0.9 knitr_1.37
[15] rstudioapi_0.13 ggsignif_0.6.3
[17] labeling_0.4.2 git2r_0.29.0
[19] GenomeInfoDbData_1.2.7 polyclip_1.10-0
[21] farver_2.1.0 pheatmap_1.0.12
[23] flowWorkspace_4.6.0 rprojroot_2.0.2
[25] vctrs_0.3.8 generics_0.1.2
[27] TH.data_1.1-0 xfun_0.29
[29] R6_2.5.1 doParallel_1.0.16
[31] ggbeeswarm_0.6.0 clue_0.3-60
[33] rsvd_1.0.5 bitops_1.0-7
[35] DelayedArray_0.20.0 assertthat_0.2.1
[37] promises_1.2.0.1 scales_1.1.1
[39] multcomp_1.4-18 beeswarm_0.4.0
[41] gtable_0.3.0 beachmat_2.10.0
[43] processx_3.5.2 RProtoBufLib_2.6.0
[45] sandwich_3.0-1 rlang_1.0.0
[47] GlobalOptions_0.1.2 splines_4.1.2
[49] rstatix_0.7.0 hexbin_1.28.2
[51] broom_0.7.12 reshape2_1.4.4
[53] yaml_2.2.2 abind_1.4-5
[55] backports_1.4.1 httpuv_1.6.5
[57] RBGL_1.70.0 tools_4.1.2
[59] ellipsis_0.3.2 jquerylib_0.1.4
[61] RColorBrewer_1.1-2 ggridges_0.5.3
[63] Rcpp_1.0.8 plyr_1.8.6
[65] base64enc_0.1-3 sparseMatrixStats_1.6.0
[67] zlibbioc_1.40.0 purrr_0.3.4
[69] RCurl_1.98-1.5 ps_1.6.0
[71] FlowSOM_2.2.0 ggpubr_0.4.0
[73] GetoptLong_1.0.5 cowplot_1.1.1
[75] zoo_1.8-9 ggrepel_0.9.1
[77] cluster_2.1.2 colorRamps_2.3
[79] fs_1.5.2 magrittr_2.0.2
[81] ncdfFlow_2.40.0 data.table_1.14.2
[83] scattermore_0.7 circlize_0.4.13
[85] mvtnorm_1.1-3 whisker_0.4
[87] ggnewscale_0.4.5 evaluate_0.14
[89] XML_3.99-0.8 jpeg_0.1-9
[91] shape_1.4.6 ggcyto_1.22.0
[93] compiler_4.1.2 tibble_3.1.6
[95] crayon_1.4.2 ggpointdensity_0.1.0
[97] htmltools_0.5.2 later_1.3.0
[99] tidyr_1.2.0 RcppParallel_5.1.5
[101] aws.s3_0.3.21 DBI_1.1.2
[103] tweenr_1.0.2 ComplexHeatmap_2.10.0
[105] MASS_7.3-55 Matrix_1.4-0
[107] car_3.0-12 cli_3.1.1
[109] parallel_4.1.2 pkgconfig_2.0.3
[111] getPass_0.2-2 xml2_1.3.3
[113] foreach_1.5.2 vipor_0.4.5
[115] bslib_0.3.1 XVector_0.34.0
[117] drc_3.0-1 stringr_1.4.0
[119] callr_3.7.0 digest_0.6.29
[121] ConsensusClusterPlus_1.58.0 graph_1.72.0
[123] rmarkdown_2.11 DelayedMatrixStats_1.16.0
[125] curl_4.3.2 gtools_3.9.2
[127] rjson_0.2.21 lifecycle_1.0.1
[129] jsonlite_1.7.3 carData_3.0-5
[131] BiocNeighbors_1.12.0 fansi_1.0.2
[133] pillar_1.7.0 lattice_0.20-45
[135] plotrix_3.8-2 fastmap_1.1.0
[137] httr_1.4.2 survival_3.2-13
[139] glue_1.6.1 png_0.1-7
[141] iterators_1.0.13 Rgraphviz_2.38.0
[143] nnls_1.4 ggforce_0.3.3
[145] stringi_1.7.6 sass_0.4.0
[147] BiocSingular_1.10.0 CytoML_2.6.0
[149] latticeExtra_0.6-29 cytolib_2.6.1
[151] irlba_2.3.5