04_2_RNA_classification_subclustering
toobiwankenobi
2020-07-28
Last updated: 2021-02-04
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Knit directory: melanoma_publication_old_data/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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
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visible FALSE
code/helper_functions//censor_dat.R
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visible FALSE
code/helper_functions//detect_mRNA_expression.R
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visible FALSE
code/helper_functions//DistanceToClusterCenter.R
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code/helper_functions//findClusters.R
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visible FALSE
code/helper_functions//findCommunity.R
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visible FALSE
code/helper_functions//getCellCount.R
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visible FALSE
code/helper_functions//getInfoFromString.R
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visible FALSE
code/helper_functions//getSpotnumber.R
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visible FALSE
code/helper_functions//plotBarFracCluster.R
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visible FALSE
code/helper_functions//plotCellCounts.R
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visible FALSE
code/helper_functions//plotCellFrac.R
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visible FALSE
code/helper_functions//plotCellFracGroups.R
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visible FALSE
code/helper_functions//plotCellFracGroupsSubset.R
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visible FALSE
code/helper_functions//plotCellFractions.R
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visible FALSE
code/helper_functions//plotDist.R
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visible FALSE
code/helper_functions//scatter_function.R
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visible FALSE
code/helper_functions//sceChecks.R
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visible FALSE
code/helper_functions//validityChecks.R
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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(Rphenograph)
library(igraph)
library(CATALYST)
library(workflowr)
library(gridExtra)
library(dplyr)Load data
sce <- readRDS(file = "data/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 <- "Tcytotoxic"
sce[,sce$celltype_rf %in% c("CD134+ Tcell", "CD134- Tcell")]$celltype <- "Tcell"Define color vector for superclasses
celltype_color <- vector(length = length(unique(sce$celltype)))
names(celltype_color) <- unique(sce$celltype)
celltype_color["CD38"] <- "gold"
celltype_color["HLA-DR"] <- "darkolivegreen"
celltype_color["Macrophage"] <- "yellow2"
celltype_color["Neutrophil"] <- "thistle"
celltype_color["Tcell"] <- "cyan3"
celltype_color["Vasculature"] <- "sienna1"
celltype_color["Stroma"] <- "tomato"
celltype_color["Tcytotoxic"] <- "deepskyblue"
celltype_color["Tumor"] <- "sienna4"
celltype_color["unknown"] <- "gray88"
# Save in SCE object
metadata(sce)$colour_vectors$celltype <- celltype_colorDefinition 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$Neutrophil <- c("CD15", "PDL1","GLUT1", "MPO","T5_CCL4","T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13",
"T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9","T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$Tcell <- 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$Tcytotoxic <- c("CD3", "CD8", "PD1", "Lag3", "CCR2","CD38","PDL1", "HLADR", "CD134","T5_CCL4", "B2M", "CD31",
"T7_CCL18","T1_CXCL8","T4_CXCL10","T8_CXCL13","T3_CXCL12","T12_CCL2","T2_CCL22","T9_CXCL9",
"T11_CCL8","T10_CCL19","cleavedPARP", "pRB")
marker_list$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)$marker_class <- "state"
# vector for clustering
fs_clustering <- vector(length = ncol(sce))
# Macrophage, Tcell, Tcytotoxic, Tumor will be clustered for a total of 6 clustes each
set.seed(12345)
# 6 clusters
for(i in c("Tcell","Macrophage","Tcytotoxic","Tumor")){
#subset cells for clustering
cur_sce <- sce[,sce$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",
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 HLA-DR_1
1703 2323 3115 4049 13176
HLA-DR_2 HLA-DR_3 HLA-DR_4 Macrophage_1 Macrophage_2
4636 5941 12700 7513 13380
Macrophage_3 Macrophage_4 Macrophage_5 Macrophage_6 Neutrophil_1
11788 13106 8945 10696 622
Neutrophil_2 Neutrophil_3 Neutrophil_4 Stroma_1 Stroma_2
1223 1701 1508 2325 3956
Stroma_3 Stroma_4 Tcell_1 Tcell_2 Tcell_3
7841 10868 24011 14807 13230
Tcell_4 Tcell_5 Tcell_6 Tcytotoxic_1 Tcytotoxic_2
12678 24222 8924 6013 7272
Tcytotoxic_3 Tcytotoxic_4 Tcytotoxic_5 Tcytotoxic_6 Tumor_1
5321 10216 5908 10474 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)$good_marker,]
# Transform and scale
assay(mean_sce, "arcsinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "arcsinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))
plotHeatmap(mean_sce, exprs_values = "arcsinh_scaled",
colour_columns_by = c("celltype"),
labels_col = mean_sce$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 - Tcytotoxic
# Density of Expression in different Tcytotoxic 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$cellID <- rownames(cur_exprs)
cur_exprs <- cur_exprs %>%
reshape2::melt(id.vars = c("cellID", "celltype", "celltype_rf", "celltype_clustered"),
variable.name = "marker", value.name = "asinh")
# clustering
a <- cur_exprs %>%
filter(celltype == "Tcytotoxic" & marker %in% marker_list$Tcytotoxic) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
# manual gating
b <- cur_exprs %>%
filter(celltype == "Tcytotoxic" & marker %in% marker_list$Tcytotoxic) %>%
ggplot(data=., aes(asinh, color = celltype_rf)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
grid.arrange(a, b, ncol = 2)Warning: Removed 17847 rows containing non-finite values (stat_density).
Warning: Removed 17847 rows containing non-finite values (stat_density).
Conclusion: if you want to catch REAL biological signal than flowSOM clustering isn’t gonna do it!
Tcell
# clustering
a <- cur_exprs %>%
filter(celltype == "Tcell" & marker %in% marker_list$Tcell) %>%
ggplot(data=., aes(asinh, color = celltype_clustered)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
# manual gating
b <- cur_exprs %>%
filter(celltype == "Tcell" & marker %in% marker_list$Tcell) %>%
ggplot(data=., aes(asinh, color = celltype_rf)) +
geom_density() +
xlim(0,4) +
facet_wrap(~marker)
grid.arrange(a, b, ncol = 2)Warning: Removed 33619 rows containing non-finite values (stat_density).
Warning: Removed 33619 rows containing non-finite values (stat_density).
Tumor
# clustering
cur_exprs %>%
filter(celltype == "Tumor" & marker %in% marker_list$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/sce_RNA.rds")
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 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=C
[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] dplyr_1.0.2 gridExtra_2.3
[3] CATALYST_1.12.2 igraph_1.2.6
[5] Rphenograph_0.99.1.9003 viridis_0.5.1
[7] viridisLite_0.3.0 scater_1.16.2
[9] ggplot2_3.3.3 SingleCellExperiment_1.12.0
[11] SummarizedExperiment_1.20.0 Biobase_2.50.0
[13] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[15] IRanges_2.24.1 S4Vectors_0.28.1
[17] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[19] matrixStats_0.57.0 LSD_4.1-0
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 circlize_0.4.12
[3] drc_3.0-1 plyr_1.8.6
[5] ConsensusClusterPlus_1.52.0 splines_4.0.3
[7] flowCore_2.0.1 BiocParallel_1.22.0
[9] TH.data_1.0-10 digest_0.6.27
[11] htmltools_0.5.0 magrittr_2.0.1
[13] CytoML_2.0.5 cluster_2.1.0
[15] openxlsx_4.2.3 ComplexHeatmap_2.4.3
[17] RcppParallel_5.0.2 sandwich_3.0-0
[19] flowWorkspace_4.0.6 cytolib_2.0.3
[21] jpeg_0.1-8.1 colorspace_2.0-0
[23] ggrepel_0.9.0 haven_2.3.1
[25] xfun_0.20 crayon_1.3.4
[27] RCurl_1.98-1.2 jsonlite_1.7.2
[29] hexbin_1.28.2 graph_1.66.0
[31] survival_3.2-7 zoo_1.8-8
[33] glue_1.4.2 gtable_0.3.0
[35] nnls_1.4 zlibbioc_1.36.0
[37] XVector_0.30.0 GetoptLong_1.0.5
[39] DelayedArray_0.16.0 ggcyto_1.16.0
[41] car_3.0-10 BiocSingular_1.4.0
[43] Rgraphviz_2.32.0 shape_1.4.5
[45] abind_1.4-5 scales_1.1.1
[47] pheatmap_1.0.12 mvtnorm_1.1-1
[49] Rcpp_1.0.5 plotrix_3.7-8
[51] clue_0.3-58 foreign_0.8-81
[53] rsvd_1.0.3 FlowSOM_1.20.0
[55] tsne_0.1-3 RColorBrewer_1.1-2
[57] ellipsis_0.3.1 farver_2.0.3
[59] pkgconfig_2.0.3 XML_3.99-0.5
[61] labeling_0.4.2 reshape2_1.4.4
[63] tidyselect_1.1.0 rlang_0.4.10
[65] later_1.1.0.1 munsell_0.5.0
[67] cellranger_1.1.0 tools_4.0.3
[69] generics_0.1.0 ggridges_0.5.3
[71] evaluate_0.14 stringr_1.4.0
[73] yaml_2.2.1 knitr_1.30
[75] fs_1.5.0 zip_2.1.1
[77] purrr_0.3.4 RANN_2.6.1
[79] RBGL_1.64.0 whisker_0.4
[81] xml2_1.3.2 compiler_4.0.3
[83] rstudioapi_0.13 beeswarm_0.2.3
[85] curl_4.3 png_0.1-7
[87] tibble_3.0.4 stringi_1.5.3
[89] forcats_0.5.0 lattice_0.20-41
[91] Matrix_1.3-2 vctrs_0.3.6
[93] pillar_1.4.7 lifecycle_0.2.0
[95] GlobalOptions_0.1.2 BiocNeighbors_1.6.0
[97] data.table_1.13.6 cowplot_1.1.1
[99] bitops_1.0-6 irlba_2.3.3
[101] httpuv_1.5.4 R6_2.5.0
[103] latticeExtra_0.6-29 promises_1.1.1
[105] RProtoBufLib_2.0.0 rio_0.5.16
[107] vipor_0.4.5 codetools_0.2-18
[109] MASS_7.3-53 gtools_3.8.2
[111] rprojroot_2.0.2 rjson_0.2.20
[113] withr_2.3.0 multcomp_1.4-15
[115] GenomeInfoDbData_1.2.4 hms_0.5.3
[117] ncdfFlow_2.34.0 grid_4.0.3
[119] rmarkdown_2.6 DelayedMatrixStats_1.10.1
[121] carData_3.0-4 Rtsne_0.15
[123] git2r_0.28.0 base64enc_0.1-3
[125] ggbeeswarm_0.6.0