scRNAseq_combined_04_labels
retogerber
2024-02-12
Last updated: 2024-02-12
Checks: 7 0
Knit directory: synovialscrnaseq/
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Modified: analysis/scRNAseq_combined_06_Figures.Rmd
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/scRNAseq_combined_04_labels.Rmd) and HTML (public/scRNAseq_combined_04_labels.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 | 58eeb06 | Reto Gerber | 2023-05-30 | add new version |
| html | 58eeb06 | Reto Gerber | 2023-05-30 | add new version |
| Rmd | 4575ba6 | Reto Gerber | 2022-12-21 | Update analyis |
Set up
suppressPackageStartupMessages({
library(magrittr)
library(SingleCellExperiment)
library(SingleR)
library(BiocParallel)
library(ggplot2)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)
bpparam <- BiocParallel::MulticoreParam(workers=n_workers, RNGseed = 123)
analysis_version <- 7
here::here()[1] "/home/retger/Synovial/synovialscrnaseq"source(here::here("code","utilities_plots.R"))
set.seed(100)syn_sce <- readRDS(file = here::here("output",paste0("combined_v",analysis_version,"_sce_hvg_cms.rds")))syn_ref <- syn_sce[,!(syn_sce$Protocol %in% c("wei","stephenson"))]Loading required package: tidySingleCellExperiment
Attaching package: 'tidySingleCellExperiment'The following object is masked from 'package:IRanges':
sliceThe following object is masked from 'package:S4Vectors':
renameThe following object is masked from 'package:matrixStats':
countThe following object is masked from 'package:magrittr':
extractThe following object is masked from 'package:stats':
filtersyn_annot <- syn_sce[,syn_sce$Protocol %in% c("wei","stephenson")]Annotate with SingleR
assay type: reconstructed
bpstart(bpparam)
out_ttests <- scran::pairwiseTTests(assay(syn_ref, "logcounts"), syn_ref$minor_celltype, block=syn_ref$Sample, direction="up",BPPARAM=bpparam, lfc=0.5)
bpstop(bpparam)
saveRDS(out_ttests, here::here("output",paste0("combined_v",analysis_version,"_SingleR_markers.rds")))out_ttests <- readRDS(here::here("output",paste0("combined_v",analysis_version,"_SingleR_markers.rds")))
markers <- scran::getTopMarkers(out_ttests[[1]], out_ttests[[2]], n=50,fdr.threshold = 0.05, pval.type="all")sapply(markers, function(m) sort(sapply(m,length))[2]) ?.CCR7+ CCL5+ LEF1low SELLlow
2
ACKRhigh IL1R1high CLU+ SELEhigh TNFAIP3+ IL6+ CCL2high venous.ACKRhigh IL1R1med CLU+ venous
2
ACKRhigh IL1R1low CLU+ venous.ACKRhigh IL1R1med CLU+ venous
2
ACKRhigh IL1R1med CLU+ venous.ACKRhigh IL1R1med CLU+ venous
0
ACKRmed IL1R1- CLU- SPARChigh SELE+ transitional.ACKRhigh IL1R1med CLU+ venous
7
B cells.?
50
CADM1high ACAN+ DKK3+.NOTCH3+ GGT5low
34
CCR7- TIGIT+ CTLA4+.CCR7+ CCL5- LEF1+ SELL+
28
CCR7+ CCL5- LEF1+ SELL+.CCR7+ CCL5+ LEF1low SELLlow
16
CCR7+ CCL5+ LEF1low SELLlow.GNLY- GZMK+ GZMH- GZMBlow
23
CD1C+ CLEC10A+.CLEC10A+ CD48low
40
CD3- NKG7- IL7R+ ILC.CCR7+ CCL5+ LEF1low SELLlow
16
CD3- NKG7+ GNLY+ NK cells.NKG7+ GNLY+ GZMK- GZMB+
17
CLEC10A+ CD48low.COLEC12neg CD48+
2
CLEC9A+ CADM1+ CLNK+.?
50
COLEC12high CD209+ LYVE1+.COLEC12med CD48low
20
COLEC12high TIMD4+.COLEC12med TIMD4+ SPP1neg & COLEC12med TIMD4neg SPP1+
26
COLEC12high TIMD4+ TOP2A+.?
50
COLEC12med CD48low.COLEC12high CD209+ LYVE1+
13
COLEC12med TIMD4+ SPP1neg & COLEC12med TIMD4neg SPP1+.COLEC12high TIMD4+
3
COLEC12neg CD48+.COLEC12med CD48low
8
GGT5high CXCL12 high FGF7+.NOTCH3+ GGT5low
27
GJA4+ CLDN5+ arterial .?
50
GNLY- GZMK+ GZMH- GZMBlow.NKG7+ GNLY+ GZMK- GZMB+
7
GNLY- GZMK+ GZMH+ GZMB+.NKG7+ GNLY+ GZMK- GZMB+
8
HLA-DRAhigh CD74+.GGT5high CXCL12 high FGF7+
40
IDO1+ LAMP3.?
50
KDR+ SPP1+ SPARChigh capillary.TOP2A CENPF+ proliferating
2
LYVE1+ PROX1+ CCL21+ lymphatic.TOP2A CENPF+ proliferating
42
Macrophages.CADM1high ACAN+ DKK3+
7
Mast cells.?
50
Neutrophils.?
50
NKG7+ GNLY+ GZMK- GZMB+.GNLY- GZMK+ GZMH+ GZMB+
6
NOTCH3+ GGT5low.GGT5high CXCL12 high FGF7+
13
Pericytes/Mural cells.?
50
Plasmablasts.LYVE1+ PROX1+ CCL21+ lymphatic
10
Plasmacytoid DCs.?
50
PRG4+ CD55+ TWISTNB+ lining SF.?
50
S100A12+ PLAC8+ CD48high.CLEC10A+ CD48low
35
SERPINE1+ COL5A3+ LOXL2high.?
50
SPP1+ CD48+.COLEC12med TIMD4+ SPP1neg & COLEC12med TIMD4neg SPP1+
16
TNXBhigh IGFBP6+ FGFBP2+.HLA-DRAhigh CD74+
34
TOP2A CENPF+ proliferating.LYVE1+ PROX1+ CCL21+ lymphatic
46
TOP2A+ CENPF+ proliferating T & NK cells.?
50 # sapply(markers, function(m) which.min(sapply(m,length)))# set.seed(1234)
# test_syn_ref <- syn_ref[,sample(seq_along(syn_ref$minor_celltype),5000)]
# table(test_syn_ref$minor_celltype,useNA="ifany")
# table(is.na(syn_ref$minor_celltype))
syn_ref <- syn_ref[,!is.na(syn_ref$minor_celltype)]
sr_trained <- trainSingleR(ref=syn_ref, labels=syn_ref$minor_celltype,
assay.type = "reconstructed", aggr.ref=TRUE)Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterations
Warning: did not converge in 10 iterationssaveRDS(sr_trained, here::here("output",paste0("combined_v",analysis_version,"_SingleR_trained.rds")))# test_syn_annot <- syn_annot[,sample(seq_len(ncol(syn_annot)),5000)]
predictions_rec <- classifySingleR(test=syn_annot,trained = sr_trained, assay.type="reconstructed")saveRDS(predictions_rec, here::here("output",paste0("combined_v",analysis_version,"_SingleR_predictions_recrec.rds")))Results
assay: reconstructed
predictions <- predictions_recsyn_sce$minor_celltype[syn_sce$Protocol %in% c("wei","stephenson")] <- predictions$labelsplotScoreHeatmap(predictions,annotation_col=as.data.frame(colData(syn_annot)[,c("Protocol","Sample"),drop=FALSE]))
| Version | Author | Date |
|---|---|---|
| 58eeb06 | Reto Gerber | 2023-05-30 |
table(predictions$labels)
ACKRhigh IL1R1+ CLU+ SELE+ venous
715
ACKRhigh IL1R1+ CLU+ SELEhigh TNFAIP3+ venous
756
ACKRhigh IL1R1+ CLU+ VCAN+ venous
1122
ACKRmed CLU- SPARChigh
925
B cells
471
C1QA/B/C+ FOLR2low CCR2+ CD48+ CLEC10A
442
CADM1high ACAN+ DKK3+
2052
CCR7high LEF1+ SELL+
743
CCR7med LEF1low SELLlow
574
CD1C+ CLEC10A+
100
CD3- NKG7- KLRB1+ IL7R+
35
CD3- NKG7+ GNLY+
277
CD48+ CLEC10A+
172
CD48high S100A12+ IL1B+
51
CD48low SPP1+
260
CLEC9A+ CADM1+ CLNK+
57
Endothelial cells
50
FOLR2+ MERTK+ TIMD4+ & FOLR2low MERTKlow SPP1+ subsets
675
FOLR2high MERTK+ SELENOPhigh CD48med
604
FOLR2high MERTK+ SELENOPhigh COLEC12high LYVE1+ CD209+ SLC40A1+
498
FOLR2high MERTK+ SELENOPhigh COLEC12high TIMD4+
783
FOLR2low MERTKlow TOP2A+ CENPF+ proliferating
81
GGT5high CXCL12 high FGF7+
5504
GJA4+ CLDN5+ arterial
330
GZMB- GZMH- GZMK+
681
GZMB+ GZMH+ GZMK- GNLY+
287
GZMB+ GZMH+ GZMK+
1389
HLA-DRAhigh CD74+
4266
IDO1+ LAMP3+
54
KDR+ SPP1+ SPARChigh capillary
651
LYVE1+ PROX1+ CCL21+ lymphatic
61
Macrophages
480
Mast cells
134
MMP13+
2154
Neutrophils
2
Pericytes/Mural cells
857
Plasmablasts
251
Plasmacytoid DCs
19
PRG4+ CD55+ TWISTNB+ lining SF
4812
SERPINE1+ COL5A3+ LOXL2high
941
TIGIT+ CTLA4+
1306
TNXBhigh IGFBP6+ FGFBP2+
3239
TOP2A+ CENPF+
136
TOP2A+ CENPF+ proliferating
68 set.seed(123)
shuffle <- sample(seq_len(dim(syn_sce)[2]),5000)
scater::plotReducedDim(syn_sce[,shuffle], "UMAP_corrected", colour_by = "minor_celltype",other_fields=list("Protocol"),point_alpha=1) +
facet_wrap(~Protocol) +
scale_color_viridis_d()Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
| Version | Author | Date |
|---|---|---|
| 58eeb06 | Reto Gerber | 2023-05-30 |
# pred.syn <- readRDS(file = here::here("output",paste0("protocol_v",analysis_version,"_predSingleR_sub.rds")))
lookup <- unique(colData(syn_ref)[,c("minor_celltype","main_celltype")])
mainlabsdf <- dplyr::left_join(
data.frame(minor_celltype=predictions$labels),
as.data.frame(lookup),
by="minor_celltype")
labssplit <- split(predictions$labels,mainlabsdf$main_celltype)
for(i in seq_along(labssplit)){
print(table(labssplit[[i]]))
}
B cells
471
CD1C+ CLEC10A+ CLEC9A+ CADM1+ CLNK+ IDO1+ LAMP3+
100 57 54
ACKRhigh IL1R1+ CLU+ SELE+ venous
715
ACKRhigh IL1R1+ CLU+ SELEhigh TNFAIP3+ venous
756
ACKRhigh IL1R1+ CLU+ VCAN+ venous
1122
ACKRmed CLU- SPARChigh
925
Endothelial cells
50
GJA4+ CLDN5+ arterial
330
KDR+ SPP1+ SPARChigh capillary
651
LYVE1+ PROX1+ CCL21+ lymphatic
61
TOP2A+ CENPF+ proliferating
68
CADM1high ACAN+ DKK3+ GGT5high CXCL12 high FGF7+
2052 5504
HLA-DRAhigh CD74+ MMP13+
4266 2154
PRG4+ CD55+ TWISTNB+ lining SF SERPINE1+ COL5A3+ LOXL2high
4812 941
TNXBhigh IGFBP6+ FGFBP2+
3239
C1QA/B/C+ FOLR2low CCR2+ CD48+ CLEC10A
442
CD48+ CLEC10A+
172
CD48high S100A12+ IL1B+
51
CD48low SPP1+
260
FOLR2+ MERTK+ TIMD4+ & FOLR2low MERTKlow SPP1+ subsets
675
FOLR2high MERTK+ SELENOPhigh CD48med
604
FOLR2high MERTK+ SELENOPhigh COLEC12high LYVE1+ CD209+ SLC40A1+
498
FOLR2high MERTK+ SELENOPhigh COLEC12high TIMD4+
783
FOLR2low MERTKlow TOP2A+ CENPF+ proliferating
81
Macrophages
480
Mast cells
134
Neutrophils
2
Pericytes/Mural cells
857
Plasmablasts
251
Plasmacytoid DCs
19
CCR7high LEF1+ SELL+ CCR7med LEF1low SELLlow CD3- NKG7- KLRB1+ IL7R+
743 574 35
CD3- NKG7+ GNLY+ GZMB- GZMH- GZMK+ GZMB+ GZMH+ GZMK- GNLY+
277 681 287
GZMB+ GZMH+ GZMK+ TIGIT+ CTLA4+ TOP2A+ CENPF+
1389 1306 136
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] gdtools_0.2.3 tidySingleCellExperiment_1.0.0
[3] ggplot2_3.3.3 BiocParallel_1.24.1
[5] SingleR_1.4.1 SingleCellExperiment_1.12.0
[7] SummarizedExperiment_1.20.0 Biobase_2.50.0
[9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[11] IRanges_2.24.1 S4Vectors_0.28.1
[13] BiocGenerics_0.36.1 MatrixGenerics_1.2.1
[15] matrixStats_0.58.0 magrittr_2.0.1
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0
[3] RColorBrewer_1.1-2 httr_1.4.2
[5] rprojroot_2.0.2 tools_4.0.3
[7] R6_2.5.0 irlba_2.3.3
[9] vipor_0.4.5 DBI_1.1.1
[11] lazyeval_0.2.2 colorspace_2.0-0
[13] withr_2.4.1 gridExtra_2.3
[15] tidyselect_1.1.0 compiler_4.0.3
[17] git2r_0.28.0 cli_2.3.0
[19] BiocNeighbors_1.8.2 DelayedArray_0.16.3
[21] plotly_4.9.3 labeling_0.4.2
[23] scales_1.1.1 systemfonts_1.0.1
[25] stringr_1.4.0 digest_0.6.27
[27] svglite_1.2.3.2 rmarkdown_2.6
[29] XVector_0.30.0 RhpcBLASctl_0.20-137
[31] scater_1.18.6 pkgconfig_2.0.3
[33] htmltools_0.5.1.1 sparseMatrixStats_1.2.1
[35] highr_0.8 limma_3.46.0
[37] htmlwidgets_1.5.3 rlang_0.4.10
[39] DelayedMatrixStats_1.12.3 farver_2.0.3
[41] generics_0.1.0 jsonlite_1.7.2
[43] dplyr_1.0.4 RCurl_1.98-1.2
[45] BiocSingular_1.6.0 GenomeInfoDbData_1.2.4
[47] scuttle_1.0.4 Matrix_1.3-2
[49] ggbeeswarm_0.6.0 Rcpp_1.0.6
[51] munsell_0.5.0 fansi_0.4.2
[53] viridis_0.5.1 lifecycle_1.0.0
[55] stringi_1.5.3 whisker_0.4
[57] yaml_2.2.1 edgeR_3.32.1
[59] zlibbioc_1.36.0 grid_4.0.3
[61] promises_1.2.0.1 dqrng_0.2.1
[63] crayon_1.4.1 lattice_0.20-41
[65] cowplot_1.1.1 beachmat_2.6.4
[67] locfit_1.5-9.4 knitr_1.31
[69] pillar_1.4.7 igraph_1.2.6
[71] glue_1.4.2 evaluate_0.14
[73] scran_1.18.7 data.table_1.13.6
[75] vctrs_0.3.6 httpuv_1.5.5
[77] gtable_0.3.0 purrr_0.3.4
[79] tidyr_1.1.2 assertthat_0.2.1
[81] xfun_0.21 rsvd_1.0.3
[83] later_1.1.0.1 viridisLite_0.3.0
[85] pheatmap_1.0.12 tibble_3.0.6
[87] beeswarm_0.2.3 bluster_1.0.0
[89] statmod_1.4.35 ellipsis_0.3.1
[91] here_1.0.1