03_Protein_quality_control
toobiwankenobi
2020-08-04
Last updated: 2021-02-11
Checks: 6 1
Knit directory: melanoma_publication_old_data/
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| File | Version | Author | Date | Message |
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Preparations
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/sceChecks.R
value ? ?
visible FALSE FALSE
code/helper_functions/validityChecks.R
value ?
visible FALSE library(SingleCellExperiment)
library(dplyr)
library(ggplot2)
library(scater)
library(CATALYST)
library(reshape2)
library(viridis)
library(ggridges)
library(cowplot)
library(BiocParallel)
library(dittoSeq)Load the single cell experiment object and the image metadata
sce <- readRDS(file = "data/data_for_analysis/sce_protein.rds")Assay
Add different assays
assay(sce, "scaled_counts") <- t(scale(t(assay(sce, "counts"))))
assay(sce, "scaled_asinh") <- t(scale(t(assay(sce, "asinh"))))General Plots
Plot Cell Counts for every Image
# this function takes all the column metadata from the sce and plots parts thereof
plotCellCounts(sce, colour_by = "Location", split_by = "ImageNumber", imageID = "ImageNumber")
Image 16 has only a few cells and should probably be excluded.
will be flagged below
cur_sce <- data.frame(colData(sce))
# show images with less than 500 cells
cur_sce %>%
group_by(ImageNumber) %>%
summarise(n=n()) %>%
filter(n<500)`summarise()` ungrouping output (override with `.groups` argument)# A tibble: 3 x 2
ImageNumber n
<int> <int>
1 16 150
2 51 397
3 71 498Flag image in sce object for future exclusion
# define vector for each single cell whether to keep (TRUE) or not (FALSE)
includeImage <- colData(sce)$ImageNumber != 16
sce$includeImage <- includeImageMean intensity of markers per image
# we use a function from Nils. This function makes use of the aggregate function to calculate the mean for each channel over all specified groups
mean_sce <- calculateSummary(sce, split_by = c("ImageNumber", "BlockID", "Location","Mutation","Cancer_Stage", "Status_at_3m","E_I_D","Adjuvant"), exprs_values = "counts")Transform data
assay(mean_sce, "asinh") <- asinh(assay(mean_sce, "meanCounts"))
assay(mean_sce, "asinh_scaled") <- t(scale(t(asinh(assay(mean_sce, "meanCounts")))))Plot the mean data
# first we define a vector of markers that we want to plot
plot_targets <- rownames(sce)
plot_targets <- plot_targets[! plot_targets %in% c("DNA1","DNA2","HistoneH3")]
# now we plot the heatmap
plotHeatmap(mean_sce,features = plot_targets ,exprs_values = "asinh",colour_columns_by = "ImageNumber",color = viridis(100))
Plot the scaled data
# now we plot the scaled heatmap
plotHeatmap(mean_sce,features = plot_targets, exprs_values = "asinh_scaled", colour_columns_by = c("ImageNumber"), zlim = c(-3,3),
color = colorRampPalette(c("dark blue", "white", "dark red"))(100))
Cell level quality control
here we plot the marker intensity distributions for all images. since we have too many images we make groups of 10.
y <- c(rep(1:10,16),rep(11,7))
# add the group information to the sce object
sce$groups <- y[colData(sce)$ImageNumber]
# now we use the function written by Nils
plotDist(sce, plot_type = "ridges",
colour_by = "groups", split_by = "rows",
exprs_values = "asinh") +
theme_minimal(base_size = 15)
# the distributions look very even across images indicating that we have no major batch effects.Define markers which had poor staining
rowData(sce)$good_marker <- !grepl( "DNA|Histone|Vimentin|Ki67Pt198|CD19|TOX1",rownames(sce))Calculate UMAP
set.seed(12345)
# UMAP
start = Sys.time()
sce <- runUMAP(sce, exprs_values = "scaled_counts",
subset_row = rowData(sce)$good_marker)
end = Sys.time()
print(end-start)Time difference of 10.53498 minsSubset SCE for UMAP visualization
cur_sce <- sce[, colnames(sce) %in% sample(sce$cellID, round(length(sce$cellID)*0.05))]
cur_sce$ImageNumber <- as.character(cur_sce$ImageNumber)Visualize features on and UMAP
Next, we will visualize different quality features on these representations.
UMAP
# Select plots in list
p.list <- list()
#
p.list$ImageNumber <- dittoDimPlot(cur_sce, var = "ImageNumber", reduction.use = "UMAP", size = 0.5, legend.show = FALSE)
p.list$Mutation <- dittoDimPlot(cur_sce, var = "Mutation", reduction.use = "UMAP", size = 0.5)
p.list$Cancer_Stage <- dittoDimPlot(cur_sce, var = "Cancer_Stage", reduction.use = "UMAP", size = 0.5)
p.list$relapse <- dittoDimPlot(cur_sce, var = "relapse", reduction.use = "UMAP", size = 0.5)
p.list$Location <- dittoDimPlot(cur_sce, var = "Location", reduction.use = "UMAP", size = 0.5)
p.list$TissueType <- dittoDimPlot(cur_sce, var = "TissueType", reduction.use = "UMAP", size = 0.5)
p.list$MM_location_simplified <- dittoDimPlot(cur_sce, var = "MM_location_simplified", reduction.use = "UMAP", size = 0.5)
p.list$treatment_group_before_surgery <- dittoDimPlot(cur_sce, var = "treatment_group_before_surgery", reduction.use = "UMAP", size = 0.5)
plot_grid(plotlist = p.list, ncol = 4, rel_widths = c(1.5, 1, 1, 1))Warning: Removed 1394 rows containing missing values (geom_point).
Warning: Removed 1394 rows containing missing values (geom_point).
Warning: Removed 1394 rows containing missing values (geom_point).Warning: Removed 381 rows containing missing values (geom_point).
Warning: Removed 381 rows containing missing values (geom_point).
Warning: Removed 381 rows containing missing values (geom_point).
Marker Expression on UMAP
p.list <- list()
for(i in rownames(sce)[rowData(cur_sce)$good_marker]){
p.list[[i]] <- plotUMAP(cur_sce, colour_by = i, by_exprs_values = "asinh")
}
plot_grid(plotlist = p.list, ncol = 7)
Scaled Expression UMAP
p.list <- list()
for(i in rownames(sce)[rowData(cur_sce)$good_marker]){
p.list[[i]] <- plotUMAP(cur_sce, colour_by = i, by_exprs_values = "scaled_asinh")
}
plot_grid(plotlist = p.list, ncol = 7)
Save data
Save updated SCE object
saveRDS(sce, file = "data/data_for_analysis/sce_protein.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] dittoSeq_1.0.2 BiocParallel_1.22.0
[3] cowplot_1.1.1 ggridges_0.5.3
[5] viridis_0.5.1 viridisLite_0.3.0
[7] reshape2_1.4.4 CATALYST_1.12.2
[9] scater_1.16.2 ggplot2_3.3.3
[11] dplyr_1.0.2 SingleCellExperiment_1.12.0
[13] SummarizedExperiment_1.20.0 Biobase_2.50.0
[15] GenomicRanges_1.42.0 GenomeInfoDb_1.26.2
[17] IRanges_2.24.1 S4Vectors_0.28.1
[19] BiocGenerics_0.36.0 MatrixGenerics_1.2.0
[21] matrixStats_0.57.0 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] igraph_1.2.6 ConsensusClusterPlus_1.52.0
[7] splines_4.0.3 flowCore_2.0.1
[9] TH.data_1.0-10 digest_0.6.27
[11] htmltools_0.5.0 fansi_0.4.1
[13] magrittr_2.0.1 CytoML_2.0.5
[15] cluster_2.1.0 limma_3.44.3
[17] openxlsx_4.2.3 ComplexHeatmap_2.4.3
[19] RcppParallel_5.0.2 sandwich_3.0-0
[21] flowWorkspace_4.0.6 cytolib_2.0.3
[23] jpeg_0.1-8.1 colorspace_2.0-0
[25] ggrepel_0.9.0 haven_2.3.1
[27] xfun_0.20 crayon_1.3.4
[29] RCurl_1.98-1.2 jsonlite_1.7.2
[31] hexbin_1.28.2 graph_1.66.0
[33] survival_3.2-7 zoo_1.8-8
[35] glue_1.4.2 gtable_0.3.0
[37] nnls_1.4 zlibbioc_1.36.0
[39] XVector_0.30.0 GetoptLong_1.0.5
[41] DelayedArray_0.16.0 ggcyto_1.16.0
[43] car_3.0-10 BiocSingular_1.4.0
[45] Rgraphviz_2.32.0 shape_1.4.5
[47] abind_1.4-5 scales_1.1.1
[49] pheatmap_1.0.12 mvtnorm_1.1-1
[51] edgeR_3.30.3 Rcpp_1.0.5
[53] plotrix_3.7-8 clue_0.3-58
[55] foreign_0.8-81 rsvd_1.0.3
[57] FlowSOM_1.20.0 tsne_0.1-3
[59] RColorBrewer_1.1-2 ellipsis_0.3.1
[61] farver_2.0.3 pkgconfig_2.0.3
[63] XML_3.99-0.5 uwot_0.1.10
[65] utf8_1.1.4 locfit_1.5-9.4
[67] labeling_0.4.2 tidyselect_1.1.0
[69] rlang_0.4.10 later_1.1.0.1
[71] munsell_0.5.0 cellranger_1.1.0
[73] tools_4.0.3 cli_2.2.0
[75] generics_0.1.0 evaluate_0.14
[77] stringr_1.4.0 yaml_2.2.1
[79] knitr_1.30 fs_1.5.0
[81] zip_2.1.1 purrr_0.3.4
[83] RBGL_1.64.0 whisker_0.4
[85] xml2_1.3.2 compiler_4.0.3
[87] rstudioapi_0.13 beeswarm_0.2.3
[89] curl_4.3 png_0.1-7
[91] tibble_3.0.4 stringi_1.5.3
[93] RSpectra_0.16-0 forcats_0.5.0
[95] lattice_0.20-41 Matrix_1.3-2
[97] vctrs_0.3.6 pillar_1.4.7
[99] lifecycle_0.2.0 GlobalOptions_0.1.2
[101] RcppAnnoy_0.0.18 BiocNeighbors_1.6.0
[103] data.table_1.13.6 bitops_1.0-6
[105] irlba_2.3.3 httpuv_1.5.4
[107] R6_2.5.0 latticeExtra_0.6-29
[109] promises_1.1.1 gridExtra_2.3
[111] RProtoBufLib_2.0.0 rio_0.5.16
[113] vipor_0.4.5 codetools_0.2-18
[115] assertthat_0.2.1 MASS_7.3-53
[117] gtools_3.8.2 rprojroot_2.0.2
[119] rjson_0.2.20 withr_2.3.0
[121] multcomp_1.4-15 GenomeInfoDbData_1.2.4
[123] hms_0.5.3 ncdfFlow_2.34.0
[125] grid_4.0.3 rmarkdown_2.6
[127] DelayedMatrixStats_1.10.1 carData_3.0-4
[129] Rtsne_0.15 git2r_0.28.0
[131] base64enc_0.1-3 ggbeeswarm_0.6.0