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This file will load the single-cell data and store it in an SingleCellExperiment data container. In order to successfully run this script, several .csv files have to provided in the data folder of this repository.
library(data.table)
library(SingleCellExperiment)# load cell data
cells <- data.frame(fread(file = "data/data_for_analysis/rna/cell.csv",stringsAsFactors = FALSE))
# load image metadata as output from CellProfiler
image_mat <- read.csv(file = "data/data_for_analysis/rna/Image.csv", stringsAsFactors = FALSE)
# load panel information as exported from AirLab and modified to contain ilastik and full columns and nicely written Target names.
panel_mat <- read.csv(file = "data/data_for_analysis/rna/panel_mat.csv", stringsAsFactors = FALSE )
# load the channel order information. this information is created from the jupyter notebook in the tiffs folder for every single tiff that is created.
# since it is the same panel for all images we just select one single file. the order or the metals in this file is the actual channels order.
tags <- read.csv( "data/data_for_analysis/rna/20190731_ZTMA256.1_slide2_TH_s1_p14_r1_a1_ac_full.csv", header = FALSE)
# load clinical data and TMA data to link images with patients
clinical_mat <- read.csv(file = "data/data_for_analysis/rna/clinical_data_RNA.csv",stringsAsFactors = FALSE)cur_counts <- cells[,grepl("Intensity_MeanIntensityCorrected_FullStackFiltered",colnames(cells))]the single cell data needs to be multiplied with the scaling factor (16 bit)
cur_counts <- cur_counts * image_mat$Scaling_FullStack[1]
# order the channels according to channel number
channelNumber <- as.numeric(sub("^.*_c", "", colnames(cur_counts)))
cur_counts <- cur_counts[,order(channelNumber,decreasing = FALSE)]any cell that has more than 25 % of its Area in the tumor mask is considered as “TRUE” meaning inside the tumor.
tumor_mask <- cells$Intensity_MeanIntensity_tumormask * image_mat$Scaling_FullStack[1]
in_tumor <- tumor_mask > 0.25cell_meta <- DataFrame(CellNumber = cells$ObjectNumber,
ImageNumber = cells$ImageNumber,
Center_X = cells$Location_Center_X,
Center_Y = cells$Location_Center_Y,
Area = cells$AreaShape_Area,
MajorAxisLength = cells$AreaShape_MajorAxisLength,
MinorAxisLength = cells$AreaShape_MinorAxisLength,
NumberOfNeighbors = cells$Neighbors_NumberOfNeighbors_8,
in_tumor = in_tumor)
# add a unique cellID to each cell consisting of "dataset"+"ImageNumber"+"ObjectNumber"
cell_meta$cellID <- paste0("RNA_",cell_meta$ImageNumber, "_",cell_meta$CellNumber)
rownames(cell_meta) <- cell_meta$cellID
# order according to ImageNumber
cell_meta <- cell_meta[order(cell_meta$ImageNumber),]here we prepare all the metadata for the rows in the single cell experiment object (rowData)
# the channel numbers are the rownumbers in the "tags" file that we create above
tags$channel <- as.numeric(rownames(tags))
colnames(tags) <- c("Metal.Tag","channel")
# include the channel information in the panel metadata (panel_mat)
panel_mat <- merge(panel_mat,tags,by="Metal.Tag")
# now we order the panel metadata by channel. therefore we first modify the column names
panel_mat <- panel_mat[order(panel_mat$channel,decreasing = FALSE),]
# rename CD8a -> CD8 in Targets
panel_mat$Target[26] <- "CD8"
# assign rownames
rownames(panel_mat) <- panel_mat$Target# create the SCE object
sce <- SingleCellExperiment(assays = list(counts = t(cur_counts)))
# Set marker name as rownames and cellID as colnames
rownames(sce) <- rownames(panel_mat)
colnames(sce) <- rownames(cell_meta)
# add the column and row metadata
colData(sce) <- cell_meta
rowData(sce) <- panel_mat
# we also generate here the generically implemented "logcounts" as asinh transformed counts
assay(sce, "asinh") <- asinh(SingleCellExperiment::counts(sce))# order according to ImageNumber
clinical_mat <- clinical_mat[order(clinical_mat$ImageNumber),]
metadata(sce) <- as.list(clinical_mat)saveRDS(sce,file = "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] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[3] Biobase_2.54.0 GenomicRanges_1.46.1
[5] GenomeInfoDb_1.30.1 IRanges_2.28.0
[7] S4Vectors_0.32.3 BiocGenerics_0.40.0
[9] MatrixGenerics_1.6.0 matrixStats_0.61.0
[11] data.table_1.14.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] xfun_0.29 bslib_0.3.1 lattice_0.20-45
[4] vctrs_0.3.8 htmltools_0.5.2 yaml_2.2.2
[7] utf8_1.2.2 rlang_1.0.0 jquerylib_0.1.4
[10] later_1.3.0 pillar_1.7.0 glue_1.6.1
[13] GenomeInfoDbData_1.2.7 lifecycle_1.0.1 stringr_1.4.0
[16] zlibbioc_1.40.0 evaluate_0.14 knitr_1.37
[19] callr_3.7.0 fastmap_1.1.0 httpuv_1.6.5
[22] ps_1.6.0 fansi_1.0.2 Rcpp_1.0.8
[25] promises_1.2.0.1 DelayedArray_0.20.0 jsonlite_1.7.3
[28] XVector_0.34.0 fs_1.5.2 digest_0.6.29
[31] stringi_1.7.6 processx_3.5.2 getPass_0.2-2
[34] grid_4.1.2 rprojroot_2.0.2 cli_3.1.1
[37] tools_4.1.2 bitops_1.0-7 magrittr_2.0.2
[40] sass_0.4.0 RCurl_1.98-1.5 tibble_3.1.6
[43] crayon_1.4.2 whisker_0.4 pkgconfig_2.0.3
[46] Matrix_1.4-0 ellipsis_0.3.2 rmarkdown_2.11
[49] httr_1.4.2 rstudioapi_0.13 R6_2.5.1
[52] git2r_0.29.0 compiler_4.1.2