Last updated: 2020-09-09
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Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 88fccd1 | khembach | 2020-09-09 | Prepare sce object for conos |
library(dplyr)
library(SingleCellExperiment)sce_org <- readRDS(file.path("output", "sce_organoid-01-clustering.rds"))
sce_org <- sce_org[, sce_org$in_FullLineage]sce_nsc <- readRDS(file.path("output", "sce_03_filtering_all_genes.rds"))
## convert to "dgCMatrix" as input for Pagoda2
counts(sce_nsc) <- as(counts(sce_nsc), "dgCMatrix")## intersection of measured features
rdat <- inner_join(data.frame(rowData(sce_org)), data.frame(rowData(sce_nsc)),
by = "ensembl_id", suffix = c(".org", ".nsc"))
## subset rows to intersection and harmonize row data and names
sce_org <- sce_org[paste0(rdat$ensembl_id, ".", rdat$symbol.org),]
sce_nsc <- sce_nsc[paste0(rdat$ensembl_id, ".", rdat$symbol.nsc),]
rdat <- rdat %>% dplyr::select(-symbol.org) %>% rename(symbol.nsc = "symbol")
rowData(sce_org) <- rdat
rowData(sce_nsc) <- rdat
rownames(sce_org) <- rownames(sce_nsc)
## subset matching columns
cdat_nsc <- colData(sce_nsc)
cdat_org <- colData(sce_org)
## harmonize the colData
## we keep following columns
## sample_id, barcode, group_id, sum, detected, subsets_Mt_fraction
cdat_nsc <- cdat_nsc[, c("sample_id", "barcode", "group_id", "sum",
"detected", "subsets_Mt_fraction")]
## sample_id, barcode, Species, Stage, Line, Sample, PredCellType, nGene, nUMI,
## PercentMito, cl_FullLineage cl_LineComp
## nsc = org --> matching columns that need to be renamed
## group_id = Stage
## sample_id = Sample
## detected = nGene
## sum = nUMI
## subsets_Mt_fraction = PercentMito
cdat_org <- cdat_org[, c("barcode", "Stage", "Line",
"Sample", "PredCellType", "nGene", "nUMI",
"PercentMito", "cl_FullLineage", "cl_LineComp")]
## rename columns to match the two dataframes
cdat_org <- cdat_org %>% rename(Sample = "sample_id", Line = "group_id")
cdat_nsc <- cdat_nsc %>% rename(sum = "nUMI", detected = "nGene",
subsets_Mt_fraction = "PercentMito")
cdat_nsc[,c("Stage", "PredCellType", "cl_FullLineage", "cl_LineComp")] <- NA
## reorder the columns
cdat_org <- cdat_org[, colnames(cdat_nsc)]
colData(sce_nsc) <- cdat_nsc
colData(sce_org) <- cdat_org
## combine the two sce objects
sce <- cbind(sce_nsc, sce_org)
saveRDS(sce, file.path("output", "sce_06-1-prepare-sce.rds"))
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/lib/libRlapack.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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] HDF5Array_1.16.1 rhdf5_2.32.2
[3] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[5] DelayedArray_0.14.0 matrixStats_0.56.0
[7] Biobase_2.48.0 GenomicRanges_1.40.0
[9] GenomeInfoDb_1.24.2 IRanges_2.22.2
[11] S4Vectors_0.26.1 BiocGenerics_0.34.0
[13] dplyr_1.0.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 XVector_0.28.0 pillar_1.4.6
[4] compiler_4.0.0 later_1.1.0.1 git2r_0.27.1
[7] zlibbioc_1.34.0 bitops_1.0-6 tools_4.0.0
[10] digest_0.6.25 lattice_0.20-41 evaluate_0.14
[13] lifecycle_0.2.0 tibble_3.0.3 pkgconfig_2.0.3
[16] rlang_0.4.7 Matrix_1.2-18 yaml_2.2.1
[19] xfun_0.15 GenomeInfoDbData_1.2.3 stringr_1.4.0
[22] knitr_1.29 generics_0.0.2 fs_1.4.2
[25] vctrs_0.3.4 grid_4.0.0 rprojroot_1.3-2
[28] tidyselect_1.1.0 glue_1.4.2 R6_2.4.1
[31] rmarkdown_2.3 Rhdf5lib_1.10.0 purrr_0.3.4
[34] magrittr_1.5 whisker_0.4 backports_1.1.9
[37] promises_1.1.1 ellipsis_0.3.1 htmltools_0.5.0
[40] httpuv_1.5.4 stringi_1.4.6 RCurl_1.98-1.2
[43] crayon_1.3.4