Stathmin 2 expression
Katharina Hembach
21/01/2021
Last updated: 2021-02-19
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 4ddbb0d | khembach | 2021-02-19 | text size |
| html | a7c4a5b | khembach | 2021-02-18 | Build site. |
| Rmd | 1755a27 | khembach | 2021-02-18 | plot stathmin2 read coverage of cells from cluster 12 |
Load packages
library(Seurat)
library(SingleCellExperiment)
library(dplyr)
library(Gviz)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(Rsamtools)
library(GenomicAlignments)Load data & convert to SCE
# so <- readRDS(file.path("output", "so_TDP-06-cluster-analysis.rds"))
so <- readRDS(file.path("output", "so_TDP_05_plasmid_expression.rds"))
so <- SetIdent(so, value = "RNA_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)Cells from cluster 12
We want to compare the stathmin2 read coverage of cells expressing TDP-HA (from cluster 12) and other neuronal cells without TDP-HA expression. For this, we randomly select 5 cells from each group and filter the corresponding stathmin2 reads from the BAM file.
clus12 <- subset(so, subset = cluster_id == "12")
## from which sample do the cells come from?
clus12$sample_id %>% table.
TDP2wON TDP4wOFF TDP4wONa TDP4wONb
97 3 88 36 ## what is the range of TDP-HA expression in all cells in cluster 12?
dat_ha <- GetAssayData(object = clus12, slot = "data")["TDP43-HA",]
summary(dat_ha) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.226 1.771 1.800 2.454 4.112 ## select cells with high TDP-HA expression
high <- clus12$barcode[which(dat_ha > 3.5)]
high ACCATTTAGGCTCCCA-1.TDP2wON ATAGACCAGCATTTCG-1.TDP2wON
"ACCATTTAGGCTCCCA-1" "ATAGACCAGCATTTCG-1"
CTTCTCTAGCCTATCA-1.TDP2wON GCTGGGTTCCCGAGAC-1.TDP4wONa
"CTTCTCTAGCCTATCA-1" "GCTGGGTTCCCGAGAC-1"
TCACATTAGTCCGTCG-1.TDP4wONa
"TCACATTAGTCCGTCG-1" ## cells with low TDP-HA expression
low <- clus12$barcode[which(dat_ha < 0.5 & dat_ha > 0)]
low ACTATGGAGCCATCCG-1.TDP2wON AGCCACGGTGTACATC-1.TDP2wON
"ACTATGGAGCCATCCG-1" "AGCCACGGTGTACATC-1"
CAACGATTCCCGTGAG-1.TDP2wON AACCCAACATTCTTCA-1.TDP4wOFF
"CAACGATTCCCGTGAG-1" "AACCCAACATTCTTCA-1"
CTAGACAAGATCACCT-1.TDP4wONa GCATCTCGTAGTATAG-1.TDP4wONa
"CTAGACAAGATCACCT-1" "GCATCTCGTAGTATAG-1"
GGAGAACGTAAGATTG-1.TDP4wONa
"GGAGAACGTAAGATTG-1" Get stathmin2 reads of selected cells
We extract the reads covering the stathmin2 genes of the selected cells.
bams <- list(TDP4wOFF = file.path("data", "Sep2020", "CellRangerCount_50076_2020-09-22--15-40-54",
"no1_Neural_cuture_d_96_TDP-43-HA_4w_DOXoff",
"possorted_genome_bam.bam"),
TDP2wON = file.path("data", "Sep2020", "CellRangerCount_50076_2020-09-22--15-40-54",
"no2_Neural_cuture_d_96_TDP-43-HA_2w_DOXON",
"possorted_genome_bam.bam"),
TDP4wONa = file.path("data", "Sep2020", "CellRangerCount_50076_2020-09-22--15-40-54",
"no3_Neural_cuture_d_96_TDP-43-HA_4w_DOXONa",
"possorted_genome_bam.bam"),
TDP4wONb = file.path("data", "Sep2020", "CellRangerCount_50076_2020-09-22--15-40-54",
"no4_Neural_cuture_d_96_TDP-43-HA_4w_DOXONb",
"possorted_genome_bam.bam"))
chr <- "chr8"
region_start <- 79611100
region_end <- 79666200
stmn2 <- GRanges(chr, IRanges(region_start, region_end), "+")
# keep all reads from cells in cluster 12
param <- ScanBamParam(which=stmn2, what = c("qname"), tag = "CB",
tagFilter = list(CB = clus12$barcode))
gals <- lapply(bams, function(x) {
readGAlignments(x, use.names = TRUE, param=param)
})
covs <- lapply(gals, coverage)Ploting with Gviz
Plot the stathmin2 transcripts and the read coverage of all cells from cluster 12.
## gene annotations from UCSC
options(ucscChromosomeNames = FALSE)
eTrack <- GeneRegionTrack(TxDb.Hsapiens.UCSC.hg38.knownGene,
chromosome = chr, start = region_start,
end = region_end, name = "annotation")
# covs[[1]]$chr8[region_start:region_end]
coords <- 79611100:79666201
dat <- matrix(c(as.vector(covs[[1]]$chr8[region_start:region_end]),
as.vector(covs[[2]]$chr8[region_start:region_end]),
as.vector(covs[[3]]$chr8[region_start:region_end]),
as.vector(covs[[4]]$chr8[region_start:region_end])),
nrow = 4, byrow = TRUE)
rownames(dat) <- names(covs)
dtrack <- DataTrack(data = dat,
start = coords[-length(coords)], end = coords[-1], chromosome = chr,
genome = "hg38")
plotTracks(c(dtrack, eTrack),
# collapseTranscripts="meta",
type = "histogram", showSampleNames = TRUE,
# chromosome = chr, from = region_start, to = region_end,
shape = "arrow", geneSymbols = TRUE, aggregateGroups=FALSE,
groups = c("TDP4wOFF", "TDP2wON", "TDP4wONa", "TDP4wONb"),
stackedBars = FALSE, fontsize=13
)
| Version | Author | Date |
|---|---|---|
| a7c4a5b | khembach | 2021-02-18 |
## one data track per sample
dats <- list("4wOFF" = matrix(as.vector(covs[[1]]$chr8[region_start:region_end]),
nrow = 1, byrow = TRUE),
"2wON" = matrix(as.vector(covs[[2]]$chr8[region_start:region_end]),
nrow = 1, byrow = TRUE),
"4wONa" = matrix(as.vector(covs[[3]]$chr8[region_start:region_end]),
nrow = 1, byrow = TRUE),
"4wONb" = matrix(as.vector(covs[[4]]$chr8[region_start:region_end]),
nrow = 1, byrow = TRUE))
# dats <- lapply(seq_along(dats), function(x) {rownames(dats[[x]]) <- names(dats)[x]; dats[[x]]})
# rownames(dat) <- names(covs)
dtrack_4wOFF <- DataTrack(data = dats[[1]],
start = coords[-length(coords)], end = coords[-1], chromosome = chr,
genome = "hg38", name = "4wOFF")
dtrack_2wON <- DataTrack(data = dats[[2]],
start = coords[-length(coords)], end = coords[-1], chromosome = chr,
genome = "hg38", name = "2wON")
dtrack_4wONa <- DataTrack(data = dats[[3]],
start = coords[-length(coords)], end = coords[-1], chromosome = chr,
genome = "hg38", name = "4wONa")
dtrack_4wONb <- DataTrack(data = dats[[4]],
start = coords[-length(coords)], end = coords[-1], chromosome = chr,
genome = "hg38", name = "4wONb")
plotTracks(c(dtrack_4wOFF, dtrack_2wON, dtrack_4wONa, dtrack_4wONb, eTrack),
# collapseTranscripts="meta",
type = "histogram", showSampleNames = TRUE,
# chromosome = chr, from = region_start, to = region_end,
shape = "arrow", geneSymbols = TRUE, aggregateGroups=FALSE,
# groups = c("TDP4wOFF", "TDP2wON", "TDP4wONa", "TDP4wONb"),
stackedBars = FALSE, fontsize=13
)
| Version | Author | Date |
|---|---|---|
| a7c4a5b | khembach | 2021-02-18 |
## zoom into intron 1 that contains the cryptic exon
chr <- "chr8"
region_start <- 79611100
region_end <- 79637000
plotTracks(c(dtrack_4wOFF, dtrack_2wON, dtrack_4wONa, dtrack_4wONb, eTrack),
# collapseTranscripts="meta",
type = "histogram", showSampleNames = TRUE,
chromosome = chr, from = region_start, to = region_end,
shape = "arrow", geneSymbols = TRUE, aggregateGroups=FALSE,
# groups = c("TDP4wOFF", "TDP2wON", "TDP4wONa", "TDP4wONb"),
stackedBars = FALSE, fontsize=13
)
| Version | Author | Date |
|---|---|---|
| a7c4a5b | khembach | 2021-02-18 |
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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] GenomicAlignments_1.24.0
[2] Rsamtools_2.4.0
[3] Biostrings_2.56.0
[4] XVector_0.28.0
[5] TxDb.Hsapiens.UCSC.hg38.knownGene_3.10.0
[6] GenomicFeatures_1.40.0
[7] AnnotationDbi_1.50.1
[8] Gviz_1.32.0
[9] dplyr_1.0.2
[10] SingleCellExperiment_1.10.1
[11] SummarizedExperiment_1.18.1
[12] DelayedArray_0.14.0
[13] matrixStats_0.56.0
[14] Biobase_2.48.0
[15] GenomicRanges_1.40.0
[16] GenomeInfoDb_1.24.2
[17] IRanges_2.22.2
[18] S4Vectors_0.26.1
[19] BiocGenerics_0.34.0
[20] Seurat_3.1.5
[21] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.9 Hmisc_4.4-1 BiocFileCache_1.12.0
[4] plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2
[7] splines_4.0.0 BiocParallel_1.22.0 listenv_0.8.0
[10] ggplot2_3.3.2 digest_0.6.25 ensembldb_2.12.1
[13] htmltools_0.5.0 checkmate_2.0.0 magrittr_1.5
[16] memoise_1.1.0 BSgenome_1.56.0 cluster_2.1.0
[19] ROCR_1.0-11 globals_0.12.5 askpass_1.1
[22] prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_1.4-1
[25] blob_1.2.1 rappdirs_0.3.1 ggrepel_0.8.2
[28] xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2
[31] jsonlite_1.7.0 VariantAnnotation_1.34.0 survival_3.2-3
[34] zoo_1.8-8 ape_5.4 glue_1.4.2
[37] gtable_0.3.0 zlibbioc_1.34.0 leiden_0.3.3
[40] future.apply_1.6.0 scales_1.1.1 DBI_1.1.0
[43] Rcpp_1.0.5 htmlTable_2.0.1 viridisLite_0.3.0
[46] progress_1.2.2 reticulate_1.16 foreign_0.8-80
[49] bit_1.1-15.2 rsvd_1.0.3 Formula_1.2-3
[52] tsne_0.1-3 htmlwidgets_1.5.1 httr_1.4.1
[55] RColorBrewer_1.1-2 ellipsis_0.3.1 ica_1.0-2
[58] pkgconfig_2.0.3 XML_3.99-0.4 nnet_7.3-14
[61] uwot_0.1.8 dbplyr_1.4.4 tidyselect_1.1.0
[64] rlang_0.4.7 reshape2_1.4.4 later_1.1.0.1
[67] munsell_0.5.0 tools_4.0.0 generics_0.0.2
[70] RSQLite_2.2.0 ggridges_0.5.2 evaluate_0.14
[73] stringr_1.4.0 yaml_2.2.1 knitr_1.29
[76] bit64_0.9-7 fs_1.4.2 fitdistrplus_1.1-1
[79] purrr_0.3.4 RANN_2.6.1 AnnotationFilter_1.12.0
[82] pbapply_1.4-2 future_1.17.0 nlme_3.1-148
[85] whisker_0.4 biomaRt_2.44.1 rstudioapi_0.11
[88] compiler_4.0.0 plotly_4.9.2.1 curl_4.3
[91] png_0.1-7 tibble_3.0.3 stringi_1.4.6
[94] lattice_0.20-41 ProtGenerics_1.20.0 Matrix_1.2-18
[97] vctrs_0.3.4 pillar_1.4.6 lifecycle_0.2.0
[100] lmtest_0.9-37 RcppAnnoy_0.0.16 data.table_1.12.8
[103] cowplot_1.0.0 bitops_1.0-6 irlba_2.3.3
[106] httpuv_1.5.4 patchwork_1.0.1 rtracklayer_1.48.0
[109] R6_2.4.1 latticeExtra_0.6-29 promises_1.1.1
[112] KernSmooth_2.23-17 gridExtra_2.3 codetools_0.2-16
[115] dichromat_2.0-0 MASS_7.3-51.6 assertthat_0.2.1
[118] openssl_1.4.2 rprojroot_1.3-2 sctransform_0.2.1
[121] GenomeInfoDbData_1.2.3 hms_0.5.3 rpart_4.1-15
[124] tidyr_1.1.0 rmarkdown_2.3 Rtsne_0.15
[127] biovizBase_1.36.0 git2r_0.27.1 base64enc_0.1-3