Last updated: 2018-12-02

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Number of guide RNAs per cell

  • number of cells with guide RNA =1 From Siwei’s cellranger run:
library(Matrix)
matrix_dir = "/Volumes/CROP-seq/data_from_Siwei/Xin_scRNA_seq_05Nov2018/filtered_gene_bc_matrices/CellRanger_index/"
matrix.path <- paste0(matrix_dir, "matrix.mtx")
dm <- readMM(file = matrix.path)
dm1 <- tail(dm,n=76)
length(colSums(dm1)[colSums(dm1)==1])
[1] 440

From Alan’s cellranger run:

matrix_dir1 = "/Volumes/CROP-seq/NSC0507_cellranger/outs/filtered_gene_bc_matrices/cellranger_ref/"
matrix.path1 <- paste0(matrix_dir1, "matrix.mtx")
mattemp1 <- readMM(file = matrix.path1)
mattemp11 <- tail(mattemp1,n=76)
length(colSums(mattemp11)[colSums(mattemp11)==1])
[1] 266
matrix_dir2 = "/Volumes/CROP-seq/NSC08_cellranger/outs/filtered_gene_bc_matrices/cellranger_ref/"
matrix.path2 <- paste0(matrix_dir2, "matrix.mtx")
mattemp2 <- readMM(file = matrix.path2)
mattemp21 <- tail(mattemp2,n=76)
length(colSums(mattemp21)[colSums(mattemp21)==1])
[1] 190

Note: in Alan’s original analysis conversion from h5 to csv step didn’t seem to work properly. if starting from matrix.mtx files. Siwei and Alan’s analyses gave the same results. So from now on, we will always start from Siwei’s matrix.mtx file.

  • distribution of gRNA number per cell
barcode.path <- paste0(matrix_dir, "barcodes.tsv")
features.path <- paste0(matrix_dir, "genes.tsv")
feature.names = read.delim(features.path, header = FALSE,
                           stringsAsFactors = FALSE)
barcode.names = read.delim(barcode.path, header = FALSE,
                           stringsAsFactors = FALSE)
colnames(dm) = barcode.names$V1
rownames(dm) = feature.names$V1
dm1 <- tail(dm,n=76)
hist(colSums(dm1),breaks=3000,xlim=c(0,30),ylim=c(0,450), main="Distribution of gRNA number per cell", xlab= "#gRNA per cell")

Expand here to see past versions of gRNAdist-1.png:
Version Author Date
fdd5647 szhao06 2018-12-01

number of cells targeted for each locus

library(dplyr)
dm1df <- as.data.frame(as.matrix(dm1))
dm1df$label = sapply(strsplit(rownames(dm1),split = '_'), function(x){x[1]})
dm1dfagg = as.data.frame(dm1df %>% group_by(label) %>% summarise_all(funs(sum)))
row.names(dm1dfagg) =dm1dfagg$label
dm1dfagg$label =NULL
  • number of cells targeted for each locus
ncell <- apply(dm1dfagg,1, function (x) length(x[x>=1]))
barplot(ncell,las=2,cex.lab=1, main= "# cells targted for each locus")

Expand here to see past versions of celldist1-1.png:
Version Author Date
8754cad szhao06 2018-12-02
fdd5647 szhao06 2018-12-01

  • number of cells only targeted for that locus
# Singletons (cells with only 1 gRNA)
nlocus <- apply(dm1dfagg, 2, function (x) length(x[x>=1]))
hist(nlocus,breaks=100, main="number of targeted locus each cell")

Expand here to see past versions of unnamed-chunk-1-1.png:
Version Author Date
8754cad szhao06 2018-12-02 

dm1dfagg.uni= dm1dfagg[,nlocus==1]

ncell.uni <- apply(dm1dfagg.uni,1, function (x) length(x[x>=1]))
barplot(ncell.uni,las=2,cex.lab=1,main= "# cells uniquely targted for each locus")

Expand here to see past versions of unnamed-chunk-1-2.png:
Version Author Date
8754cad szhao06 2018-12-02

UMI count distribution for cells with unique targeted locus

# Singletons (cells with only 1 targeted locus)
dm.uni <- dm[,nlocus==1]
nUMI <- colSums(dm.uni)
hist(nUMI,breaks=100,xlim=c(0,1e5))

Expand here to see past versions of UMI-1.png:
Version Author Date
8754cad szhao06 2018-12-02

Prepare data for differential gene expression

save(dm1,dm1dfagg,nlocus, file="data/DE_input.Rd")

Parameters used:

  • for a cell to be considered targeted uniquely at a locus: total read counts for the 3 gRNAs targeting that locus >1, total read counts for gRNA of other locus=0.

  • negative control: neg_EGFP and neg_CTRL are pooled together.

  • cells to be exluded due to low total UMI count: no filtering

Session information

sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin11.4.2 (64-bit)
Running under: OS X El Capitan 10.11.6

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_0.7.5   Matrix_1.2-10

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.17      knitr_1.20        bindr_0.1.1      
 [4] whisker_0.3-2     magrittr_1.5      workflowr_1.1.1  
 [7] tidyselect_0.2.4  lattice_0.20-35   R6_2.2.2         
[10] rlang_0.2.1       stringr_1.2.0     tools_3.3.2      
[13] grid_3.3.2        R.oo_1.22.0       git2r_0.18.0     
[16] htmltools_0.3.6   yaml_2.1.16       rprojroot_1.2    
[19] digest_0.6.12     assertthat_0.2.0  tibble_1.4.2     
[22] bindrcpp_0.2.2    purrr_0.2.5       R.utils_2.7.0    
[25] glue_1.2.0        evaluate_0.10     rmarkdown_1.10   
[28] stringi_1.1.5     pillar_1.2.3      backports_1.0.5  
[31] R.methodsS3_1.7.1 pkgconfig_2.0.1

This reproducible R Markdown analysis was created with workflowr 1.1.1