Last updated: 2022-10-01

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/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot4.0_Lenti/01_fasta/ ../output/pilot4.0_Lenti/01_fasta
/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot4.0_Lenti/02_blast/ ../output/pilot4.0_Lenti/02_blast
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/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot4.0_Lenti/03_keys/ ../output/pilot4.0_Lenti/03_keys
/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot4.0_iPSC_incIntrons/03_vireo-TX/C099_iPSC_GEX/donor_ids.tsv ../output/pilot4.0_iPSC_incIntrons/03_vireo-TX/C099_iPSC_GEX/donor_ids.tsv

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Goal

Attribute lenti barcodes (experiment S000312; R1=cell barcodes; R2=lenti barcodes) with iPSC cell barcodes/demultiplexed donor IDs (pilot #4 exp ID: C099).

Blastn results

blastn strategy

Example scripts:

14 bp barcode: blastn -db output/pilot4_Lenti/00_sequences/bc14_forward.fa -query output/pilot4_Lenti/01_fasta/S000312-Lenti.fa -query_loc 26-40 -word_size 5 -max_hsps 1 -evalue 0.1 -num_threads 4 -outfmt 6 -out output/pilot4_Lenti/02_blast/S000312-Lenti_bc14_blast.out

30 bp barcode: blastn -db output/pilot4_Lenti/00_sequences/bc30_forward.fa -query output/pilot4_Lenti/01_fasta/S000312-Lenti.fa -query_loc 46-76 -word_size 10 -max_hsps 1 -evalue 0.1 -max_target_seqs 1 -outfmt 6 -out output/pilot4_Lenti/02_blast/S000312-Lenti_bc30_blast.out

Read in blastn hits and remove ambiguous matches by first keeping the longest hit for each read and if two lenti barcodes have equally long hits, removing the read entirely. Number of reads with lenti barcode matches:

load_parse_blastn <- function(path) {
  df <- fread(path, header=FALSE, select = c(1:6))
  names(df) <- c("qseqid", "sseqid", "pident", "length", "mismatch", "gapopen")
  # If >1 hit for a lenti barcode, keep the one(s) with the longer overlap
  df <- df[df[, .I[length == max(length)], by=qseqid]$V1]
  # If >1 hit for a lenti barcode with equal lengths, remove them both.
  df <- df[!(duplicated(df$qseqid) | duplicated(df$qseqid, fromLast = TRUE)), ]
  return(df)
}

## Load and process blastn results

bc_header <- load_parse_blastn(paste0(blast_out, "S000312_header_blast.out"))
p3_c4_header <- load_parse_blastn(paste0(blast_out_p3, "Capture4-Lenti_header_blast.out"))
p3_c5_header <- load_parse_blastn(paste0(blast_out_p3, "Capture5-Lenti_header_blast.out"))

p1 <- gghistogram(bc_header, x="length", bins=27)
p2 <- gghistogram(p3_c4_header, x="length", bins=27)
p3 <- gghistogram(p3_c5_header, x="length", bins=27)

plot_grid(p1, p2, p3, ncol=3, labels="auto")
Distribution of blastn hit lengths for the 25 bp header sequence for (a) this analysis compared to (b) Capture 4 and (c) Capture 5 from the first Lenti-barcode test (i.e. pilot 3).

Distribution of blastn hit lengths for the 25 bp header sequence for (a) this analysis compared to (b) Capture 4 and (c) Capture 5 from the first Lenti-barcode test (i.e. pilot 3). 

rm(p3_c4_header, p3_c5_header)

Compared to the last Lenti barcode analysis, these reads have very low quality headers.

bc_bc14 <- load_parse_blastn(paste0(blast_out, "S000312_bc14_blast.out"))
p3_bc14 <- load_parse_blastn(paste0(blast_out_p3, "Capture5-Lenti_bc14_blast.out"))

p1 <- gghistogram(bc_header, x="length", bins=15)
p2 <- gghistogram(p3_bc14, x="length", bins=15)

plot_grid(p1, p2, ncol=2, labels="auto")
Distribution of blastn hit lengths for the 14 bp barcode sequence for (a) this analysis compared to (b) Capture 5 from the first Lenti-barcode test (i.e. pilot 3).

Distribution of blastn hit lengths for the 14 bp barcode sequence for (a) this analysis compared to (b) Capture 5 from the first Lenti-barcode test (i.e. pilot 3). 

rm(p3_bc14)
bc_bc30 <- load_parse_blastn(paste0(blast_out, "S000312_bc30_blast.out"))
p3_bc30<- load_parse_blastn(paste0(blast_out_p3, "Capture5-Lenti_bc30_blast.out"))

p1 <- gghistogram(bc_bc30, x="length", bins=32)
p2 <- gghistogram(p3_bc30, x="length", bins=32)

plot_grid(p1, p2, ncol=2, labels="auto")
Distribution of blastn hit lengths for the 30 bp barcode sequence for (a) this analysis compared to (b) Capture 5 from the first Lenti-barcode test (i.e. pilot 3).

Distribution of blastn hit lengths for the 30 bp barcode sequence for (a) this analysis compared to (b) Capture 5 from the first Lenti-barcode test (i.e. pilot 3). 

rm(p3_bc30)

Again, in the first test the 14 and 30 bp barcode blast hits were consistently longer than the hits for the current analysis.

# Merge
n_reads <- 474065418
S000312_both <- bc_bc14 %>% inner_join(bc_bc30, by = "qseqid",  suffix = c(".bc14", ".bc30"))

message("Of the ", n_reads, 
        " sequenced, the number with lenti barcode matches (no additional filtering) for: \nbc14: ", 
        nrow(bc_bc14), " (", round(nrow(bc_bc14)/n_reads*100, 4), "%)", 
        "\nbc30: ", nrow(bc_bc30), " (", round(nrow(bc_bc30)/n_reads*100, 4), "%)", 
        "\nBoth bc14 and bc30: ", nrow(S000312_both), " (", 
        round(nrow(S000312_both)/n_reads*100, 4), "%)")
Of the 474065418 sequenced, the number with lenti barcode matches (no additional filtering) for: 
bc14: 69894072 (14.7436%)
bc30: 2526639 (0.533%)
Both bc14 and bc30: 435375 (0.0918%)
rm(bc_bc14, bc_bc30, bc_header)
gc()
            used   (Mb) gc trigger    (Mb)   max used    (Mb)
Ncells  49237319 2629.6  205192234 10958.5  256490292 13698.1
Vcells 878264420 6700.7 2939757056 22428.6 3659485460 27919.7

Note that in the pilot 3 analysis for capture 5 there were 46,041,484 reads, 17,260,005 with bc14 hits, 12,795,040 with bc30 hits… with 12,544,555 reads having a hit for both bc14 and bc30 (27% of total reads).

h1 <- gghistogram(S000312_both, x = "length.bc14")
Warning: Using `bins = 30` by default. Pick better value with the argument
`bins`.
h2 <- gghistogram(S000312_both, x = "length.bc30")
Warning: Using `bins = 30` by default. Pick better value with the argument
`bins`.
h_plots <- plot_grid(h1, h2, nrow=1)
p3 <- ggboxplot(S000312_both, x="length.bc14", y = "length.bc30", outlier.shape = NA)

plot_grid(h_plots, p3, ncol=1, labels=c("", "c"))
Distribution of (a) bc14 and (b) bc30 blast hit lengths and (c) the relationship between the two across reads.

Distribution of (a) bc14 and (b) bc30 blast hit lengths and (c) the relationship between the two across reads.

Filter and merge with cell barcodes

For the last pilot I used “strict” minimum hit length cutoffs of 12 and 26 and an max missmatch rate of 1 and 2 for the bc14 and bc30 blast hits, respectively. However given the lower quality I’m going to be more lenient for this analysis and require lengths 10 and 22 and no max mismatch rate.

# Add lenti barcode IDs
S000312_both$lenti_bc <- paste0("lenti_", S000312_both$sseqid.bc14, "-", S000312_both$sseqid.bc30)

# Filtering by minimum length
S000312_both_filt <- subset(S000312_both, S000312_both$length.bc14 >= 10 & S000312_both$length.bc30 >= 22)

message("Number of barcodes meeting this relatively loose threshold: ", nrow(S000312_both_filt))
Number of barcodes meeting this relatively loose threshold: 14365

Note that in the first analysis, 12,411,301 reads from capture 5 met this criteria.

Merge with cell barcodes from the R1 reads

The R1 reads are all exactly 28 bp and contain the 16 bp cell barcode followed by a 12 bp UMI.

parse_merge_R1_cellbarcodes <- function(path, lenti) {
  cbc <- fread(path, header=FALSE, sep="\t")
  summary(nchar(cbc$cell_barcode))
  names(cbc) <- c("qseqid", "R1")
  cbc$qseqid <- gsub(">", "", gsub(" .*", "", cbc$qseqid))
  cbc <- separate(cbc, R1, into = c("cell_barcode", "umi"), sep = 16)
  lenti <- lenti %>% left_join(cbc, by = "qseqid")
  rm(cbc)
  return(lenti)
}

if(rerun){
  S000312_both_filt <- parse_merge_R1_cellbarcodes("/mnt/mcfiles/Datasets/BAUH_2020_MND-single-cell/pilot4_20220710/LentiBarcodes/S000312/G000099_iPSC_GEX_S1_L001_R1_001.tsv", S000312_both_filt)
  write.table(S000312_both_filt, paste0(out, "S000312-Lenti_merged_barcodes.tsv"), 
                      sep = "\t", quote=FALSE, row.names = FALSE)
} else{
  S000312_both_filt <- read.table(paste0(out, "S000312-Lenti_merged_barcodes.tsv"), 
                      sep = "\t", header=TRUE)
}

Make lenti2cell barcode keys

How many UMIs support each cell barcode to lenti barcode pair….

S000312_both_filt_counts <- S000312_both_filt %>% distinct(cell_barcode, lenti_bc, umi) %>%
  group_by(cell_barcode, lenti_bc) %>%
  tally() 

table(S000312_both_filt_counts$n)

    1     2     3     4 
10046   118     1     2

Most pairs have just one UMI supporting the match. Because each cell can have more than 1 lenti barcode, we expect multiple lenti barcodes per cell… Here is the distribution of the number of lenti-barcodes matching to each cell barcode:

lenti_barcode_key <- as.list(unstack(rev(S000312_both_filt[, c("cell_barcode", "lenti_bc")])))


hist(unlist(lapply(lenti_barcode_key, FUN=function(x) length(x))),
     main = "lenti barcodes per cell", xlab = "# lenti barcodes")

And here is the distribution of the number of cell barcodes matching to each lenti barcode:

lenti_barcode_key_rev <- as.list(unstack(rev(S000312_both_filt[, c("lenti_bc", "cell_barcode")])))


lenti_barcode_key_rev_counts <- unlist(lapply(lenti_barcode_key_rev, FUN=function(x) length(unique(x))))
hist(lenti_barcode_key_rev_counts,
     main = "cell barcodes per lenti barcode", xlab = "# cell barcodes")

message("Zooming in on lenti-barcodes with > 10 cell barcode matches.")
Zooming in on lenti-barcodes with > 10 cell barcode matches.
hist(lenti_barcode_key_rev_counts[lenti_barcode_key_rev_counts >10 ],
     main = "cell barcodes per lenti barcode", xlab = "# cell barcodes")

For these Lenti barcodes that match to more than 1 cell barcode, we expect them to be assigned the same donor…

Compare with iPSC donor IDs

p4_ipsc_donors <- read.table("/mnt/beegfs/mccarthy/backed_up/general/cazodi/Projects/BAUH_2020_MND-single-cell/output/pilot4.0_iPSC_incIntrons/03_vireo-TX/C099_iPSC_GEX/donor_ids.tsv", sep="\t", header=TRUE) 
p4_ipsc_donors <- subset(p4_ipsc_donors, ! donor_id %in% c("unassigned", "doublet"))
p4_ipsc_donors$cell_barcode <- gsub("-1", "", p4_ipsc_donors$cell)

counts <- table(p4_ipsc_donors$cell_barcode %in% S000312_both_filt$cell_barcode)
message("Of the ", nrow(p4_ipsc_donors), " QCed and donor assigned cells, ", 
        counts[["TRUE"]], 
        " (", round(counts[["TRUE"]]/nrow(p4_ipsc_donors)*100, 1), 
        "%) have lenti-barcode matches.")
Of the 10358 QCed and donor assigned cells, 3400 (32.8%) have lenti-barcode matches.

How specific are lenti-barcodes to cells assigned to the same donor?

results <- S000312_both_filt %>% left_join(p4_ipsc_donors, by="cell_barcode")

matches <- results[complete.cases(results), ]


matches_key <- as.list(unstack(rev(matches[, c("lenti_bc", "donor_id")])))

matches_key_counts <- unlist(lapply(matches_key, FUN=function(x) length(unique(x))))
hist(matches_key_counts, 
     main = "donor IDs per lenti barcode", xlab = "# donors")

table(matches_key_counts)
matches_key_counts
  1   2   3   4   5   6   7   8   9  10  11  12  15  16  17  19  21  22  23  25 
981 165  50  21  13   6   9   6   4   1   4   1   1   1   1   2   1   2   1   1 
 27  28  32  38  52  53  57  68 
  1   1   1   1   1   1   1   1 
if(save){write.table(matches, paste0(out, "Lenti2cell_barcode_key.tsv"), sep = "\t", 
              quote=FALSE, row.names = FALSE)}

sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.6 (Green Obsidian)

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.15.so

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] stringr_1.4.0     viridis_0.6.2     viridisLite_0.4.0 cowplot_1.1.1    
 [5] ggpubr_0.4.0      ggplot2_3.3.6     data.table_1.14.2 tidyr_1.2.0      
 [9] dplyr_1.0.9       argparse_2.1.3   

loaded via a namespace (and not attached):
 [1] tidyselect_1.1.2 xfun_0.30        bslib_0.3.1      purrr_0.3.4     
 [5] carData_3.0-5    colorspace_2.0-3 vctrs_0.4.1      generics_0.1.3  
 [9] htmltools_0.5.2  yaml_2.3.5       utf8_1.2.2       rlang_1.0.4     
[13] jquerylib_0.1.4  later_1.3.0      pillar_1.7.0     glue_1.6.2      
[17] withr_2.5.0      DBI_1.1.3        lifecycle_1.0.1  munsell_0.5.0   
[21] ggsignif_0.6.3   gtable_0.3.0     workflowr_1.6.2  evaluate_0.15   
[25] labeling_0.4.2   knitr_1.37       fastmap_1.1.0    httpuv_1.6.5    
[29] fansi_1.0.3      highr_0.9        broom_0.7.10     Rcpp_1.0.9      
[33] backports_1.4.1  promises_1.2.0.1 scales_1.2.0     jsonlite_1.8.0  
[37] abind_1.4-5      farver_2.1.1     fs_1.5.2         gridExtra_2.3   
[41] digest_0.6.29    stringi_1.7.8    rstatix_0.7.0    rprojroot_2.0.2 
[45] grid_4.1.1       cli_3.3.0        tools_4.1.1      magrittr_2.0.3  
[49] sass_0.4.0       tibble_3.1.7     car_3.0-12       crayon_1.5.1    
[53] pkgconfig_2.0.3  ellipsis_0.3.2   assertthat_0.2.1 rmarkdown_2.11  
[57] rstudioapi_0.13  R6_2.5.1         git2r_0.29.0     compiler_4.1.1