Last updated: 2021-12-28
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Knit directory: ChromatinSplicingQTLs/analysis/
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| Rmd | 304e9b7 | Benjmain Fair | 2021-02-11 | added data about picking samples |
library(tidyverse)
library(readxl)
library(knitr)
LCLs.1000Genomes.vec <- read.table("../data/GiladStrains/YRI.list.from1000Genomes.txt", sep='\t') %>% pull(V1) %>%
gsub("^NA", "", ., perl=T)
LCLs.GiladFreezer <- read.table("../data/GiladStrains/Human_LCLs.txt", sep = '\t', header = T, stringsAsFactors = F)
LCLs.GiladFreezer.LCL.YRI.vec <- read.table("../data/GiladStrains/LCL_YRI.GiladCryoSheetTab.list.txt", stringsAsFactors = F) %>% pull(V1) %>% as.character()
GrowthNotes <- read_delim("../data/GrowthNotes/GrowthNotes.tsv", delim = '\t')
SamplesToRestock.vec <- read.table("../data/GiladStrains/Human_LCLs.ToRestock.txt", stringsAsFactors = F) %>% pull(V1) %>% as.character()Ok now figure out which lines to pull
AlreadyDone.vec <- GrowthNotes %>%
filter(!is.na(`Raw Count1 (x10^6)`)) %>%
pull(`Cell Line`) %>% as.character()
#Lines that I can replenish from Briana's stocks
LinesToPutBack.vec <- intersect(SamplesToRestock.vec, AlreadyDone.vec)
LinesStillNeedToRestock.vec <- setdiff(SamplesToRestock.vec, AlreadyDone.vec)
#Are there any YRI lines in Gilad lab freezer that aren't in YRI box
LCLs.GiladFreezer.MainTab.YRI.vec <- LCLs.GiladFreezer %>%
filter(Human.LCL.ID %in% LCLs.1000Genomes.vec) %>%
pull(Human.LCL.ID) %>% as.character()
setdiff(LCLs.GiladFreezer.MainTab.YRI.vec, LCLs.GiladFreezer.LCL.YRI.vec) [1] "18489" "18498" "18501" "18503" "18504" "18509" "18511" "18516" "18518"
[10] "18519" "18521" "18522" "18852" "18855" "18856" "18857" "18858" "18859"
[19] "18860" "18861" "18862" "18871" "18872" "18907" "18909" "18912" "18913"
[28] "18914" "18916" "19092" "19093" "19099" "19100" "19102" "19103" "19114"
[37] "19116" "19119" "19120" "19127" "19129" "19130" "19131" "19132" "19137"
[46] "19138" "19139" "19140" "19142" "19143" "19144" "19145" "19152" "19153"
[55] "19159" "19161" "19171" "19173" "19190" "19201" "19202" "19203" "19204"
[64] "19205" "19206" "19210" "19211" "19222" "18854" "19208"#And the reverse?
setdiff(LCLs.GiladFreezer.LCL.YRI.vec, LCLs.GiladFreezer.MainTab.YRI.vec)[1] "19193" "19128" "18520"BlacklistFromYang <- c("18502", "18516", "18916", "19012")
BlacklistFromBriana <- c("18517", "19128")Let’s include those reverse matches in this experiment. Let’s come up with a list of “StillToDo” strains based on the following points
#Start with set of LCLs in GiladFreezer Main tab
StillToDo.vec <- LCLs.GiladFreezer.MainTab.YRI.vec %>%
#Add list of LCLs in GiladFreezer YRI tab
union(LCLs.GiladFreezer.MainTab.YRI.vec) %>%
#intersect with 1000Genome's YRI list
intersect(LCLs.1000Genomes.vec) %>%
#Subtract out the blacklist strains and the ones we've already done
setdiff(BlacklistFromYang) %>%
setdiff(BlacklistFromBriana) %>%
setdiff(AlreadyDone.vec) %>% sort()
length(StillToDo.vec)[1] 65From that list, let’s prioritize a list of 30 strains for this experiment by
NumVials <- read_delim("../data/GiladStrains/Human_LCLs.NumberOfVials.counts.txt", delim = '\t', col_names = c("Strain", "Count"))
LinesStillNeedToRestock.vec[1] "19209" "18870" "19222" "19143" "18510" "18499" "18498"LinesToRedoFromBriannasStocks.vec <- c("18861", "19140")
Points1And2 <- union(LinesStillNeedToRestock.vec,LinesToRedoFromBriannasStocks.vec)
#Points 3 and 4
LinesWithMoreThan2Vials.vec <- NumVials %>%
filter(Strain %in% StillToDo.vec) %>%
filter(Count > 2) %>%
pull(Strain) %>%
setdiff(Points1And2)
LinesWithMoreThan2Vials.vec [1] "18486" "18497" "18500" "18501" "18503" "18504" "18505" "18508" "18509"
[10] "18515" "18518" "18521" "18522" "18852" "18853" "18854" "18855" "18857"
[19] "18859" "18860" "18862" "18871" "18872" "18914" "19100" "19102" "19103"
[28] "19114" "19116" "19120" "19127" "19129" "19132" "19139" "19141" "19142"
[37] "19144" "19145" "19147" "19159" "19161" "19173" "19190" "19201" "19202"
[46] "19203" "19204" "19205" "19206" "19207" "19208" "19211" "19223" "19238"
[55] "19239" "19257"LCLs.PreviousMolecularDatasets <- read_excel("../data/GiladStrains/individuals.data.types.xlsx", col_names =c("Line", "NumberAssays", "Assay1", "Assay2", "Assay3", "Assay4", "Assay5", "Assay6", "Assay7", "Assay8"))
head(LCLs.PreviousMolecularDatasets)# A tibble: 6 x 10
Line NumberAssays Assay1 Assay2 Assay3 Assay4 Assay5 Assay6 Assay7 Assay8
<dbl> <dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 18499 8 RNAseq Decay prot H3K27ac methyl… DNase ribo 4su
2 18498 8 RNAseq Decay prot H3K27ac methyl… DNase ribo 4su
3 19159 7 RNAseq Decay methyla… H3K27ac DNase ribo 4su <NA>
4 19150 1 RNAseq <NA> <NA> <NA> <NA> <NA> <NA> <NA>
5 19153 7 RNAseq Decay prot H3K27ac DNase ribo 4su <NA>
6 19152 7 RNAseq Decay prot H3K27ac DNase ribo 4su <NA> Points3And4 <- LCLs.PreviousMolecularDatasets %>%
filter(Line %in% LinesWithMoreThan2Vials.vec) %>%
arrange(desc(NumberAssays)) %>%
slice_head(n=32-length(Points1And2)) %>%
pull(Line) %>% as.character()
LinesToThaw <- union(Points3And4, Points1And2)
length(LinesToThaw)[1] 32Lastly, let’s obtain the exact location of these strains
#Read in location of strains
GiladLabLinesLocation <- read_delim("../data/GiladStrains/Large_Cryo_2019_Human_LCLs.tsv", delim = '\t', comment = '#')
GiladLabLinesLocation %>%
filter(Line %in% LinesToThaw) %>%
# filter(is.na(Notes1)) %>%
arrange(Box) %>%
distinct(Line, .keep_all = T) %>%
dplyr::select(Box, Position, Line, Notes1) %>%
write_tsv("../data/GiladStrains/20200111_CellsToWake.tsv")
GiladLabLinesLocation %>%
filter(Line %in% c("18870", "18510"))# A tibble: 12 x 11
LineRowNumber LineColumnNumber LineColumnLetter LineReferenceString
<dbl> <dbl> <chr> <chr>
1 149 2 B Human LCLs'!B149
2 149 3 C Human LCLs'!C149
3 149 4 D Human LCLs'!D149
4 149 5 E Human LCLs'!E149
5 149 6 F Human LCLs'!F149
6 149 7 G Human LCLs'!G149
7 149 8 H Human LCLs'!H149
8 149 9 I Human LCLs'!I149
9 149 10 J Human LCLs'!J149
10 152 2 B Human LCLs'!B152
11 152 3 C Human LCLs'!C152
12 261 4 D Human LCLs'!D261
# … with 7 more variables: Notes1ReferenceString <chr>,
# Notes2ReferenceString <chr>, Box <dbl>, Position <dbl>, Line <chr>,
# Notes1 <chr>, Notes2 <lgl>As of 1/19, only 14/32 strains that we woke up are growing well. therefore, let’s thaw 13 more strains, bringing the total to 27, which, assuming no more than three drop out due to poor growth, will leave us with a batch of 24 for fractionation.
Let’s pick which ones to do by picking from the list of cell lines left to do, subtracting the 14 lines that are growing well, and then intersecting amongst the gilad lab YRI cryo rack strains which are probably stocked in better condition and I think will grow better…
GiladLabYRI.Rack <- read_delim("../data/GiladStrains/LCL_YRI.Gilad.Positions2.txt", delim = '\t') %>%
mutate(Line=as.character(Line))
FourteenLinesAlreadyGrowing <- GrowthNotes %>%
filter(Thawed == "1/12/21") %>%
filter(`CellFractionedAttempted?` == "Y") %>%
pull(`Cell Line`) %>% as.character()
CellLinesToThaw_Jan19 <- LCLs.1000Genomes.vec %>%
setdiff(AlreadyDone.vec) %>%
setdiff(FourteenLinesAlreadyGrowing) %>%
base::intersect(GiladLabYRI.Rack %>% pull(Line))
CellLinesToThaw_Jan19 [1] "19225" "18500" "18505" "18517" "18497" "18853" "19128" "19147" "18508"
[10] "18510" "18515" "18502" "19239" "19238" "19207" "19257" "19209" "19223"
[19] "18520"GiladLabYRI.Rack %>%
filter(Line %in% CellLinesToThaw_Jan19) %>%
slice_head(n=13) %>%
write_tsv("../data/GiladStrains/20200119_CellsToWake.tsv")
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-conda_cos6-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /project2/yangili1/bjf79/ChromatinSplicingQTLs/code/.snakemake/conda/4480a43d58942cece9fb73087fc984b8/lib/R/lib/libRblas.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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.31 readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.5 purrr_0.3.4 readr_1.4.0 tidyr_1.1.3
[9] tibble_3.1.0 ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.20 bslib_0.2.4 haven_2.3.1
[5] colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.10 jquerylib_0.1.3
[13] later_1.1.0.1 pillar_1.5.1 withr_2.4.1 glue_1.4.2
[17] DBI_1.1.1 dbplyr_2.1.1 modelr_0.1.8 lifecycle_1.0.0
[21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2
[25] rvest_1.0.0 evaluate_0.14 ps_1.6.0 httpuv_1.5.5
[29] fansi_0.4.2 broom_0.7.6 Rcpp_1.0.6 promises_1.2.0.1
[33] backports_1.2.1 scales_1.1.1 jsonlite_1.7.2 fs_1.5.0
[37] hms_1.0.0 digest_0.6.27 stringi_1.4.3 rprojroot_2.0.2
[41] grid_3.6.1 cli_2.4.0 tools_3.6.1 magrittr_2.0.1
[45] sass_0.3.1 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.1 xml2_1.3.2 reprex_1.0.0 lubridate_1.7.10
[53] rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.7 httr_1.4.2
[57] R6_2.5.0 git2r_0.28.0 compiler_3.6.1