scRNAseq_complete_00_ambient_RNA
retogerber
2022-10-18
Last updated: 2022-10-18
Checks: 6 1
Knit directory: synovialscrnaseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 3443cc6 | Reto Gerber | 2022-04-25 | Update |
| html | f2e34e1 | Reto Gerber | 2021-07-29 | Update navbar |
| Rmd | ee1face | Reto Gerber | 2021-07-29 | Add missing scripts |
| html | 222b0d1 | Reto Gerber | 2021-07-29 | Update analysis to v5 |
| Rmd | e88c23e | Reto Gerber | 2021-07-12 | add ambient RNA analysis |
| html | e88c23e | Reto Gerber | 2021-07-12 | add ambient RNA analysis |
| Rmd | 6b4ed06 | Reto Gerber | 2021-07-01 | Add new DS, update iSEE |
| Rmd | 83c0d88 | Reto Gerber | 2021-06-14 | bugfix iSEE_LandingPage.R |
| Rmd | 3072789 | Reto Gerber | 2021-06-09 | Update annotation scripts |
Set up
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
library(purrr)
library(stringr)
library(SummarizedExperiment)
library(SingleCellExperiment)
library(scater)
library(scran)
library(igraph)
library(SingleR)
library(scuttle)
library(celldex)
library(ggbeeswarm)
library(tidySingleCellExperiment)
library(bluster)
library(DropletUtils)
library(BiocParallel)
})
n_workers <- 10
RhpcBLASctl::blas_set_num_threads(n_workers)
bpparam <- MulticoreParam(workers = n_workers, RNGseed=123)
# bp_param <- BiocParallel::MulticoreParam(workers=20)
here::here()[1] "/home/retger/Synovial/synovialscrnaseq"raw_data_dir <- here::here("..","data_server")
raw_data_dir_blaz <- here::here("..","data_blaz")
remove_low_quality_samples <- TRUE
set.seed(100)filter low quality samples
if(remove_low_quality_samples){
samples_to_remove <- c("Syn_Bio_080","Syn_Bio_086","Syn_Bio_055_DMSO","Syn_Bio_055_Tofa","Syn_Bio_072_DMSO","Syn_Bio_072_Tofa","Syn_Bio_094_DMSO","Syn_Bio_094_Tofa")
# samples_to_remove %in% syn_sce$Sample
# syn_sce <- syn_sce[,!(syn_sce$Sample %in% samples_to_remove)]
}# prepare
samples <- here::here(raw_data_dir,list.files(raw_data_dir),"raw_feature_bc_matrix")
names(samples) <- purrr::map_chr(strsplit(samples, "/") , ~ .x[length(.x)-1])
samples_to_remove <- c("o23841_1_09-485", # Sample not belongin to Synovial
"o23841_1_13-26_10","26_10000","26_5000", # Aggregated into Aggr_26
"Aggr_26", # old (incorrect) version
"26_10_comb", # intermediate
"23_10000","23_5000", # Aggregated into Aggr_23
"31_10000","31_5000", # Aggregated into Aggr_31
"SynTissue_28_10000","SynTissue_28_5000", # Aggregated into Aggr_28
"o28599_SpaceRangerCount_2022-06-20--14-51-56" # new spatial data
)
samples <- samples[!stringr::str_detect(samples, paste0(samples_to_remove,collapse = "|"))]
sam_ind <- stringr::str_detect(samples,"Aggr_|26_comb")
samples[sam_ind] <- paste0(
purrr::map_chr(strsplit(samples[sam_ind], "/") , ~ paste0(.x[-length(.x)],collapse="/")),
"/outs/count/raw_feature_bc_matrix")metadata_df <- readRDS(here::here("output","Sample_summaries_direct_dissociation.rds"))
sce_swapped_ls <- list()
swappedDrops_samples_comb <- c()
rowdat_syn_sce <- DropletUtils::read10xCounts(samples=samples[20])
order_id <- "24300"# read swappedDrops data
after.mat <- readRDS(here::here("output",paste0("syn_v4_swappedDrops_",order_id,"_after.rds")))
swappedDrops_samples <- names(after.mat$cleaned)[names(after.mat$cleaned) %in% metadata_df$`FGCZ_Sample Name`]
if(remove_low_quality_samples){
swappedDrops_samples <- swappedDrops_samples[!(swappedDrops_samples %in% metadata_df$`FGCZ_Sample Name`[stringr::str_replace_all(metadata_df$Sample," ","_") %in% samples_to_remove])]
}
print(swappedDrops_samples)[1] "o24300_1_01-79" "o24300_1_02-86" "o24300_1_03-83" "o24300_1_04-84"
[5] "o24300_1_05-78" "o24300_1_06-81" "o24300_1_07-87" "o24300_1_11-80"
[9] "o24300_1_12-89"bpstart(bpparam)
sce_swapped_ls[[order_id]] <- BiocParallel::bplapply(BPPARAM = bpparam,
swappedDrops_samples,
function(i){
sce_tmp <- SingleCellExperiment(assays=SimpleList(counts=after.mat$cleaned[[i]]),
rowData=DataFrame(rowData(rowdat_syn_sce)),
colData=DataFrame(Sample=i,
Barcode=colnames(after.mat$cleaned[[i]]),
row.names=colnames(after.mat$cleaned[[i]]))
)
colData(sce_tmp) <- dplyr::left_join(as.data.frame(colData(sce_tmp)),
metadata_df,
by= c("Sample" = "FGCZ_Sample Name"),
suffix=c(".x",".y")) %>%
dplyr::rename(Sample_unique=Sample,
Sample=Sample.y) %>%
dplyr::mutate(Sample = dplyr::if_else(is.na(Sample), Sample_unique, Sample),
Sample = stringr::str_replace_all(Sample, " ", "_"),
Diagnosis = stringr::str_replace_all(Diagnosis, " ", "_")) %>%
DataFrame(row.names=colnames(sce_tmp))
colnames(sce_tmp) <- paste0(sce_tmp$Sample, ".", sce_tmp$Barcode)
rownames(sce_tmp) <- paste0(rowData(sce_tmp)$ID, ".", rowData(sce_tmp)$Symbol)
sce_tmp
})
bpstop(bpparam)
names(sce_swapped_ls[[order_id]]) <- swappedDrops_samples
swappedDrops_samples_comb <- c(swappedDrops_samples_comb,swappedDrops_samples)
rm(after.mat)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 27965017 1493.5 59405312 3172.6 28346425 1513.9
Vcells 701041277 5348.6 1987275592 15161.8 2330557716 17780.8order_id <- "24793"# read swappedDrops data
after.mat <- readRDS(here::here("output",paste0("syn_v4_swappedDrops_",order_id,"_after.rds")))
swappedDrops_samples <- names(after.mat$cleaned)[names(after.mat$cleaned) %in% metadata_df$`FGCZ_Sample Name`]
if(remove_low_quality_samples){
swappedDrops_samples <- swappedDrops_samples[!(swappedDrops_samples %in% metadata_df$`FGCZ_Sample Name`[stringr::str_replace_all(metadata_df$Sample," ","_") %in% samples_to_remove])]
}
print(swappedDrops_samples)[1] "o24793_1_01-91" "o24793_1_02-92" "o24793_1_03-93" "o24793_1_04-95"
[5] "o24793_1_05-96" "o24793_1_06-98a" "o24793_1_07-98b" "o24793_1_08-99" bpstart(bpparam)
sce_swapped_ls[[order_id]] <- BiocParallel::bplapply(BPPARAM = bpparam,
swappedDrops_samples,
function(i){
sce_tmp <- SingleCellExperiment(assays=SimpleList(counts=after.mat$cleaned[[i]]),
rowData=DataFrame(rowData(rowdat_syn_sce)),
colData=DataFrame(Sample=i,
Barcode=colnames(after.mat$cleaned[[i]]),
row.names=colnames(after.mat$cleaned[[i]]))
)
colData(sce_tmp) <- dplyr::left_join(as.data.frame(colData(sce_tmp)),
metadata_df,
by= c("Sample" = "FGCZ_Sample Name"),
suffix=c(".x",".y")) %>%
dplyr::rename(Sample_unique=Sample,
Sample=Sample.y) %>%
dplyr::mutate(Sample = dplyr::if_else(is.na(Sample), Sample_unique, Sample),
Sample = stringr::str_replace_all(Sample, " ", "_"),
Diagnosis = stringr::str_replace_all(Diagnosis, " ", "_")) %>%
DataFrame(row.names=colnames(sce_tmp))
colnames(sce_tmp) <- paste0(sce_tmp$Sample, ".", sce_tmp$Barcode)
rownames(sce_tmp) <- paste0(rowData(sce_tmp)$ID, ".", rowData(sce_tmp)$Symbol)
sce_tmp
})
bpstop(bpparam)
names(sce_swapped_ls[[order_id]]) <- swappedDrops_samples
swappedDrops_samples_comb <- c(swappedDrops_samples_comb,swappedDrops_samples)
rm(after.mat)
gc() used (Mb) gc trigger (Mb) max used (Mb)
Ncells 38731026 2068.5 59405312 3172.6 38790224 2071.7
Vcells 1291423075 9852.8 2864817327 21856.9 2438078823 18601.1EmptyDrops
lower_cutoffs <- lapply(names(samples), function(i) 100)
names(lower_cutoffs) <- names(samples)
lower_cutoffs[["o23841_1_03-54b"]] <- 80
lower_cutoffs[["o23841_1_04-59"]] <- 80
lower_cutoffs[["o23841_1_08-75"]] <- 80
lower_cutoffs[["o23841_1_10-76"]] <- 80
lower_cutoffs[["o24300_1_05-78"]] <- 80
lower_cutoffs[["o24300_1_07-87"]] <- 80
lower_cutoffs[["o24300_1_12-89"]] <- 80
lower_cutoffs[["o23841_1_14-72DMSO"]] <- 200
lower_cutoffs[["o23841_1_15-72Tofa"]] <- 200
lower_cutoffs[["o24793_1_05-96"]] <- 200
lower_cutoffs[["o24793_1_06-98a"]] <- 200
lower_cutoffs[["o24793_1_08-99"]] <- 200
lower_cutoffs[["SynBio_Tofacitinib"]] <- 200
lower_cutoffs[["SynBio_Untreated"]] <- 200
lower_cutoffs[["SynTissue_49_8000"]] <- 150# error: emptyDrops o23841_1_08-75
RhpcBLASctl::blas_set_num_threads(1)
res <- list()
print(names(sce_swapped_ls))[1] "24300" "24793"for(k in names(sce_swapped_ls)){
print(names(sce_swapped_ls[[k]]))
bpstart(bpparam)
res_tmp <- BiocParallel::bplapply(seq_along(names(sce_swapped_ls[[k]])),
BPPARAM = bpparam,
function(i){
se <- sce_swapped_ls[[k]][[i]]
niters <- 500000
tmp_lower_cutoff <- lower_cutoffs[[names(sce_swapped_ls[[k]])[i]]]
bcrank <- barcodeRanks(counts(se),lower = tmp_lower_cutoff)
e.out <- emptyDrops(counts(se), test.ambient = TRUE, niters = niters,
lower = tmp_lower_cutoff)
barcodes_to_remove <- colData(se)$Barcode[which(e.out$FDR > 0.001)]
se <- se[,!(se$Barcode %in% barcodes_to_remove)]
se <- se[,(colSums(counts(se)) > 0)]
list(bcrank = bcrank, e.out = e.out,
barcodes_to_remove = barcodes_to_remove,
lower_cutoff = tmp_lower_cutoff,
sce=se)
# res[[i]] <- list(bcrank = bcrank, e.out = e.out, barcodes_to_remove = barcodes_to_remove)
})
bpstop(bpparam)
names(res_tmp) <- names(sce_swapped_ls[[k]])
res <- c(res,res_tmp)
}[1] "o24300_1_01-79" "o24300_1_02-86" "o24300_1_03-83" "o24300_1_04-84"
[5] "o24300_1_05-78" "o24300_1_06-81" "o24300_1_07-87" "o24300_1_11-80"
[9] "o24300_1_12-89"
[1] "o24793_1_01-91" "o24793_1_02-92" "o24793_1_03-93" "o24793_1_04-95"
[5] "o24793_1_05-96" "o24793_1_06-98a" "o24793_1_07-98b" "o24793_1_08-99" saveRDS(res, here::here("output","emptyDrops_result_v4_tmp.rds"))samples <- samples[!(names(samples) %in% swappedDrops_samples_comb)]
if(remove_low_quality_samples){
samples <- samples[!(names(samples) %in% metadata_df$`FGCZ_Sample Name`[stringr::str_replace_all(metadata_df$Sample," ","_") %in% samples_to_remove])]
}
print(names(samples)) [1] "26_comb" "Aggr_23" "Aggr_28"
[4] "Aggr_31" "o23841_1_01-53" "o23841_1_02-54a"
[7] "o23841_1_03-54b" "o23841_1_04-59" "o23841_1_05-62"
[10] "o23841_1_06-64" "o23841_1_07-74" "o23841_1_08-75"
[13] "o23841_1_10-76" "o23841_1_11-077W" "o23841_1_12-077K"
[16] "o23841_1_14-72DMSO" "o23841_1_15-72Tofa" "o24555_1_1-94_tofa"
[19] "o24555_1_2-94_control1" "SynBio_Tofacitinib" "SynBio_Untreated"
[22] "SynTissue_49_8000" "SynTissue_50_6000" # read
bpstart(bpparam)
res2 <- BiocParallel::bplapply(BPPARAM = bpparam,
# res2 <- lapply(
seq_along(samples),
function(i){
se <- DropletUtils::read10xCounts(samples=samples[i])
colnames(se) <- paste0(se$Sample, ".", se$Barcode)
rownames(se) <- paste0(rowData(se)$ID, ".", rowData(se)$Symbol)
# se <- se[,sample(seq_len(dim(se)[2]),size = round(dim(se)[2]/50), replace = FALSE )]
niters <- 500000
if (names(samples)[i] == "o23841_1_03-54b"){
niters <- 1000000
}
# niters <- 1000
if (names(samples)[i] %in% names(lower_cutoffs)){
tmp_lower_cutoff <- lower_cutoffs[[names(samples)[i]]]
} else {
tmp_lower_cutoff <- 100
}
bcrank <- barcodeRanks(counts(se),lower = tmp_lower_cutoff)
e.out <- emptyDrops(counts(se), test.ambient = TRUE, niters = niters,
lower = tmp_lower_cutoff)
barcodes_to_remove <- colData(se)$Barcode[which(e.out$FDR > 0.001)]
se <- se[,!(se$Barcode %in% barcodes_to_remove)]
se <- se[,(colSums(counts(se)) > 0)]
# Just for joining
tmpsamnam <- unique(colData(se)$Sample)
if(tmpsamnam %in% c("Aggr_23","26_comb","Aggr_31","Aggr_28")){
replacename <- switch(tmpsamnam,
Aggr_23 = "23_5000",
`26_comb` = "26_5000",
Aggr_31 = "31_5000",
Aggr_28 = "SynTissue_28_5000"
)
colData(se)$Sample[colData(se)$Sample == tmpsamnam] <- replacename
}
colData(se) <- dplyr::left_join(as.data.frame(colData(se)),
metadata_df,
by= c("Sample" = "FGCZ_Sample Name"),
suffix=c(".x",".y")) %>%
dplyr::rename(Sample_unique=Sample,
Sample=Sample.y) %>%
dplyr::mutate(Sample = dplyr::if_else(is.na(Sample), Sample_unique, Sample),
Sample = stringr::str_replace_all(Sample, " ", "_"),
Diagnosis = stringr::str_replace_all(Diagnosis, " ", "_")) %>%
DataFrame(row.names=colnames(se))
tmp <- list(bcrank = bcrank, e.out = e.out,
barcodes_to_remove = barcodes_to_remove,
lower_cutoff = tmp_lower_cutoff,
sce=se)
saveRDS(list(i=i,tmp=tmp), here::here("output","emptyDrops_result_v4tmptmp.rds"))
tmp
})
bpstop(bpparam)
names(res2) <- names(samples)
res_comb <- c(res, res2)
names(res_comb) <- c(swappedDrops_samples_comb, names(samples))
saveRDS(res_comb, here::here("output","emptyDrops_result_v4.rds"))
res_comb <- readRDS(here::here("output","emptyDrops_result_v4.rds"))
res <- res_comb
rm(res_comb)# res <- readRDS(here::here("output","emptyDrops_result_v4.rds"))
cat("### BCRank {.tabset}\n\n")BCRank
for(i in seq_along(res)){
cat("#### ",names(res)[i],"\n\n")
uniq <- !duplicated(res[[i]]$bcrank$rank)
suppressWarnings(plot(res[[i]]$bcrank$rank[uniq], res[[i]]$bcrank$total[uniq], log="xy",
xlab="Rank", ylab="Total UMI count", cex.lab=1.2))
suppressWarnings(abline(h=metadata(res[[i]]$bcrank)$inflection, col="darkgreen", lty=2))
suppressWarnings(abline(h=metadata(res[[i]]$bcrank)$knee, col="dodgerblue", lty=2))
suppressWarnings(abline(h=res[[i]]$lower_cutoff))
suppressWarnings(legend("bottomleft", legend=c("Inflection", "Knee"),
col=c("darkgreen", "dodgerblue"), lty=2, cex=1.2))
cat("\n\n")
}
for(i in seq_along(res)){
print(names(res)[i])
print(table(Sig=res[[i]]$e.out$FDR <= 0.001, Limited=res[[i]]$e.out$Limited))
print(paste0(rep("-",80),collapse = ""))
}[1] "o24300_1_01-79"
Limited
Sig FALSE TRUE
FALSE 1276866 0
TRUE 171 7777
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_02-86"
Limited
Sig FALSE TRUE
FALSE 1201081 0
TRUE 820 16081
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_03-83"
Limited
Sig FALSE TRUE
FALSE 1253493 0
TRUE 109 6040
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_04-84"
Limited
Sig FALSE TRUE
FALSE 1254880 0
TRUE 54 5090
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_05-78"
Limited
Sig FALSE TRUE
FALSE 641198 0
TRUE 3 2046
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_06-81"
Limited
Sig FALSE TRUE
FALSE 1430138 0
TRUE 37 6873
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_07-87"
Limited
Sig FALSE TRUE
FALSE 746471 0
TRUE 89 3538
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_11-80"
Limited
Sig FALSE TRUE
FALSE 387418 0
TRUE 6011 9865
[1] "--------------------------------------------------------------------------------"
[1] "o24300_1_12-89"
Limited
Sig FALSE TRUE
FALSE 471959 0
TRUE 1 1853
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_01-91"
Limited
Sig FALSE TRUE
FALSE 1163603 0
TRUE 715 4975
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_02-92"
Limited
Sig FALSE TRUE
FALSE 1325635 0
TRUE 41 6826
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_03-93"
Limited
Sig FALSE TRUE
FALSE 998455 0
TRUE 50 5839
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_04-95"
Limited
Sig FALSE TRUE
FALSE 1102702 0
TRUE 0 2320
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_05-96"
Limited
Sig FALSE TRUE
FALSE 1520866 0
TRUE 92 6546
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_06-98a"
Limited
Sig FALSE TRUE
FALSE 1336516 0
TRUE 592 3874
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_07-98b"
Limited
Sig FALSE TRUE
FALSE 1075768 0
TRUE 178 6613
[1] "--------------------------------------------------------------------------------"
[1] "o24793_1_08-99"
Limited
Sig FALSE TRUE
FALSE 1218827 0
TRUE 90 4958
[1] "--------------------------------------------------------------------------------"
[1] "26_comb"
Limited
Sig FALSE TRUE
FALSE 1633590 0
TRUE 138 11897
[1] "--------------------------------------------------------------------------------"
[1] "Aggr_23"
Limited
Sig FALSE TRUE
FALSE 1204295 0
TRUE 143 7158
[1] "--------------------------------------------------------------------------------"
[1] "Aggr_28"
Limited
Sig FALSE TRUE
FALSE 1102828 0
TRUE 87 5884
[1] "--------------------------------------------------------------------------------"
[1] "Aggr_31"
Limited
Sig FALSE TRUE
FALSE 1021960 0
TRUE 243 5368
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_01-53"
Limited
Sig FALSE TRUE
FALSE 1130506 0
TRUE 62 5123
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_02-54a"
Limited
Sig FALSE TRUE
FALSE 718656 0
TRUE 350 3416
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_03-54b"
Limited
Sig FALSE TRUE
FALSE 435097 0
TRUE 1 838
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_04-59"
Limited
Sig FALSE TRUE
FALSE 525357 0
TRUE 0 1562
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_05-62"
Limited
Sig FALSE TRUE
FALSE 1175186 0
TRUE 31 5506
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_06-64"
Limited
Sig FALSE TRUE
FALSE 1440747 0
TRUE 2 5702
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_07-74"
Limited
Sig FALSE TRUE
FALSE 1274442 0
TRUE 2 4666
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_08-75"
Limited
Sig FALSE TRUE
FALSE 343491 0
TRUE 0 1256
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_10-76"
Limited
Sig FALSE TRUE
FALSE 478076 0
TRUE 20 2725
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_11-077W"
Limited
Sig FALSE TRUE
FALSE 1449723 0
TRUE 106 7800
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_12-077K"
Limited
Sig FALSE TRUE
FALSE 1095398 0
TRUE 156 7435
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_14-72DMSO"
Limited
Sig FALSE TRUE
FALSE 1242348 0
TRUE 40 5677
[1] "--------------------------------------------------------------------------------"
[1] "o23841_1_15-72Tofa"
Limited
Sig FALSE TRUE
FALSE 1428447 0
TRUE 78 5753
[1] "--------------------------------------------------------------------------------"
[1] "o24555_1_1-94_tofa"
Limited
Sig FALSE TRUE
FALSE 1232194 0
TRUE 200 8326
[1] "--------------------------------------------------------------------------------"
[1] "o24555_1_2-94_control1"
Limited
Sig FALSE TRUE
FALSE 828522 0
TRUE 673 6831
[1] "--------------------------------------------------------------------------------"
[1] "SynBio_Tofacitinib"
Limited
Sig FALSE TRUE
FALSE 1601179 0
TRUE 796 7323
[1] "--------------------------------------------------------------------------------"
[1] "SynBio_Untreated"
Limited
Sig FALSE TRUE
FALSE 1620526 0
TRUE 612 10046
[1] "--------------------------------------------------------------------------------"
[1] "SynTissue_49_8000"
Limited
Sig FALSE TRUE
FALSE 442527 0
TRUE 1 943
[1] "--------------------------------------------------------------------------------"
[1] "SynTissue_50_6000"
Limited
Sig FALSE TRUE
FALSE 833419 0
TRUE 99 4514
[1] "--------------------------------------------------------------------------------"cat("### Pval hist {.tabset}\n\n")Pval hist
for(i in seq_along(res)){
cat("#### ",names(res)[i],"\n\n")
print(hist(res[[i]]$e.out$PValue[res[[i]]$e.out$Total <= res[[i]]$lower_cutoff & res[[i]]$e.out$Total > 0],
xlab="P-value", main="", col="grey80"))
cat("\n\n")
}o24300_1_01-79
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 62622 57311 55036 56043 55863 56599 57540 57530 58940 55861 62751 62482 [13] 60989 65441 63369 67097 80772 69945 78613 83056
$density [1] 0.9878378 0.9040588 0.8681716 0.8840566 0.8812172 0.8928273 0.9076712 [8] 0.9075134 0.9297557 0.8811856 0.9898727 0.9856293 0.9620778 1.0323064 [15] 0.9996214 1.0584292 1.2741470 1.1033553 1.2400896 1.3101762
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_02-86
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 66030 61157 58528 59128 57733 57186 56867 56725 55922 54721 56621 56415 [13] 55467 54317 57184 57047 62624 73031 78794 53267
$density [1] 1.1109017 1.0289174 0.9846866 0.9947811 0.9713114 0.9621085 0.9567416 [8] 0.9543526 0.9408428 0.9206369 0.9526029 0.9491371 0.9331877 0.9138399 [15] 0.9620749 0.9597700 1.0535985 1.2286879 1.3256458 0.8961745
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_03-83
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 70333 59838 58867 57464 59098 59362 58791 58000 60110 62832 [11] 67017 61881 69926 66594 52759 67235 105865 63263 56819 22696
$density [1] 1.1355479 0.9661029 0.9504258 0.9277740 0.9541554 0.9584178 0.9491988 [8] 0.9364279 0.9704945 1.0144420 1.0820101 0.9990878 1.1289768 1.0751806 [15] 0.8518103 1.0855298 1.7092230 1.0214006 0.9173602 0.3664339
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_04-84
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 60102 53641 53336 53177 51986 52884 52072 52439 53325 52772 53905 55511 [13] 60404 61030 60958 66769 72486 85530 93688 79040
$density [1] 0.9812131 0.8757321 0.8707527 0.8681569 0.8487129 0.8633735 0.8501169 [8] 0.8561085 0.8705732 0.8615450 0.8800421 0.9062614 0.9861435 0.9963634 [15] 0.9951880 1.0900572 1.1833918 1.3963455 1.5295313 1.2903910
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_05-78
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 37670 30818 29554 29450 28812 28883 29513 29980 29459 28920 29322 31664 [13] 33835 35581 34898 32972 33856 36503 34719 32926
$density [1] 1.1784119 0.9640642 0.9245231 0.9212698 0.9013115 0.9035326 0.9232406 [8] 0.9378495 0.9215513 0.9046900 0.9172656 0.9905292 1.0584435 1.1130628 [15] 1.0916968 1.0314467 1.0591005 1.1419053 1.0860973 1.0300077
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_06-81
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 71892 68090 67732 66797 67543 67950 68280 68497 68790 67326 67918 70367 [13] 74155 72218 75244 76323 69080 83150 75020 79032
$density [1] 1.0087245 0.9553783 0.9503551 0.9372360 0.9477032 0.9534139 0.9580442 [8] 0.9610889 0.9652000 0.9446585 0.9529649 0.9873271 1.0404769 1.0132987 [15] 1.0557568 1.0708964 0.9692691 1.1666868 1.0526139 1.1089067
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_07-87
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 42881 37645 35728 34488 34285 35183 35100 35404 34772 33408 34877 36071 [13] 37304 34408 38673 36164 42670 44292 37856 41906
$density [1] 1.1540879 1.0131675 0.9615739 0.9282009 0.9227374 0.9469059 0.9446721 [8] 0.9528539 0.9358444 0.8991341 0.9386703 0.9708053 1.0039900 0.9260478 [15] 1.0408349 0.9733083 1.1484091 1.1920631 1.0188463 1.1278470
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_11-80
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 23700 21472 18730 18482 18399 17906 17092 17037 16888 16288 15489 15581 [13] 15324 14703 15395 20292 19567 24066 2342 5328
$density [1] 1.4188176 1.2854368 1.1212850 1.1064383 1.1014694 1.0719556 1.0232249 [8] 1.0199323 1.0110123 0.9750929 0.9272602 0.9327678 0.9173823 0.8802057 [15] 0.9216328 1.2147952 1.1713926 1.4407284 0.1402055 0.3189646
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24300_1_12-89
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 28961 24730 24289 22991 23250 22142 22417 21864 21555 21536 21945 22065 [13] 21688 22270 21511 20495 23019 29907 25580 28367
$density [1] 1.2308588 1.0510389 1.0322962 0.9771304 0.9881381 0.9410475 0.9527351 [8] 0.9292323 0.9160996 0.9152921 0.9326749 0.9377749 0.9217522 0.9464875 [15] 0.9142296 0.8710490 0.9783205 1.2710643 1.0871644 1.2056135
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_01-91
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 54594 50020 48672 48237 48573 48485 48742 50198 48872 49295 51564 52720 [13] 56169 55867 54539 63393 62710 64766 94665 97496
$density [1] 0.9498102 0.8702331 0.8467810 0.8392130 0.8450587 0.8435277 0.8479989 [8] 0.8733299 0.8502606 0.8576198 0.8970952 0.9172069 0.9772116 0.9719575 [15] 0.9488534 1.1028926 1.0910100 1.1267797 1.6469536 1.6962065
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_02-92
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 61874 56576 55339 54641 53521 55168 54749 56396 56337 55801 [11] 57363 60196 62893 64407 61223 71262 86797 99365 61702 107989
$density [1] 0.9566179 0.8747069 0.8555820 0.8447904 0.8274744 0.8529382 0.8464601 [8] 0.8719240 0.8710118 0.8627248 0.8868745 0.9306748 0.9723724 0.9957800 [15] 0.9465530 1.1017634 1.3419460 1.5362566 0.9539587 1.6695900
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_03-93
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 52056 47654 45752 44143 45024 43926 43952 43800 43791 43597 43382 44733 [13] 46408 43260 47764 53298 65300 85914 42092 58802
$density [1] 1.0573525 0.9679398 0.9293067 0.8966250 0.9145197 0.8922173 0.8927454 [8] 0.8896580 0.8894752 0.8855347 0.8811677 0.9086090 0.9426313 0.8786896 [15] 0.9701741 1.0825798 1.3263623 1.7450703 0.8549654 1.1943761
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_04-95
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 54449 46047 45185 44197 45062 44907 44490 45822 46281 45564 49175 50367 [13] 52255 54266 51975 53931 78785 70285 82995 94457
$density [1] 0.9895365 0.8368416 0.8211759 0.8032204 0.8189406 0.8161237 0.8085452 [8] 0.8327525 0.8410942 0.8280637 0.8936887 0.9153517 0.9496636 0.9862108 [15] 0.9445749 0.9801226 1.4318102 1.2773343 1.5083213 1.7166275
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_05-96
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 77196 65593 62832 63617 61864 62424 62066 65466 65340 65316 [11] 66654 70925 74506 71465 71433 84776 70593 135088 97331 114267
$density [1] 1.0233093 0.8695001 0.8329003 0.8433063 0.8200685 0.8274919 0.8227462 [8] 0.8678166 0.8661463 0.8658282 0.8835647 0.9401810 0.9876507 0.9473393 [15] 0.9469151 1.1237897 0.9357800 1.7907250 1.2902187 1.5147221
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24793_1_06-98a
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
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o24793_1_07-98b
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o24793_1_08-99
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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26_comb
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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Aggr_23
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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Aggr_28
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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Aggr_31
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_01-53
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_02-54a
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_03-54b
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_04-59
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$counts [1] 31727 27658 26179 25999 25113 24513 24221 23954 24012 23933 22640 22597 [13] 22955 23158 21387 23059 28522 36875 31258 33493
$density [1] 1.2126830 1.0571559 1.0006249 0.9937449 0.9598798 0.9369464 0.9257854 [8] 0.9155800 0.9177969 0.9147774 0.8653558 0.8637122 0.8773958 0.8851550 [15] 0.8174631 0.8813710 1.0901801 1.4094520 1.1947566 1.2801838
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_05-62
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$counts [1] 60074 55900 53507 53629 52982 54374 53776 54707 54780 54533 54317 55382 [13] 60504 54611 57398 60465 72687 61168 69966 73677
$density [1] 1.0282797 0.9568338 0.9158731 0.9179613 0.9068867 0.9307134 0.9204775 [8] 0.9364133 0.9376629 0.9334350 0.9297378 0.9479672 1.0356399 0.9347701 [15] 0.9824749 1.0349724 1.2441749 1.0470055 1.1975999 1.2611206
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_06-64
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$counts [1] 68234 63310 63070 64486 64056 64887 64330 65444 64481 65345 [11] 67600 68842 75815 68877 70281 84221 82739 92466 67731 101831
$density [1] 0.9556275 0.8866661 0.8833049 0.9031362 0.8971140 0.9087522 0.9009514 [8] 0.9165531 0.9030661 0.9151666 0.9467482 0.9641426 1.0618005 0.9646328 [15] 0.9842960 1.1795278 1.1587722 1.2950003 0.9485829 1.4261585
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_07-74
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$counts [1] 65861 56116 54785 53893 54048 54587 55013 55448 54597 56032 56384 57336 [13] 54963 57733 60225 66527 98427 92421 76594 87651
$density [1] 1.0382922 0.8846632 0.8636801 0.8496178 0.8520614 0.8605587 0.8672745 [8] 0.8741322 0.8607163 0.8833389 0.8888882 0.9038964 0.8664863 0.9101550 [15] 0.9494412 1.0487916 1.5516919 1.4570079 1.2074968 1.3818094
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_08-75
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$counts [1] 20353 18550 17347 16855 16521 15960 15930 15764 15900 15616 15593 14959 [13] 15713 15341 14918 17152 17820 17730 19706 25173
$density [1] 1.1871065 1.0819449 1.0117789 0.9830826 0.9636017 0.9308809 0.9291311 [8] 0.9194491 0.9273814 0.9108168 0.9094753 0.8724967 0.9164744 0.8947772 [15] 0.8701054 1.0004054 1.0393670 1.0341177 1.1493696 1.4682372
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_10-76
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$counts [1] 33505 27634 24197 24125 22788 22832 22895 21584 21223 21227 22878 24698 [13] 24384 24427 24953 22815 20767 30784 11112 28241
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_11-077W
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$counts [1] 86733 68772 67296 64600 65082 64034 64731 64909 63643 64126 65310 64895 [13] 68034 65214 65738 79270 73979 94413 92892 96541
$density [1] 1.2044477 0.9550261 0.9345291 0.8970902 0.9037836 0.8892302 0.8989093 [8] 0.9013812 0.8838004 0.8905078 0.9069498 0.9011868 0.9447776 0.9056167 [15] 0.9128934 1.1008102 1.0273349 1.3110986 1.2899768 1.3406498
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_12-077K
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$counts [1] 69887 55131 52634 50278 50128 49493 49902 48602 48321 47138 47131 46879 [13] 50410 48424 44664 60711 47153 80924 66696 75159
$density [1] 1.2827245 1.0118890 0.9660584 0.9228157 0.9200626 0.9084076 0.9159145 [8] 0.8920540 0.8868964 0.8651833 0.8650549 0.8604296 0.9252385 0.8887869 [15] 0.8197749 1.1143058 0.8654587 1.4853005 1.2241560 1.3794882
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_14-72DMSO
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$counts [1] 63690 56426 53829 53291 51922 52480 50039 50553 50496 51072 [11] 51699 50510 50640 49653 54504 58606 103628 74817 113696 69737
$density [1] 1.0516079 0.9316694 0.8887895 0.8799063 0.8573023 0.8665156 0.8262114 [8] 0.8346983 0.8337571 0.8432677 0.8536203 0.8339883 0.8361348 0.8198381 [15] 0.8999346 0.9676642 1.7110382 1.2353297 1.8772744 1.1514520
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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o23841_1_15-72Tofa
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$counts [1] 79480 65478 61061 60530 59239 58591 58797 59676 59143 58846 [11] 60941 58813 60218 61020 59596 83941 110609 114028 57825 133173
$density [1] 1.1186449 0.9215731 0.8594058 0.8519323 0.8337620 0.8246417 0.8275411 [8] 0.8399126 0.8324109 0.8282307 0.8577169 0.8277663 0.8475410 0.8588288 [15] 0.8387866 1.1814315 1.5567714 1.6048923 0.8138606 1.8743495
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\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
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attr(,“class”) [1] “histogram”
o24555_1_1-94_tofa
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 65585 57507 55005 54770 55404 54800 55041 55753 56317 56240 58172 58743 [13] 59828 63343 63793 63455 85579 57349 64464 76098
$density [1] 1.0775965 0.9448706 0.9037614 0.8999003 0.9103172 0.9003932 0.9043529 [8] 0.9160515 0.9253183 0.9240531 0.9557969 0.9651788 0.9830059 1.0407592 [15] 1.0481530 1.0425994 1.4061085 0.9422746 1.0591778 1.2503307
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
o24555_1_2-94_control1
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 44368 37680 36714 36067 36262 35675 35839 35821 36068 36439 35987 37919 [13] 36184 38035 36991 45451 65373 55141 22442 43682
$density [1] 1.1258942 0.9561777 0.9316643 0.9152458 0.9201942 0.9052983 0.9094600 [8] 0.9090032 0.9152712 0.9246858 0.9132157 0.9622427 0.9182148 0.9651863 [15] 0.9386935 1.1533767 1.6589227 1.3992727 0.5694942 1.1084861
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
SynBio_Tofacitinib
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 81343 60734 59283 59842 59993 61758 63378 64637 67702 65559 [11] 72102 76068 80975 76370 70112 99996 118621 104587 132618 115653
$density [1] 1.0223266 0.7633107 0.7450744 0.7521000 0.7539978 0.7761804 0.7965408 [8] 0.8123640 0.8508852 0.8239518 0.9061848 0.9560299 1.0177015 0.9598255 [15] 0.8811743 1.2567593 1.4908401 1.3144594 1.6667557 1.4535380
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
SynBio_Untreated
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 83258 64775 61928 61633 62370 63018 63021 64619 65510 65826 [11] 71323 73468 74008 76118 79991 92014 104604 152340 112177 115319
$density [1] 1.0359854 0.8060000 0.7705746 0.7669039 0.7760745 0.7841376 0.7841749 [8] 0.8040589 0.8151457 0.8190777 0.8874773 0.9141677 0.9208869 0.9471418 [15] 0.9953338 1.1449369 1.3015952 1.8955777 1.3958266 1.4349227
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
SynTissue_49_8000
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 22595 20092 18915 19163 18257 18547 18458 18887 18472 18189 18527 20105 [13] 19676 19666 22184 21740 20385 39346 36781 26480
$density [1] 1.0353637 0.9206695 0.8667362 0.8781002 0.8365848 0.8498734 0.8457952 [8] 0.8654531 0.8464367 0.8334689 0.8489570 0.9212652 0.9016072 0.9011490 [15] 1.0165305 0.9961853 0.9340955 1.8029395 1.6854043 1.2133848
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
SynTissue_50_6000
$breaks [1] 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 [16] 0.75 0.80 0.85 0.90 0.95 1.00
$counts [1] 42959 37765 36287 34601 34779 33846 33272 33059 33658 32810 32066 32217 [13] 30582 34142 30437 35453 45596 72885 54625 67678
$density [1] 1.0893388 0.9576312 0.9201526 0.8773996 0.8819133 0.8582546 0.8436993 [8] 0.8382981 0.8534874 0.8319841 0.8131180 0.8169470 0.7754873 0.8657605 [15] 0.7718104 0.8990043 1.1562069 1.8481914 1.3851610 1.7161542
$mids [1] 0.025 0.075 0.125 0.175 0.225 0.275 0.325 0.375 0.425 0.475 0.525 0.575 [13] 0.625 0.675 0.725 0.775 0.825 0.875 0.925 0.975
\(xname [1] "res[[i]]\)e.out\(PValue[res[[i]]\)e.out\(Total <= res[[i]]\)lower_cutoff & res[[i]]\(e.out\)Total > 0]"
$equidist [1] TRUE
attr(,“class”) [1] “histogram”
cat("### {-}")cat("### logProb plot {.tabset}\n\n")logProb plot
for(i in seq_along(res)){
cat("#### ",names(res)[i],"\n\n")
plot(res[[i]]$e.out$Total, -res[[i]]$e.out$LogProb, col=ifelse(res[[i]]$e.out$FDR > 0.001, "red", "black"),
xlab="Total UMI count", ylab="-Log Probability")
cat("\n\n")
}
o23841_1_14-72DMSO
o23841_1_15-72Tofa
o24555_1_1-94_tofa
o24555_1_2-94_control1
SynBio_Tofacitinib
tryCatch({
res <- readRDS(here::here("output","emptyDrops_result_v4.rds"))
sce_comb <- lapply(seq_along(res), function(i){
tmpsce <- res[[i]]$sce
tmpsce <- tmpsce[,!(tmpsce$Barcode %in% res[[i]]$barcodes_to_remove)]
tmpsce[,(colSums(counts(tmpsce)) > 0)]
}) %>%
purrr::reduce(cbind)
tmpfilename <- paste0("syn_v4_sce_emptyDrops",dplyr::if_else(remove_low_quality_samples, "_invivo",""),".rds")
saveRDS(sce_comb, file =here::here("output",tmpfilename))
}, error=function(e) print(e))
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS
Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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=C
[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] gdtools_0.2.3 BiocParallel_1.24.1
[3] DropletUtils_1.10.3 bluster_1.0.0
[5] tidySingleCellExperiment_1.0.0 ggbeeswarm_0.6.0
[7] celldex_1.0.0 scuttle_1.0.4
[9] SingleR_1.4.1 igraph_1.2.6
[11] scran_1.18.7 scater_1.18.6
[13] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[15] Biobase_2.50.0 GenomicRanges_1.42.0
[17] GenomeInfoDb_1.26.7 IRanges_2.24.1
[19] S4Vectors_0.28.1 BiocGenerics_0.36.1
[21] MatrixGenerics_1.2.1 matrixStats_0.58.0
[23] stringr_1.4.0 purrr_0.3.4
[25] ggplot2_3.3.3 dplyr_1.0.4
[27] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1
[3] rprojroot_2.0.2 XVector_0.30.0
[5] BiocNeighbors_1.8.2 fs_1.5.0
[7] bit64_4.0.5 fansi_0.4.2
[9] interactiveDisplayBase_1.28.0 AnnotationDbi_1.52.0
[11] R.methodsS3_1.8.1 sparseMatrixStats_1.2.1
[13] cachem_1.0.4 knitr_1.31
[15] jsonlite_1.7.2 RhpcBLASctl_0.20-137
[17] dbplyr_2.1.0 R.oo_1.24.0
[19] HDF5Array_1.18.1 shiny_1.6.0
[21] BiocManager_1.30.12 compiler_4.0.3
[23] httr_1.4.2 dqrng_0.2.1
[25] lazyeval_0.2.2 assertthat_0.2.1
[27] Matrix_1.3-2 fastmap_1.1.0
[29] limma_3.46.0 cli_2.3.0
[31] later_1.1.0.1 BiocSingular_1.6.0
[33] htmltools_0.5.1.1 tools_4.0.3
[35] rsvd_1.0.3 gtable_0.3.0
[37] glue_1.4.2 GenomeInfoDbData_1.2.4
[39] rappdirs_0.3.3 Rcpp_1.0.6
[41] rhdf5filters_1.2.1 vctrs_0.3.6
[43] svglite_1.2.3.2 ExperimentHub_1.16.1
[45] DelayedMatrixStats_1.12.3 xfun_0.21
[47] beachmat_2.6.4 mime_0.10
[49] lifecycle_1.0.0 irlba_2.3.3
[51] statmod_1.4.35 AnnotationHub_2.22.1
[53] edgeR_3.32.1 zlibbioc_1.36.0
[55] scales_1.1.1 promises_1.2.0.1
[57] rhdf5_2.34.0 yaml_2.2.1
[59] curl_4.3 memoise_2.0.0
[61] gridExtra_2.3 stringi_1.5.3
[63] RSQLite_2.2.3 highr_0.8
[65] BiocVersion_3.12.0 systemfonts_1.0.1
[67] rlang_0.4.10 pkgconfig_2.0.3
[69] bitops_1.0-6 evaluate_0.14
[71] lattice_0.20-41 Rhdf5lib_1.12.1
[73] htmlwidgets_1.5.3 bit_4.0.4
[75] tidyselect_1.1.0 here_1.0.1
[77] magrittr_2.0.1 R6_2.5.0
[79] generics_0.1.0 DelayedArray_0.16.3
[81] DBI_1.1.1 pillar_1.4.7
[83] whisker_0.4 withr_2.4.1
[85] RCurl_1.98-1.2 tibble_3.0.6
[87] crayon_1.4.1 plotly_4.9.3
[89] BiocFileCache_1.14.0 rmarkdown_2.6
[91] viridis_0.5.1 locfit_1.5-9.4
[93] grid_4.0.3 data.table_1.13.6
[95] blob_1.2.1 git2r_0.28.0
[97] digest_0.6.27 xtable_1.8-4
[99] tidyr_1.1.2 httpuv_1.5.5
[101] R.utils_2.10.1 munsell_0.5.0
[103] beeswarm_0.2.3 viridisLite_0.3.0
[105] vipor_0.4.5