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merge_spe.R
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merge_spe.R
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## This script requires R 4.1
# module load conda_R/4.1.x
## utils
library("here")
library("sessioninfo")
## reading the data
library("SpatialExperiment")
## vis
library("spatialLIBD")
## Define some info for the samples
sample_info <- data.frame(
sample_id = c(
"DLPFC_Br2743_ant_manual_alignment",
"DLPFC_Br2743_mid_manual_alignment_extra_reads",
"DLPFC_Br2743_post_manual_alignment",
"DLPFC_Br3942_ant_manual_alignment",
"DLPFC_Br3942_mid_manual_alignment",
"DLPFC_Br3942_post_manual_alignment",
"DLPFC_Br6423_ant_manual_alignment_extra_reads",
"DLPFC_Br6423_mid_manual_alignment",
"DLPFC_Br6423_post_extra_reads",
"DLPFC_Br8492_ant_manual_alignment",
"DLPFC_Br8492_mid_manual_alignment_extra_reads",
"DLPFC_Br8492_post_manual_alignment",
"Round2/DLPFC_Br2720_ant_manual_alignment",
"Round2/DLPFC_Br2720_mid_manual_alignment",
"Round2/DLPFC_Br2720_post_extra_reads",
"Round2/DLPFC_Br6432_ant_manual_alignment",
"Round2/DLPFC_Br6432_mid_manual_alignment",
"Round2/DLPFC_Br6432_post_manual_alignment",
"Round3/DLPFC_Br6471_ant_manual_alignment_all",
"Round3/DLPFC_Br6471_mid_manual_alignment_all",
"Round3/DLPFC_Br6471_post_manual_alignment_all",
"Round3/DLPFC_Br6522_ant_manual_alignment_all",
"Round3/DLPFC_Br6522_mid_manual_alignment_all",
"Round3/DLPFC_Br6522_post_manual_alignment_all",
"Round3/DLPFC_Br8325_ant_manual_alignment_all",
"Round3/DLPFC_Br8325_mid_manual_alignment_all",
"Round3/DLPFC_Br8325_post_manual_alignment_all",
"Round3/DLPFC_Br8667_ant_extra_reads",
"Round3/DLPFC_Br8667_mid_manual_alignment_all",
"Round3/DLPFC_Br8667_post_manual_alignment_all",
"Round4/DLPFC_Br2720_ant_2",
"Round4/DLPFC_Br6432_ant_2",
"Round4/DLPFC_Br8325_mid_2"
),
subjects = c(rep(
c("Br2743", "Br3942", "Br6423", "Br8492", "Br2720","Br6432","Br6471","Br6522","Br8325","Br8667"),
each = 3
),"Br2720","Br6432","Br8325"),
regions = c(rep(
c("anterior", "middle", "posterior"),
10
),"anterior","anterior","middle"),
sex = c(rep(
c("M", "M", "M", "F","F","M","M","M","F","F"),
each = 3
),"F","M","F"),
age = c(rep(
c(61.54, 47.53, 51.73, 53.40,48.22,48.88,55.46,33.39,57.62,37.33),
each = 3
),48.22,48.88,57.62),
diagnosis = "Control"
)
sample_info$sample_path <- file.path(
here::here("processed-data", "NextSeq"),
sample_info$sample_id,
"outs"
)
stopifnot(all(file.exists(sample_info$sample_path)))
#clean up sample_id
sample_info$sample_id <- gsub("_all|_extra_reads|DLPFC_|_manual_alignment", "", basename(sample_info$sample_id))
## Build SPE object
Sys.time()
spe_wrapper <- read10xVisiumWrapper(
sample_info$sample_path,
sample_info$sample_id,
type = "sparse",
data = "raw",
images = c("lowres", "hires", "detected", "aligned"),
load = TRUE,
reference_gtf = "/dcl02/lieber/ajaffe/SpatialTranscriptomics/refdata-gex-GRCh38-2020-A/genes/genes.gtf"
)
Sys.time()
# 2021-11-23 15:05:05 SpatialExperiment::read10xVisium: reading basic data from SpaceRanger
# 2021-11-23 15:12:23 read10xVisiumAnalysis: reading analysis output from SpaceRanger
# 2021-11-23 15:13:09 add10xVisiumAnalysis: adding analysis output from SpaceRanger
# 2021-11-23 15:13:15 rtracklayer::import: reading the reference GTF file
# 2021-11-23 15:14:03 adding gene information to the SPE object
# 2021-11-23 15:14:04 adding information used by spatialLIBD
## Add the experimental information
### Equivalent to:
## colData(spe_wrapper)$subject <- ...
spe_wrapper$subject <- sample_info$subjects[match(spe_wrapper$sample_id, sample_info$sample_id)]
spe_wrapper$region <- sample_info$regions[match(spe_wrapper$sample_id, sample_info$sample_id)]
spe_wrapper$sex <- sample_info$sex[match(spe_wrapper$sample_id, sample_info$sample_id)]
spe_wrapper$age <- sample_info$age[match(spe_wrapper$sample_id, sample_info$sample_id)]
spe_wrapper$diagnosis <- sample_info$diagnosis[match(spe_wrapper$sample_id, sample_info$sample_id)]
## Remove genes with no data
no_expr <- which(rowSums(counts(spe_wrapper)) == 0)
length(no_expr)
# [1] 7468
length(no_expr) / nrow(spe_wrapper) * 100
# [1] 20.40381
#remove genes that are not expressed in any spots
spe_wrapper <- spe_wrapper[-no_expr, ]
spe_wrapper <- spe_wrapper[, which(spatialData(spe_wrapper)$in_tissue=="TRUE")]
## Remove spots without counts
spe_wrapper <- spe_wrapper[, -which(colSums(counts(spe_wrapper)) == 0)]
## Read in cell counts and segmentation results
segmentations_list <- lapply(sample_info$sample_id, function(sampleid) {
current<-sample_info$sample_path[sample_info$sample_id==sampleid]
file <- file.path(current, "spatial", "tissue_spot_counts.csv")
if(!file.exists(file)) return(NULL)
x <- read.csv(file)
x$key <- paste0(x$barcode, "_", sampleid)
return(x)
})
## Merge them (once the these files are done, this could be replaced by an rbind)
segmentations <- Reduce(function(...) merge(..., all = TRUE), segmentations_list[lengths(segmentations_list) > 0])
## Add the information
segmentation_match <- match(spe_wrapper$key, segmentations$key)
segmentation_info <- segmentations[segmentation_match, - which(colnames(segmentations) %in% c("barcode", "tissue", "row", "col", "imagerow", "imagecol", "key")), drop=FALSE]
colData(spe_wrapper) <- cbind(colData(spe_wrapper), segmentation_info)
load(here::here("processed-data","rdata", "spe", "spe_final.Rdata"),verbose = TRUE)
spe$cell_count <- NULL
#fixing the key since spe$Barcode doesn't exist anymore
spe$key <- paste0(colnames(spe), '_', spe$sample_id)
#checks that all spots are in the same order
stopifnot(identical(spe_wrapper$key,spe$key))
#fix rowRanges
rowData(spe)$gene_search <- paste0(rowData(spe)$gene_name, "; ", rowData(spe)$gene_id)
#checks that all the genes are in the same order
stopifnot(identical(rowRanges(spe_wrapper)$gene_search,rowRanges(spe)$gene_search))
##adding missing elements
logcounts(spe_wrapper) <- logcounts(spe)
rowRanges(spe_wrapper)$is.HVG <- rowRanges(spe)$is.HVG
stopifnot(identical(rowRanges(spe_wrapper),rowRanges(spe)))
reducedDims(spe_wrapper) <- c(reducedDims(spe),reducedDims(spe_wrapper))
spe$BayesSpace_cluster.init <- spe$cluster.init
spe$cluster.init <-NULL
spe$BayesSpace <- spe$spatial.cluster
spe$spatial.cluster <-NULL
colData(spe_wrapper) <- cbind(colData(spe_wrapper),colData(spe)[,!colnames(colData(spe))%in%colnames(colData(spe_wrapper))])
spe<-spe_wrapper
save(spe, file = here::here("processed-data", "rdata", "spe", "spe_merged_final.Rdata"))
## Reproducibility information
print('Reproducibility information:')
Sys.time()
proc.time()
options(width = 120)
session_info()
# ─ Session info ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
# hash: fuel pump, flag: Botswana, person walking: dark skin tone
#
# setting value
# version R version 4.1.2 Patched (2021-11-04 r81138)
# os CentOS Linux 7 (Core)
# system x86_64, linux-gnu
# ui X11
# language (EN)
# collate en_US.UTF-8
# ctype en_US.UTF-8
# tz US/Eastern
# date 2021-11-23
# pandoc 2.13 @ /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-4.1.x/bin/pandoc
#
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# viridis 0.6.2 2021-10-13 [2] CRAN (R 4.1.2)
# viridisLite 0.4.0 2021-04-13 [2] CRAN (R 4.1.0)
# withr 2.4.2 2021-04-18 [2] CRAN (R 4.1.0)
# xfun 0.28 2021-11-04 [2] CRAN (R 4.1.2)
# XML 3.99-0.8 2021-09-17 [2] CRAN (R 4.1.2)
# xml2 1.3.2 2020-04-23 [2] CRAN (R 4.1.0)
# xtable 1.8-4 2019-04-21 [2] CRAN (R 4.1.0)
# XVector 0.34.0 2021-10-26 [2] Bioconductor
# yaml 2.2.1 2020-02-01 [2] CRAN (R 4.1.0)
# zlibbioc 1.40.0 2021-10-26 [2] Bioconductor
#
# [1] /users/aspangle/R/4.1.x
# [2] /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-4.1.x/R/4.1.x/lib64/R/site-library
# [3] /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-4.1.x/R/4.1.x/lib64/R/library
#
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