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make-data_spatialLIBD.R
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make-data_spatialLIBD.R
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## Run this on JHPCE at
## /dcl02/lieber/ajaffe/SpatialTranscriptomics/HumanPilot/Analysis/spatialLIBD_files
library('SingleCellExperiment')
library('here')
library('sessioninfo')
## Output directory
dir.create(here('Analysis',
'spatialLIBD_files'), showWarnings = FALSE)
## Load data
load(here(
'Analysis',
'Human_DLPFC_Visium_processedData_sce_scran.Rdata'
),
verbose = TRUE)
load(here('Analysis',
'rda_scran',
'clust_k5_list.Rdata'),
verbose = TRUE)
load(
here(
'Analysis',
'rda_scran',
'clust_10x_layer_maynard_martinowich.Rdata'
),
verbose = TRUE
)
load(here('Analysis', 'Layer_Guesses', 'rda',
'layer_guess_tab.Rdata'),
verbose = TRUE)
## From the original spatialLIBD code
clust_k5 <- do.call(cbind, clust_k5_list)
colnames(clust_k5) <- paste0('SNN_k50_', colnames(clust_k5))
rownames(clust_k5) <- NULL
colData(sce) <- cbind(colData(sce), clust_k5)
sce$Cluster10X <- sce$Cluster
sce$Maynard <- clust_10x_layer_maynard
sce$Martinowich <- clust_10x_layer_martinowich
sce$Layer <- "NA"
## From https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/layer_specificity.R
sce$layer_guess <- NA
m <- match(sce$key, layer_guess_tab$key)
sce$layer_guess[!is.na(m)] <- layer_guess_tab$layer[m[!is.na(m)]]
sce$layer_guess[which(sce$layer_guess == 'Layer 2/3')] <- 'Layer 3'
sce$layer_guess <-
factor(gsub(' ', '', sce$layer_guess), levels = c('WM', paste0('Layer', seq_len(6))))
sce$layer_guess_reordered <-
factor(sce$layer_guess, levels = c(paste0('Layer', seq_len(6)), 'WM'))
sce$layer_guess_reordered_short <- sce$layer_guess_reordered
levels(sce$layer_guess_reordered_short) <-
gsub('ayer', '', levels(sce$layer_guess_reordered))
## From https://github.com/LieberInstitute/HumanPilot/blob/master/Analysis/Layer_Guesses/misc_numbers.R
ix_mito <- grep("^MT-", rowData(sce)$gene_name)
sce$expr_chrM <- colSums(assays(sce)$counts[ix_mito,])
sce$expr_chrM_ratio <- sce$expr_chrM / sce$sum_umi
## Manually compare vs the info from the other script
summary(sce$expr_chrM_ratio)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.04853 0.15465 0.18442 0.18554 0.21521 0.44156
## Fix the rowRanges
## From https://github.com/LieberInstitute/HumanPilot/blob/c8a3a31b991081d656ededee59da45aa0494b334/Analysis/Layer_Notebook.R#L78-L87
map = read.delim(
here(
"/10X/151675/151675_raw_feature_bc_matrix__features.tsv.gz"
),
as.is = TRUE,
header = FALSE,
col.names = c("EnsemblID", "Symbol", "Type")
)
## get GTF, this seems like what they used
gtf = rtracklayer::import(
"/dcl01/ajaffe/data/lab/singleCell/refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf"
)
gtf = gtf[gtf$type == "gene"]
names(gtf) = gtf$gene_id
gtf = gtf[map$EnsemblID]
seqlevels(gtf)[seq_len(25)] = paste0("chr", seqlevels(gtf)[seq_len(25)])
# mcols(gtf) = mcols(gtf)[,c(5:9)]
## Keep the non-empty mcols()
mcols_empty_n <- sapply(mcols(gtf), function(x)
sum(is.na(x)))
names(mcols_empty_n[mcols_empty_n == 0])
# [1] "source" "type" "gene_id" "gene_version" "gene_name"
# [6] "gene_source" "gene_biotype"
mcols(gtf) <- mcols(gtf)[, names(mcols_empty_n[mcols_empty_n == 0])]
## Check the order
stopifnot(identical(rownames(sce), names(gtf)))
## Replace the rowRanges info
rowRanges(sce) <- gtf
## To simplify other code later
rowData(sce)$gene_search <-
paste0(rowData(sce)$gene_name, '; ', rowData(sce)$gene_id)
## Double check our selection of chrM genes now that we have the
## correct rowRanges data
stopifnot(identical(which(seqnames(sce) == 'chrMT'), ix_mito))
## Add whether the gene is a top highly variable gene (HVG) or not
rowData(sce)$is_top_hvg <- rownames(sce) %in% top.hvgs
## Add more cluster results?
## Likely the ones from Lukas
## initial k-means ones from 10x?
save(
sce,
file = here(
'Analysis',
'spatialLIBD_files',
'Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata'
)
)
## Now for the sce_layer object
## note that this re-loads the top.hvgs object
load(here('Analysis', 'Layer_Guesses', 'rda', 'sce_layer.Rdata'),
verbose = TRUE)
## Fix the rowRanges info
rowRanges(sce_layer) <-
rowRanges(sce)[match(rownames(sce_layer), rownames(sce)), ]
## Save the top HVG (layer-level) info
rowData(sce_layer)$is_top_hvg_sce_layer <-
rownames(sce_layer) %in% top.hvgs
## For the different plots
sce_layer$layer_guess_reordered <-
factor(sce_layer$layer_guess, levels = c(paste0('Layer', seq_len(6)), 'WM'))
sce_layer$layer_guess_reordered_short <-
sce_layer$layer_guess_reordered
levels(sce_layer$layer_guess_reordered_short) <-
gsub('ayer', '', levels(sce_layer$layer_guess_reordered))
save(
sce_layer,
file = here(
'Analysis',
'spatialLIBD_files',
'Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata'
)
)
## Also the modeling results
load(here('Analysis',
'Layer_Guesses',
'rda',
'modeling_results.Rdata'),
verbose = TRUE)
modeling_results <- list('anova' = results_anova,
'enrichment' = results_specificity,
'pairwise' = results_pairwise)
save(
modeling_results,
file = here(
'Analysis',
'spatialLIBD_files',
'Human_DLPFC_Visium_modeling_results.Rdata'
)
)
## List all files
dir(here('Analysis',
'spatialLIBD_files'))
# [1] "Human_DLPFC_Visium_modeling_results.Rdata"
# [2] "Human_DLPFC_Visium_processedData_sce_scran_sce_layer_spatialLIBD.Rdata"
# [3] "Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata"
######################################
##### Run locally (not on JHPCE) #####
######################################
usethis::use_directory("data-raw/spatialLIBD_files", ignore = TRUE)
system2('scp',
paste0(
'e:/dcl02/lieber/ajaffe/SpatialTranscriptomics/HumanPilot/Analysis/spatialLIBD_files/* ',
here::here('data-raw/spatialLIBD_files'),
'/'
)
)
## Update on 2020-02-21 the modeling results only
# system(
# paste0(
# 'scp e:/dcl02/lieber/ajaffe/SpatialTranscriptomics/HumanPilot/Analysis/spatialLIBD_files/Human_DLPFC_Visium_modeling_results.Rdata ',
# here::here('data-raw/spatialLIBD_files'),
# '/'
# )
# )
system2('echo', paste(
'data-raw/spatialLIBD_files >>',
here::here('.gitignore')
))
## Add the clusters from Lukas M Weber and Stephanie Hicks
library('SingleCellExperiment')
load(
here::here(
'data-raw',
'spatialLIBD_files',
'Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata'
),
verbose = TRUE
)
local_cluster_csvs <-
dir(
'~/Dropbox/code/HumanPilot/outputs/SpatialDE_clustering/',
pattern = '^cluster_labels_.*.csv',
full.names = TRUE
)
csv_file <- local_cluster_csvs[1]
spatial_clus <-
do.call(rbind,
lapply(local_cluster_csvs, read.csv, stringsAsFactors = FALSE))
## It's too big to include as data in the pkg
pryr::object_size(spatial_clus)
# 8.01 MB
## Check the order
stopifnot(identical(spatial_clus$key, sce$key))
## Drop the 'key' and 'ground_truth' since we don't need it
spatial_clus <-
spatial_clus[, -which(colnames(spatial_clus) %in% c('key', 'ground_truth'))]
## Append
colData(sce) <- cbind(colData(sce), spatial_clus)
## Rename Cluster10X to something else
colnames(colData(sce))[colnames(colData(sce)) == 'Cluster10X'] <-
'GraphBased'
## Double check that it all works!
# sce_layer <-
# fetch_data('sce_layer', here::here('data-raw/spatialLIBD_files'))
# modeling_results <-
# fetch_data('modeling_results',
# here::here('data-raw/spatialLIBD_files'))
#
# sig_genes <-
# sig_genes_extract_all(n = nrow(sce_layer),
# modeling_results,
# sce_layer = sce_layer)
#
#
# spatialLIBD::run_app(
# sce = sce,
# sce_layer = sce_layer,
# modeling_results = modeling_results,
# sig_genes = sig_genes,
# sce_discrete_vars = c('GraphBased',
# 'Layer',
# 'Maynard',
# 'Martinowich',
# paste0('SNN_k50_k', 4:28), colnames(spatial_clus))
# )
## It does! =)
paste0("c('", paste(colnames(spatial_clus), collapse = "', '"), "')")
# c('SpatialDE_PCA', 'SpatialDE_pool_PCA', 'HVG_PCA', 'pseudobulk_PCA', 'markers_PCA', 'SpatialDE_UMAP', 'SpatialDE_pool_UMAP', 'HVG_UMAP', 'pseudobulk_UMAP', 'markers_UMAP', 'SpatialDE_PCA_spatial', 'SpatialDE_pool_PCA_spatial', 'HVG_PCA_spatial', 'pseudobulk_PCA_spatial', 'markers_PCA_spatial', 'SpatialDE_UMAP_spatial', 'SpatialDE_pool_UMAP_spatial', 'HVG_UMAP_spatial', 'pseudobulk_UMAP_spatial', 'markers_UMAP_spatial')"
## Overwrite the file
save(
sce,
file = here::here(
'data-raw',
'spatialLIBD_files',
'Human_DLPFC_Visium_processedData_sce_scran_spatialLIBD.Rdata'
)
)
## Reproducibility information
print('Reproducibility information:')
Sys.time()
proc.time()
options(width = 120)
session_info()
# ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
# setting value
# version R version 3.6.1 Patched (2019-10-31 r77350)
# 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 2020-02-19
#
# ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
# package * version date lib source
# assertthat 0.2.1 2019-03-21 [2] CRAN (R 3.6.1)
# backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.1)
# Biobase * 2.46.0 2019-10-29 [2] Bioconductor
# BiocGenerics * 0.32.0 2019-10-29 [1] Bioconductor
# BiocParallel * 1.20.1 2019-12-21 [1] Bioconductor
# Biostrings 2.54.0 2019-10-29 [1] Bioconductor
# bitops 1.0-6 2013-08-17 [2] CRAN (R 3.6.1)
# cli 2.0.0 2019-12-09 [1] CRAN (R 3.6.1)
# colorout * 1.2-2 2019-10-31 [1] Github (jalvesaq/colorout@641ed38)
# colorspace 1.4-1 2019-03-18 [2] CRAN (R 3.6.1)
# crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.1)
# DelayedArray * 0.12.2 2020-01-06 [2] Bioconductor
# digest 0.6.23 2019-11-23 [1] CRAN (R 3.6.1)
# fansi 0.4.0 2018-10-05 [1] CRAN (R 3.6.1)
# GenomeInfoDb * 1.22.0 2019-10-29 [1] Bioconductor
# GenomeInfoDbData 1.2.2 2019-10-28 [2] Bioconductor
# GenomicAlignments 1.22.1 2019-11-12 [1] Bioconductor
# GenomicRanges * 1.38.0 2019-10-29 [1] Bioconductor
# ggplot2 3.2.1 2019-08-10 [1] CRAN (R 3.6.1)
# glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.1)
# gtable 0.3.0 2019-03-25 [2] CRAN (R 3.6.1)
# here * 0.1 2017-05-28 [1] CRAN (R 3.6.1)
# htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.1)
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# later 1.0.0 2019-10-04 [1] CRAN (R 3.6.1)
# lattice 0.20-38 2018-11-04 [3] CRAN (R 3.6.1)
# lazyeval 0.2.2 2019-03-15 [2] CRAN (R 3.6.1)
# lifecycle 0.1.0 2019-08-01 [1] CRAN (R 3.6.1)
# magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.1)
# Matrix 1.2-17 2019-03-22 [3] CRAN (R 3.6.1)
# matrixStats * 0.55.0 2019-09-07 [1] CRAN (R 3.6.1)
# munsell 0.5.0 2018-06-12 [2] CRAN (R 3.6.1)
# pillar 1.4.3 2019-12-20 [1] CRAN (R 3.6.1)
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# rmote * 0.3.4 2019-10-31 [1] Github (cloudyr/rmote@fbce611)
# rprojroot 1.3-2 2018-01-03 [2] CRAN (R 3.6.1)
# Rsamtools 2.2.1 2019-11-06 [1] Bioconductor
# rtracklayer 1.46.0 2019-10-29 [1] Bioconductor
# S4Vectors * 0.24.1 2019-12-01 [1] Bioconductor
# scales 1.1.0 2019-11-18 [2] CRAN (R 3.6.1)
# servr 0.15 2019-08-07 [1] CRAN (R 3.6.1)
# sessioninfo * 1.1.1 2018-11-05 [1] CRAN (R 3.6.1)
# SingleCellExperiment * 1.8.0 2019-10-29 [2] Bioconductor
# SummarizedExperiment * 1.16.1 2019-12-19 [1] Bioconductor
# tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.1)
# withr 2.1.2 2018-03-15 [2] CRAN (R 3.6.1)
# xfun 0.11 2019-11-12 [1] CRAN (R 3.6.1)
# XML 3.99-0.3 2020-01-20 [2] CRAN (R 3.6.1)
# XVector 0.26.0 2019-10-29 [1] Bioconductor
# zlibbioc 1.32.0 2019-10-29 [2] Bioconductor
#
# [1] /users/lcollado/R/3.6.x
# [2] /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-3.6.x/R/3.6.x/lib64/R/site-library
# [3] /jhpce/shared/jhpce/core/conda/miniconda3-4.6.14/envs/svnR-3.6.x/R/3.6.x/lib64/R/library