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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ss2-iPSC_estimate-params.Rmd) and HTML (public/ss2-iPSC_estimate-params.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b1e4853 | cazodi | 2021-03-16 | update plot functions |
| Rmd | ae3af3e | cazodi | 2021-03-10 | update simple ss2 ipsc sim |
| html | ae3af3e | cazodi | 2021-03-10 | update simple ss2 ipsc sim |
| Rmd | d24c101 | cazodi | 2021-02-17 | ipsc ss2 preprocessing |
| html | d24c101 | cazodi | 2021-02-17 | ipsc ss2 preprocessing |
suppressPackageStartupMessages({
library(data.table)
library(SingleCellExperiment)
library(scater)
library(tidyverse)
library(Matrix)
library(splatter)
library(fitdistrplus)
})set.seed(42)
n.genes <- 504
save <- TRUEThe empirical data and the preprocessing performed is described in Cuomo et al., 2020. The processed data is available for download at Zenodo. The iPSC lines were multiplexed into 24 experimental pools (4-6 lines in each pool) and single cells were sequenced using the Smart-Seq2 protocol. Cells were assigned to their donor of origin using Cardelino. Here, we include only day0 cells (i.e. iPSC stage) that were successfully assigned to a donor and passed donor- and cell-level QC described below.
Snapshot of iPSC-ss2 reference data:
# Convert to SCE object
meta.loc <- "/mnt/mcfiles/Datasets/single-cell-data/cuomo_iPSC_endoderm/cell_metadata_cols.tsv"
counts.loc <- "/mnt/mcfiles/Datasets/single-cell-data/cuomo_iPSC_endoderm/counts.tsv"
meta <- read.csv(meta.loc, sep="\t", header=TRUE)
meta$X <- NULL
counts <- fread(counts.loc, sep="\t", header=TRUE)
counts <- as.data.frame(counts)
row.names(counts) <- counts$GeneID
counts$GeneID <- NULL
sce.ss <- SingleCellExperiment(assays = list(counts = counts),
colData = meta)
rm(counts)
# Standardize col data names
colnames(colData(sce.ss))[colnames(colData(sce.ss)) == "experiment"] <- "Batch"
colnames(colData(sce.ss))[colnames(colData(sce.ss)) == "donor"] <- "Sample"
sce.ss$donor_exp <- paste(sce.ss$Sample, sce.ss$Batch, sep="_")
sce.ss <- subset(sce.ss, , (day == "day0"))
sce.ss <- sce.ss[sample(rownames(sce.ss), n.genes), ]
if(save){ saveRDS(sce.ss, file = "data/sce_iPSC-ss2_D0.rds") }
# Aggregate into population wide data
keepSamples <- names(table(sce.ss$Sample)[table(sce.ss$Sample) > 100])
sce.100 <- subset(sce.ss, , Sample %in% keepSamples)
agg.ss <- aggregateAcrossCells(sce.100, ids = sce.100$donor_exp, statistics="mean")
pb.ss <- aggregateAcrossCells(sce.100, ids = sce.100$donor_exp, statistics="sum")
if(save){
saveRDS(agg.ss, file = "data/agg_iPSC-ss2_D0.rds")
saveRDS(pb.ss, file = "data/pseudoB_iPSC-ss2_D0.rds")
}
sce.ssclass: SingleCellExperiment
dim: 504 9661
metadata(0):
assays(1): counts
rownames(504): ENSG00000110066_SUV420H1 ENSG00000128045_RASL11B ...
ENSG00000160326_SLC2A6 ENSG00000167528_ZNF641
rowData names(0):
colnames(9661): 21672_1#101 21672_1#102 ... 24475_8#96 24475_8#98
colData names(94): assigned auxDir ... princ_curve_scaled01 donor_exp
reducedDimNames(0):
altExpNames(0):counts(sce.ss[1:5,1:5]) 21672_1#101 21672_1#102 21672_1#103 21672_1#104
ENSG00000110066_SUV420H1 1.4547171 1.3348227 2.25280087 3.93828400
ENSG00000128045_RASL11B 6.4995706 6.4622821 4.85403378 4.02948001
ENSG00000107186_MPDZ 0.0000000 1.2726227 0.99701653 1.12888160
ENSG00000139437_TCHP 0.1073557 2.8248941 0.04746060 0.00000000
ENSG00000083290_ULK2 0.3624107 0.0494878 0.01252976 0.02203401
21672_1#105
ENSG00000110066_SUV420H1 2.31723710
ENSG00000128045_RASL11B 5.37921226
ENSG00000107186_MPDZ 2.51720554
ENSG00000139437_TCHP 0.09386073
ENSG00000083290_ULK2 0.24289306Samples with the most cells (use joxm):
# SCE for donor-experiment with most cells (joxm from exp_39)
n.samples <- sort(table(sce.ss$donor_exp), decreasing=TRUE)[1:5]
sce.ss.joxm39 <- subset(sce.ss, , donor_exp == "joxm_expt_39")
if(save){ saveRDS(sce.ss.joxm39, file = "data/sce_iPSC-ss2_D0-joxm39.rds") }
n.samples
joxm_expt_39 sojd_expt_38 letw_expt_28 nudd_expt_34 yelp_expt_43
383 321 232 178 174 Single-cell parameters are estimated from scRNA-seq data from cells from the donor with the most cells (joxm in experiment 39) using 1255 randomly selected genes. Down-sampling the number of genes to match the number of genes we will simulate ensures the estimated library size parameters reflect real data.
Population parameters are estimated from either mean aggregated or pseudo-bulked (i.e. sum aggregated) scRNA-seq data. When mean aggregation is used we set pop.quant.norm to False because quantile normalization is not needed.
Parameters estimated for ss2-iPSC:
params <- newSplatPopParams(pop.cv.bins = 50)
# Estimate params from single-cell population-scale data
params.ss2.sc <- splatPopEstimate(params = params,
counts = as.matrix(counts(sce.ss.joxm39)),
means = as.matrix(counts(agg.ss)))
params.ss2.sc <- setParams(params.ss2.sc, pop.quant.norm = FALSE)
# Estimate params from pseudo-bulk population-scale data
params.ss2.psuB <- splatPopEstimate(params = params,
counts = as.matrix(counts(sce.ss.joxm39)),
means = as.matrix(counts(pb.ss)))
# Save parameter files
if(save){
saveRDS(params.ss2.sc, file = "output/01_sims/splatPop-params_iPSC-ss2_sc.rds")
saveRDS(params.ss2.psuB, file = "output/01_sims/splatPop-params_iPSC-ss2_psudoB.rds")
}
params.ss2.scA Params object of class SplatPopParams
Parameters can be (estimable) or [not estimable], 'Default' or 'NOT DEFAULT'
Secondary parameters are usually set during simulation
Global:
(GENES) (CELLS) [Seed]
504 383 617511
53 additional parameters
Batches:
[BATCHES] [BATCH CELLS] [Location] [Scale] [Remove]
1 383 0.1 0.1 FALSE
Mean:
(RATE) (SHAPE)
0.649032851154963 2.04892069999893
Library size:
(LOCATION) (SCALE) (Norm)
7.34174641600733 0.0470967865430461 FALSE
Exprs outliers:
(PROBABILITY) (Location) (Scale)
0 4 0.5
Groups:
[Groups] [Group Probs]
1 1
Diff expr:
[Probability] [Down Prob] [Location] [Scale]
0.1 0.5 0.1 0.4
BCV:
(COMMON DISP) (DOF)
0.1000244140625 10.4660831656687
Dropout:
[Type] (MIDPOINT) (SHAPE)
none -0.127652372689195 -1.64027326023482
Paths:
[From] [Steps] [Skew] [Non-linear] [Sigma Factor]
0 100 0.5 0.1 0.8
Population params:
(MEAN.SHAPE) (MEAN.RATE) [POP.QUANT.NORM] [similarity.scale]
1.91469187560408 0.623241597574194 FALSE 1
[batch.size] [nCells.sample] [nCells.shape] [nCells.rate]
10 FALSE 1.5 0.015
[CV.BINS]
50
(CV.PARAMS)
data.frame (50 x 4) with columns: start, end, shape, rate
start end shape rate
1 0.000 0.238 8.491719 11.80614
2 0.238 0.513 56.190528 128.01391
3 0.513 0.585 82.076840 212.75538
4 0.585 0.684 35.909855 107.69844
# ... with 46 more rows
eQTL params:
[eqtl.n] [eqtl.dist]
0.5 1e+06
[eqtl.maf.min] [eqtl.maf.max]
0.05 0.5
[eqtl.group.specific] [eqtl.condition.specific]
0.2 0.2
(eqtl.ES.shape) (eqtl.ES.rate)
3.6 12
Condition params:
[nConditions] [condition.prob] [cde.prob] [cde.downProb]
1 1 0.1 0.5
[cde.facLoc] [cde.facScale]
0.1 0.4
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.4 (2021-02-15)
os Red Hat Enterprise Linux
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Australia/Melbourne
date 2021-05-13
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sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 4.0.2)
SingleCellExperiment * 1.12.0 2020-10-27 [1] Bioconductor
sparseMatrixStats 1.2.1 2021-02-02 [1] Bioconductor
splatter * 1.15.2 2021-04-15 [1] Bioconductor
stringi 1.5.3 2020-09-09 [1] CRAN (R 4.0.2)
stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
SummarizedExperiment * 1.20.0 2020-10-27 [1] Bioconductor
survival * 3.2-10 2021-03-16 [1] CRAN (R 4.0.4)
testthat 3.0.0 2020-10-31 [1] CRAN (R 4.0.2)
tibble * 3.1.1 2021-04-18 [1] CRAN (R 4.0.4)
tidyr * 1.1.3 2021-03-03 [1] CRAN (R 4.0.4)
tidyselect 1.1.0 2020-05-11 [1] CRAN (R 4.0.2)
tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.0.4)
usethis 1.6.3 2020-09-17 [1] CRAN (R 4.0.2)
utf8 1.2.1 2021-03-12 [1] CRAN (R 4.0.4)
vctrs 0.3.7 2021-03-29 [1] CRAN (R 4.0.4)
vipor 0.4.5 2017-03-22 [1] CRAN (R 4.0.2)
viridis 0.6.0 2021-04-15 [1] CRAN (R 4.0.4)
viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.0.4)
whisker 0.4 2019-08-28 [1] CRAN (R 4.0.2)
withr 2.4.2 2021-04-18 [1] CRAN (R 4.0.4)
workflowr 1.6.2 2020-04-30 [1] CRAN (R 4.0.2)
xfun 0.22 2021-03-11 [1] CRAN (R 4.0.4)
xml2 1.3.2 2020-04-23 [1] CRAN (R 4.0.2)
XVector 0.30.0 2020-10-27 [1] Bioconductor
yaml 2.2.1 2020-02-01 [1] CRAN (R 4.0.2)
zlibbioc 1.36.0 2020-10-27 [1] Bioconductor
[1] /mnt/mcfiles/cazodi/R/x86_64-pc-linux-gnu-library/4.0
[2] /opt/R/4.0.4/lib/R/library