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| Rmd | b1e4853 | cazodi | 2021-03-16 | update plot functions |
suppressPackageStartupMessages({
library(data.table)
library(SingleCellExperiment)
library(scater)
library(tidyverse)
library(Matrix)
library(splatter)
library(fitdistrplus)
})set.seed(42)
n.genes <- 504
save <- FALSEThe empirical data and the preprocessing performed is described in Jerber et al., 2021. The processed data was downloaded from Zenodo as an h5. This was converted to an SCE object using in house code:
wget https://zenodo.org/record/4333872/files/D30.h5?download=1 data/.
Rscript EUUI_2019_sceQTL-Workflow/code/scanpy_to_SCE.R -h5 data/D30.h5 -od data/ -s sce_Neuro-10x.rdsThis object contins count data for 32,738 genes for 250923 cells from 175 individuals sequenced in 12 pools. The cells are annotated as being one of seven cell types. For our reference data we keep cells from the two most abundant celltypes (Floor Plate Progenitors, FPPs, n=58k; dopaminergic neurons, DAs, n=69k) sequenced in the batch with the most cells (pool7, n=17,229). This leaves 17229 cells from 22 individuals (cells per sample: min=119, mean=783, max=2548). After cell filtering, genes were filtered to keep only genes with non-zero counts in at least 0.5% of cells (as in Jerber et al), leaving 13086 genes. Then the n.genes used for this study (1255) were randomly selected.
Snapshot of Neuro-10x reference data after filtering:
# Get subset of data to use for estimating params
sce <- readRDS("data/sce_Neuro-10x.rds")
# Keep two most abundant cell types
# table(sce$celltype) #DA=69007 FPP=58109
sce <- subset(sce, , celltype %in% c("DA", "FPP"))
# Keep largest batch
#sort(table(sce$pool_id))
sce <- subset(sce, , pool_id == "pool7")
# Keep genes with non-zero count in at least 0.05% of cells (Jerber et al)
genes.keep <- rowSums(counts(sce) != 0) >= ncol(sce) * 0.005
# table(genes.keep) # 13086 genes meet this criteria
sce <- sce[genes.keep, ]
# Randomly select n genes
sce <- sce[sample(rownames(sce), n.genes), ]
# Standardize col data names
colnames(colData(sce))[colnames(colData(sce)) == "celltype"] <- "Group"
colnames(colData(sce))[colnames(colData(sce)) == "donor_id"] <- "Sample"
colnames(colData(sce))[colnames(colData(sce)) == "pool_id"] <- "Batch"
counts(sce) <- as.matrix(counts(sce))
if(save){ saveRDS(sce, file = "data/sce_Neuro-10x_2CT.rds") }
sceclass: SingleCellExperiment
dim: 504 17229
metadata(0):
assays(1): counts
rownames(504): AC090627.1 GRIK5 ... ANKRD26 RP11-849I19.1
rowData names(0):
colnames(17229): AAACCTGAGAACTCGG-1-13 AAACCTGAGCAGGCTA-1-13 ...
TTTGTCATCACATGCA-1-16 TTTGTCATCAGTTCGA-1-16
colData names(9): index sample_index ... Batch treatment
reducedDimNames(0):
altExpNames(0):counts(sce[1:5,1:5]) AAACCTGAGAACTCGG-1-13 AAACCTGAGCAGGCTA-1-13 AAACCTGAGTACGTTC-1-13
AC090627.1 0 0 0
GRIK5 0 0 0
TREX1 0 1 0
C7orf13 0 0 0
HSP90AA1 5 17 14
AAACCTGGTAGGGACT-1-13 AAACCTGGTAGTAGTA-1-13
AC090627.1 0 0
GRIK5 0 0
TREX1 0 0
C7orf13 0 0
HSP90AA1 25 18For population wide gene mean and variance parameters we do not want to add additional variance by including multiple celltypes in the data, thus we will only use the most abundant cell type (DA, n=13,186) for the population wide aggregation. Only one sample (uupc_2) had fewer than 100 DA cells and was removed for this data.
sce.DA <- subset(sce, , Group == "DA")
keepSamples <- names(table(sce.DA$Sample)[table(sce.DA$Sample) > 100])
sce.DA100 <- subset(sce.DA, , Sample %in% keepSamples)
agg.10x <- aggregateAcrossCells(sce.DA100, ids = sce.DA100$Sample, statistics="mean")
if(save){ saveRDS(agg.10x, file = "data/agg_Neuro-10x_DA.rds") }
counts(agg.10x[1:5,1:5]) HPSI0114i-wegi_1 HPSI0115i-iuad_2 HPSI0115i-sehp_2 HPSI0215i-hipn_1
AC090627.1 0.02167183 0.02237136 0.06630824 0.07379135
GRIK5 0.08049536 0.06711409 0.09050179 0.08524173
TREX1 0.08359133 0.03803132 0.05107527 0.01653944
C7orf13 0.12383901 0.14093960 0.16487455 0.10941476
HSP90AA1 13.40866873 12.58836689 14.09856631 16.03435115
HPSI0215i-uiao_2
AC090627.1 0.04909561
GRIK5 0.08010336
TREX1 0.03359173
C7orf13 0.13436693
HSP90AA1 13.23514212Use cells from sample with most DA cells (HPSI0614i-wihj_4, n=2268)
sort(table(sce.DA$Sample))
HPSI0115i-uupc_2 HPSI0514i-wiii_3 HPSI0514i-yewo_4 HPSI0614i-juzt_4
87 125 136 138
HPSI0914i-yuvg_2 HPSI0714i-keui_4 HPSI1014i-tixi_4 HPSI0514i-naah_2
225 288 301 313
HPSI0114i-wegi_1 HPSI0215i-uiao_2 HPSI0914i-zerv_7 HPSI0414i-seru_1
323 387 420 428
HPSI0115i-iuad_2 HPSI0414i-xojn_3 HPSI0514i-sohd_2 HPSI0514i-rutc_2
447 461 471 506
HPSI0814i-siqu_4 HPSI0215i-hipn_1 HPSI1114i-kuul_1 HPSI0115i-sehp_2
783 786 989 1116
HPSI1014i-roug_3 HPSI0614i-wihj_4
2188 2268 sce.wihj4 <- subset(sce.DA, , Sample == "HPSI0614i-wihj_4")
if(save){ saveRDS(sce.wihj4, file = "data/sce_Neuro-10x_DA-wihj4.rds") }For DA cells: shape = 1.532930934, rate = 0.002557208
n.DA <- as.numeric(unname(table(sce.DA$Sample)))
summary(fitdist(n.DA, distr = "gamma", method = "mle", lower = c(0, 0)))Fitting of the distribution ' gamma ' by maximum likelihood
Parameters :
estimate Std. Error
shape 1.532930934 NA
rate 0.002557208 NA
Loglikelihood: -161.6325 AIC: 327.265 BIC: 329.4471
Correlation matrix:
[1] NAFor FPP cells: shape = 1.080982011, rate = 0.005884068
sce.FPP <- subset(sce, , Group == "FPP")
n.FPP <- as.numeric(unname(table(sce.FPP$Sample)))
summary(fitdist(n.FPP, distr = "gamma", method = "mle", lower = c(0, 0)))Fitting of the distribution ' gamma ' by maximum likelihood
Parameters :
estimate Std. Error
shape 1.080982011 NA
rate 0.005884068 NA
Loglikelihood: -136.66 AIC: 277.32 BIC: 279.502
Correlation matrix:
[1] NASingle-cell parameters are estimated from scRNA-seq data from cells from the donor with the most cells (HPSI0614i-wihj_4) 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 from DA cells only. When mean aggregation is used we set pop.quant.norm to False because quantile normalization is not needed.
Parameters estimated for Neuro-10x:
params <- newSplatPopParams(pop.cv.bins = 50)
# Estimate params from single-cell population-scale data
params <- splatPopEstimate(params = params,
counts = as.matrix(counts(sce.wihj4)),
means = as.matrix(counts(agg.10x)))
params <- setParams(params, pop.quant.norm = FALSE)
# Save parameter files
if(save){ saveRDS(params, file = "output/01_sims/splatPop-params_Neuro-10x.rds")}
paramsA 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 2268 617511
53 additional parameters
Batches:
[BATCHES] [BATCH CELLS] [Location] [Scale] [Remove]
1 2268 0.1 0.1 FALSE
Mean:
(RATE) (SHAPE)
3.32657467596678 0.60912244170244
Library size:
(LOCATION) (SCALE) (Norm)
5.39700653849036 0.160666914198516 FALSE
Exprs outliers:
(PROBABILITY) (LOCATION) (SCALE)
0.0218687872763419 4.40374693749414 0.714296444062618
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)
0.152945761376577 63.6282262338234
Dropout:
[Type] (MIDPOINT) (SHAPE)
none -0.284905276922353 -1.17852570735244
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]
0.411179662876732 0.915576478884146 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 0.00000 0.00526 7.733047 7.252956
2 0.00526 0.00705 30.043687 39.862890
3 0.00705 0.00809 17.577106 25.013988
4 0.00809 0.00988 48.156110 67.715723
# ... 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 2021-05-17
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stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.0.2)
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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