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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 <- FALSE

Process empirical data

Convert to SCE object

The 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.rds

Filter cells and genes

This 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") }

sce
class: 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                    18

Aggregate into population wide data

For 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        13.23514212

Get cells to estimate single-cell parameters

Use 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") }

Get distribution of the number of cells per donor

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] NA

For 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] NA

Estimate splatPop parameters

Single-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")}

params
A 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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 gtable                 0.3.0    2019-03-25 [1] CRAN (R 4.0.2)
 haven                  2.4.0    2021-04-14 [1] CRAN (R 4.0.4)
 hms                    1.0.0    2021-01-13 [1] CRAN (R 4.0.4)
 htmltools              0.5.1.1  2021-01-22 [1] CRAN (R 4.0.3)
 httpuv                 1.5.5    2021-01-13 [1] CRAN (R 4.0.4)
 httr                   1.4.2    2020-07-20 [1] CRAN (R 4.0.2)
 IRanges              * 2.24.0   2020-10-27 [1] Bioconductor  
 irlba                  2.3.3    2019-02-05 [1] CRAN (R 4.0.2)
 jquerylib              0.1.3    2020-12-17 [1] CRAN (R 4.0.3)
 jsonlite               1.7.2    2020-12-09 [1] CRAN (R 4.0.4)
 knitr                  1.32     2021-04-14 [1] CRAN (R 4.0.4)
 later                  1.1.0.1  2020-06-05 [1] CRAN (R 4.0.2)
 lattice                0.20-41  2020-04-02 [2] CRAN (R 4.0.4)
 lifecycle              1.0.0    2021-02-15 [1] CRAN (R 4.0.4)
 limma                  3.46.0   2020-10-27 [1] Bioconductor  
 locfit                 1.5-9.4  2020-03-25 [1] CRAN (R 4.0.2)
 lubridate              1.7.10   2021-02-26 [1] CRAN (R 4.0.4)
 magrittr               2.0.1    2020-11-17 [1] CRAN (R 4.0.3)
 MASS                 * 7.3-53.1 2021-02-12 [1] CRAN (R 4.0.4)
 Matrix               * 1.3-2    2021-01-06 [1] CRAN (R 4.0.2)
 MatrixGenerics       * 1.2.0    2020-10-27 [1] Bioconductor  
 matrixStats          * 0.57.0   2020-09-25 [1] CRAN (R 4.0.2)
 memoise                2.0.0    2021-01-26 [1] CRAN (R 4.0.4)
 modelr                 0.1.8    2020-05-19 [1] CRAN (R 4.0.2)
 munsell                0.5.0    2018-06-12 [1] CRAN (R 4.0.2)
 pillar                 1.6.0    2021-04-13 [1] CRAN (R 4.0.4)
 pkgbuild               1.2.0    2020-12-15 [1] CRAN (R 4.0.4)
 pkgconfig              2.0.3    2019-09-22 [1] CRAN (R 4.0.2)
 pkgload                1.2.1    2021-04-06 [1] CRAN (R 4.0.4)
 prettyunits            1.1.1    2020-01-24 [1] CRAN (R 4.0.2)
 processx               3.5.1    2021-04-04 [1] CRAN (R 4.0.4)
 promises               1.2.0.1  2021-02-11 [1] CRAN (R 4.0.4)
 ps                     1.6.0    2021-02-28 [1] CRAN (R 4.0.4)
 purrr                * 0.3.4    2020-04-17 [1] CRAN (R 4.0.2)
 R6                     2.5.0    2020-10-28 [1] CRAN (R 4.0.2)
 Rcpp                   1.0.6    2021-01-15 [1] CRAN (R 4.0.4)
 RCurl                  1.98-1.3 2021-03-16 [1] CRAN (R 4.0.4)
 readr                * 1.4.0    2020-10-05 [1] CRAN (R 4.0.2)
 readxl                 1.3.1    2019-03-13 [1] CRAN (R 4.0.2)
 remotes                2.3.0    2021-04-01 [1] CRAN (R 4.0.4)
 reprex                 2.0.0    2021-04-02 [1] CRAN (R 4.0.4)
 rlang                  0.4.10   2020-12-30 [1] CRAN (R 4.0.4)
 rmarkdown              2.7      2021-02-19 [1] CRAN (R 4.0.4)
 rprojroot              2.0.2    2020-11-15 [1] CRAN (R 4.0.3)
 rstudioapi             0.13     2020-11-12 [1] CRAN (R 4.0.3)
 rsvd                   1.0.5    2021-04-16 [1] CRAN (R 4.0.4)
 rvest                  1.0.0    2021-03-09 [1] CRAN (R 4.0.4)
 S4Vectors            * 0.28.0   2020-10-27 [1] Bioconductor  
 sass                   0.3.1    2021-01-24 [1] CRAN (R 4.0.3)
 scales                 1.1.1    2020-05-11 [1] CRAN (R 4.0.2)
 scater               * 1.18.6   2021-02-26 [1] Bioconductor  
 scuttle                1.0.3    2020-11-23 [1] Bioconductor  
 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