Last updated: 2021-09-21

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Knit directory: KEJP_2020_splatPop/

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File Version Author Date Message
Rmd bd0c8b0 cazodi 2021-05-17 updates to 10x neuroseq and ss2 ipsc examples
html bd0c8b0 cazodi 2021-05-17 updates to 10x neuroseq and ss2 ipsc examples 
suppressPackageStartupMessages({
  library(tidyverse)
  library(RColorBrewer)
  library(ggpubr)
})

source("code/plot_functions.R")
source("code/misc_functions.R")
date <- Sys.Date()
save <- TRUE

eQTL mapping performance

For simulations with 100 samples simulated in 10 batches with 10 samples per batch. Simulations are described in detail at the bottom of the page here.

r <- read.table("output/demo_eQTL/02_limix/RESULTS.tsv", header=TRUE)

r %>% separate(id, into = c("date", "sim", "nCells"), sep= "_") %>%
  mutate(nCells = as.numeric(gsub("/", "", nCells)),
         power = recall) %>%
  distinct(nCells, .keep_all = TRUE) %>%
  pivot_longer(c("power", "FDR"), names_to="metric") %>%
  ggline(x = "nCells", y = "value", color = "metric", size=1,
  palette = projectColors("eqtl.metrics"), legend="right")
Change in eQTL mapping power (TP/TP+FN) and empirical false discovery rate (FP/FP+TP) as the number of cells per sample increases.

Change in eQTL mapping power (TP/TP+FN) and empirical false discovery rate (FP/FP+TP) as the number of cells per sample increases.

Version Author Date
bd0c8b0 cazodi 2021-05-17 
if(save){
  save.name <- paste0(date, "_ss2-eqtl-nCells")
  ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 5, height = 3)
}

Inspecting eQTL results

Properties of discovered eQTL

For simulations with 100 samples simulated in 10 batches with 10 samples per batch, with 80 cells per sample, we show the number of true positive (TP), false negatives (FN), false positives (FP), true negatives (TN), defined as:

  • TP: Simulated eGene, correct eSNP mapped significantly (p <= 0.05)
  • FN: Simulated eGene, no significant eQTL or correct eSNP not significant
  • FP: Not simulated as eGenes, but significant eQTL
  • TN: Not simulated as eGenes, no significant eQTL
rFull <- read.table("output/demo_eQTL/02_limix/2021-05-14_ss2-large_80/results_detailed.tsv",
                    header=TRUE)

rFull <- rFull %>% mutate(result = gsub("FP.2", "FN", result),
                          result = gsub("FP.1", "FP", result))
table(rFull$result)

 FN  FP  TN  TP 
 67   4 148 285

We can also inspect the properties of eQTL that are mapped correctly. For example, we see that eQTL are mapped more successfully when they have large effect sizes.

my_comparisons <- list( c("TP", "FP"), c("TP", "FN"))

rFull %>% mutate(mean = meanSampled, CV = cvSampled, MAF = eSNP.MAF, 
                 beta = eQTL.EffectSize) %>% 
  mutate(beta = na_if(beta, 0)) %>%
  pivot_longer(c("mean", "CV", "MAF", "beta"), names_to="factor") %>%
  mutate(result = gsub("FP.2", "FN", result),
         result = gsub("FP.1", "FP", result),
         value = abs(value),
         factor = factor(factor, c("mean", "CV", "beta", "MAF"))) %>% 
  filter(result %in% c("TP", "FP", "FN")) %>%
  mutate(result = factor(result, c("TP", "FN", "FP"))) %>%
  
  ggboxplot("result", "value", color="result", legend="none", 
           add="jitter", add.params = list(alpha=0.1),
           palette = projectColors("eqtl.result")) + 
  theme(axis.title.x = element_blank()) +
  geom_hline(aes(yintercept=0), linetype="dashed") +
  facet_wrap(. ~ factor, scales = "free_y", nrow=1) +
  stat_compare_means(aes(label = ..p.signif..),
                  method = "t.test", ref.group = "TP")
Simulated properties of eGenes with the correct eSNP identified (TP) or not (FN) (FDR<=0.05) and non-eGenes with a significant eQTL hit (FP). Properties are (left to right) simulated gene mean, gene variance (coefficient of variation), eQTL effect size (beta), and eSNP minor allele frequency (MAF). No simulated beta or eSNP MAF for FPs as they were not simulated as eGenes. Results from t-tests compared to TP are shown (ns: p > 0.05; $*$: p <= 0.05; $**$: p <= 0.01; $***$: p <= 0.001; $****$: p <= 0.0001)

Simulated properties of eGenes with the correct eSNP identified (TP) or not (FN) (FDR<=0.05) and non-eGenes with a significant eQTL hit (FP). Properties are (left to right) simulated gene mean, gene variance (coefficient of variation), eQTL effect size (beta), and eSNP minor allele frequency (MAF). No simulated beta or eSNP MAF for FPs as they were not simulated as eGenes. Results from t-tests compared to TP are shown (ns: p > 0.05; \(*\): p <= 0.05; \(**\): p <= 0.01; \(***\): p <= 0.001; \(****\): p <= 0.0001)

Version Author Date
bd0c8b0 cazodi 2021-05-17 
  #stat_compare_means(comparisons = my_comparisons) 

if(save){
  save.name <- paste0(date, "_ss2-eqtl-properties")
  ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 7, height = 3)
}

Estimated vs simulated eQTL effect sizes

rFull2 <- rFull %>% mutate(pred_EffectSize = ifelse(is.na(pred_EffectSize), 0, 
                                                    pred_EffectSize),
                 eQTL.EffectSize = -1 * eQTL.EffectSize)



rFull2 %>% ggscatter(x="eQTL.EffectSize", y="pred_EffectSize", color="result",
            palette = projectColors("eqtl.result"), alpha = 0.5, 
            xlab = "simulated beta", ylab = "estimated beta")

if(save){
  save.name <- paste0(date, "_ss2-eqtl-effect-sizes")
  ggsave(paste0("output/00_Figures/", save.name, ".pdf"), width = 4, height = 4)
}
tpCor <- rFull2 %>% filter(result == "TP") %>%
  dplyr::summarize(cor(pred_EffectSize, eQTL.EffectSize))

message("Correlation between estimated and simulated eQTL betas for TPs: ",
        round(tpCor$`cor(pred_EffectSize, eQTL.EffectSize)`, 3))
Correlation between estimated and simulated eQTL betas for TPs: 0.945

devtools::session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value                               
 version  R version 4.0.4 (2021-02-15)        
 os       Red Hat Enterprise Linux 8.4 (Ootpa)
 system   x86_64, linux-gnu                   
 ui       X11                                 
 language (EN)                                
 collate  en_AU.UTF-8                         
 ctype    en_AU.UTF-8                         
 tz       Australia/Melbourne                 
 date     2021-09-21                          

─ Packages ───────────────────────────────────────────────────────────────────
 package      * version date       lib source        
 abind          1.4-5   2016-07-21 [1] CRAN (R 4.0.2)
 assertthat     0.2.1   2019-03-21 [1] CRAN (R 4.0.2)
 backports      1.2.1   2020-12-09 [1] CRAN (R 4.0.4)
 broom          0.7.9   2021-07-27 [1] CRAN (R 4.0.4)
 bslib          0.2.5.1 2021-05-18 [1] CRAN (R 4.0.4)
 cachem         1.0.6   2021-08-19 [1] CRAN (R 4.0.4)
 callr          3.7.0   2021-04-20 [1] CRAN (R 4.0.4)
 car            3.0-11  2021-06-27 [1] CRAN (R 4.0.4)
 carData        3.0-4   2020-05-22 [1] CRAN (R 4.0.2)
 cellranger     1.1.0   2016-07-27 [1] CRAN (R 4.0.2)
 cli            3.0.1   2021-07-17 [1] CRAN (R 4.0.4)
 colorspace     2.0-2   2021-06-24 [1] CRAN (R 4.0.4)
 crayon         1.4.1   2021-02-08 [1] CRAN (R 4.0.4)
 curl           4.3.2   2021-06-23 [1] CRAN (R 4.0.4)
 data.table     1.14.0  2021-02-21 [1] CRAN (R 4.0.4)
 DBI            1.1.1   2021-01-15 [1] CRAN (R 4.0.4)
 dbplyr         2.1.1   2021-04-06 [1] CRAN (R 4.0.4)
 desc           1.3.0   2021-03-05 [1] CRAN (R 4.0.4)
 devtools       2.4.2   2021-06-07 [1] CRAN (R 4.0.4)
 digest         0.6.27  2020-10-24 [1] CRAN (R 4.0.2)
 dplyr        * 1.0.7   2021-06-18 [1] CRAN (R 4.0.4)
 ellipsis       0.3.2   2021-04-29 [1] CRAN (R 4.0.4)
 evaluate       0.14    2019-05-28 [1] CRAN (R 4.0.2)
 fansi          0.5.0   2021-05-25 [1] CRAN (R 4.0.4)
 farver         2.1.0   2021-02-28 [1] CRAN (R 4.0.4)
 fastmap        1.1.0   2021-01-25 [1] CRAN (R 4.0.3)
 forcats      * 0.5.1   2021-01-27 [1] CRAN (R 4.0.4)
 foreign        0.8-81  2020-12-22 [2] CRAN (R 4.0.4)
 fs             1.5.0   2020-07-31 [1] CRAN (R 4.0.2)
 generics       0.1.0   2020-10-31 [1] CRAN (R 4.0.2)
 ggplot2      * 3.3.5   2021-06-25 [1] CRAN (R 4.0.4)
 ggpubr       * 0.4.0   2020-06-27 [1] CRAN (R 4.0.3)
 ggsignif       0.6.2   2021-06-14 [1] CRAN (R 4.0.4)
 git2r          0.28.0  2021-01-10 [1] CRAN (R 4.0.4)
 glue           1.4.2   2020-08-27 [1] CRAN (R 4.0.2)
 gtable         0.3.0   2019-03-25 [1] CRAN (R 4.0.2)
 haven          2.4.3   2021-08-04 [1] CRAN (R 4.0.4)
 highr          0.9     2021-04-16 [1] CRAN (R 4.0.4)
 hms            1.1.0   2021-05-17 [1] CRAN (R 4.0.4)
 htmltools      0.5.2   2021-08-25 [1] CRAN (R 4.0.4)
 httpuv         1.6.2   2021-08-18 [1] CRAN (R 4.0.4)
 httr           1.4.2   2020-07-20 [1] CRAN (R 4.0.2)
 jquerylib      0.1.4   2021-04-26 [1] CRAN (R 4.0.4)
 jsonlite       1.7.2   2020-12-09 [1] CRAN (R 4.0.4)
 knitr          1.33    2021-04-24 [1] CRAN (R 4.0.4)
 labeling       0.4.2   2020-10-20 [1] CRAN (R 4.0.2)
 later          1.3.0   2021-08-18 [1] CRAN (R 4.0.4)
 lifecycle      1.0.0   2021-02-15 [1] CRAN (R 4.0.4)
 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)
 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)
 openxlsx       4.2.4   2021-06-16 [1] CRAN (R 4.0.4)
 pillar         1.6.2   2021-07-29 [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       3.5.2   2021-04-30 [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             2.5.1   2021-08-19 [1] CRAN (R 4.0.4)
 RColorBrewer * 1.1-2   2014-12-07 [1] CRAN (R 4.0.2)
 Rcpp           1.0.7   2021-07-07 [1] CRAN (R 4.0.4)
 readr        * 2.0.1   2021-08-10 [1] CRAN (R 4.0.4)
 readxl         1.3.1   2019-03-13 [1] CRAN (R 4.0.2)
 remotes        2.4.0   2021-06-02 [1] CRAN (R 4.0.4)
 reprex         2.0.1   2021-08-05 [1] CRAN (R 4.0.4)
 rio            0.5.27  2021-06-21 [1] CRAN (R 4.0.4)
 rlang          0.4.11  2021-04-30 [1] CRAN (R 4.0.4)
 rmarkdown      2.10    2021-08-06 [1] CRAN (R 4.0.4)
 rprojroot      2.0.2   2020-11-15 [1] CRAN (R 4.0.3)
 rstatix        0.7.0   2021-02-13 [1] CRAN (R 4.0.4)
 rstudioapi     0.13    2020-11-12 [1] CRAN (R 4.0.3)
 rvest          1.0.1   2021-07-26 [1] CRAN (R 4.0.4)
 sass           0.4.0   2021-05-12 [1] CRAN (R 4.0.4)
 scales         1.1.1   2020-05-11 [1] CRAN (R 4.0.2)
 sessioninfo    1.1.1   2018-11-05 [1] CRAN (R 4.0.2)
 stringi        1.7.3   2021-07-16 [1] CRAN (R 4.0.4)
 stringr      * 1.4.0   2019-02-10 [1] CRAN (R 4.0.2)
 testthat       3.0.4   2021-07-01 [1] CRAN (R 4.0.4)
 tibble       * 3.1.3   2021-07-23 [1] CRAN (R 4.0.4)
 tidyr        * 1.1.3   2021-03-03 [1] CRAN (R 4.0.4)
 tidyselect     1.1.1   2021-04-30 [1] CRAN (R 4.0.4)
 tidyverse    * 1.3.1   2021-04-15 [1] CRAN (R 4.0.4)
 tzdb           0.1.2   2021-07-20 [1] CRAN (R 4.0.4)
 usethis        2.0.1   2021-02-10 [1] CRAN (R 4.0.4)
 utf8           1.2.2   2021-07-24 [1] CRAN (R 4.0.4)
 vctrs          0.3.8   2021-04-29 [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           0.25    2021-08-06 [1] CRAN (R 4.0.4)
 xml2           1.3.2   2020-04-23 [1] CRAN (R 4.0.2)
 yaml           2.2.1   2020-02-01 [1] CRAN (R 4.0.2)
 zip            2.2.0   2021-05-31 [1] CRAN (R 4.0.4)

[1] /mnt/mcfiles/cazodi/R/x86_64-pc-linux-gnu-library/4.0
[2] /opt/R/4.0.4/lib/R/library