metaanalysis_merge
Author
Brian M. Schilder2021-11-08
Contents
knitr::opts_chunk$set(echo = TRUE, root.dir = here::here())
# knitr::opts_knit$set(echo = T, root.dir = here::here())
library(dplyr)
library(ggplot2)
library(googledrive)
library(patchwork)
set.seed(2019)
#### Import utility functions ####
source(here::here("code/utils.R"))Merge all cell-type and tissue enrichment datasets for meta-analysis.
1 Merge cell-types
1.1 Download
You can download the meta-analysis tables from Google Drive and process them however you like.
# Table 1
googledrive::drive_download("https://docs.google.com/spreadsheets/d/18bKV3mUuyIv1yPN7gps9HPmhROb4jz0x7KxtA8mf0iM/edit#gid=743954199", path = here::here("data/metaanalysis/TableS1.xlsx"), overwrite = TRUE)
sheets <- readxl::excel_sheets(here::here("data/metaanalysis/TableS1.xlsx")) 1.2 Automated merging
Alternatively, you can use the function merge_data we’ve provided in
code/utils.R to automatically download, merge and harmonise the data.
celltype_data <- merge_data(googledrive_url = "https://docs.google.com/spreadsheets/d/18bKV3mUuyIv1yPN7gps9HPmhROb4jz0x7KxtA8mf0iM/edit#gid=743954199",
file_name = "TableS1.xlsx",
sheet_search = "celltypes")## ! Using an auto-discovered, cached token.## To suppress this message, modify your code or options to clearly consent to
## the use of a cached token.## See gargle's "Non-interactive auth" vignette for more details:## <https://gargle.r-lib.org/articles/non-interactive-auth.html>## ℹ The googledrive package is using a cached token for
## 'brian_schilder@alumni.brown.edu'.## File downloaded:## • 'TableS1' <id: 18bKV3mUuyIv1yPN7gps9HPmhROb4jz0x7KxtA8mf0iM>## Saved locally as:## • '/Users/schilder/Desktop/Mount_Sinai/PD_omics_review/data/metaanalysis/TableS1.xlsx'## [1] "Bryois2020_celltypes"## Warning: Missing target_cols:
## - Count## [1] "Reynolds2019_celltypes"## Warning: Missing target_cols:
## - Count## [1] "Corces2020_celltypes"## Warning: Missing target_cols:
## - Count
## - Dataset## [1] "Schilder2020_celltypes"
## [1] "Li2021_celltypes"## Warning: Missing target_cols:
## - Count## [1] "Nalls2019_celltypes"## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Coercing text to numeric in M2197 / R2197C13: ''## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
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## Coercing text to numeric in M5630 / R5630C13: ''## New names:
## * `` -> ...17## Warning: Missing target_cols:
## - Count## [1] "Agarwal2020_celltypes"## Warning: Missing target_cols:
## - Count## [1] "Nott2019_celltypes"## Warning: Missing target_cols:
## - Count## [1] "Coetzee2016_celltypes"## Warning: Missing target_cols:
## - Count
## - Dataset## [1] "Li2019_celltypes"## Warning: Missing target_cols:
## - Count## Harmonising labels:
## - Cell_Type## Dropping dataset-specific columns.## 8,116 rows merged.## Saving merged data: ==> /Users/schilder/Desktop/Mount_Sinai/PD_omics_review/data/metaanalysis/merged_celltypes.csv1.3 Preprocess
1.3.1 FDR plots
plot_celltypes <- celltype_data %>%
subset(!is.na(Sig) & !is.na(Cell_Type) & !( Cell_Type %in% c("brain"))) %>%
dplyr::mutate( Sig_neglog = -log1p(Sig)) %>%
dplyr::group_by(Reference, .drop = F) %>%
dplyr::mutate(Cell_Type= paste0(Cell_Type,"@",group_indices())) %>%
## Calculate zscore for fine-mapping overlap
dplyr::summarise(Cell_Type,Source,Sheet,Dataset,Sig,Count,
FDR = p.adjust(p = Sig, method = "fdr"),
bonf = p.adjust(p = Sig, method = "bonf"),
n_tests = n()) %>%
dplyr::mutate(Score = ifelse(Reference=="Schilder et al, 2020 (bioRxiv)",
as.numeric(cut(Count, 10))/10,
scales::rescale(-log10(FDR), c(0.0000000001,1))) ) %>%
dplyr::ungroup() %>%
dplyr::mutate(FDR_all = p.adjust(p = Sig, method = "fdr")) %>%
subset(FDR_all<.05 & FDR<.05 & Sig < 0.05 & (!is.infinite(Score))) ## Warning: `group_indices()` was deprecated in dplyr 1.0.0.
## Please use `cur_group_id()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.## `summarise()` has grouped output by 'Reference'. You can override using the `.groups` argument.# Sort by medians
plot_celltypes <- plot_celltypes %>%
dplyr::group_by(Reference, Cell_Type) %>%
dplyr::summarise(mean_Score = mean(Score, na.rm=T),
median_Score = median(Score, na.rm=T),
.groups = "keep") %>%
merge(plot_celltypes, by=c("Reference","Cell_Type")) %>%
dplyr::arrange(Reference, desc(median_Score), bonf)
plot_celltypes$Cell_Type <- factor(plot_celltypes$Cell_Type,
levels = rev(unique(plot_celltypes$Cell_Type)), ordered = T)
gg_FDR1 <- gg_FDR2 <- plot_fdr(dat = plot_celltypes, y="FDR", label = "Cell_Type")
plotly::ggplotly(gg_FDR1)gg_FDR2 <- plot_fdr(dat = plot_celltypes, label = "Cell_Type")
plotly::ggplotly(gg_FDR2)1.3.2 Cell-types plot data
createDT(plot_celltypes)1.3.3 Cell-types counts
celltype_counts <- plot_celltypes %>% dplyr::group_by(Reference) %>%
dplyr::summarise(n_celltypes=dplyr::n_distinct(Cell_Type)) %>%
dplyr::arrange(desc(n_celltypes))
knitr::kable(celltype_counts)| Reference | n_celltypes |
|---|---|
| Nalls et al, 2019 (Lancet Neurology) | 117 |
| Schilder et al, 2020 (bioRxiv) | 8 |
| Coetzee et al, 2016 (Scientific Reports) | 7 |
| Reynolds et al. 2019 (npj Parkinson’s Disease) | 4 |
| Bryois et al, 2020 (Nature Genetics) | 3 |
| Li et al, 2019 (Nature Communications) | 3 |
| Nott et al, 2019 (Science) | 3 |
| Agarwal et al, 2020 (Nature Communications) | 2 |
1.4 Summary plot
Summarise the top celltypes/tissues per study according to abstracted “Score” value (computed here).
### Limit the number of celltypes/study for visualization purposes
max_celltypes <- 25
celltype_plot <- ggplot(plot_celltypes %>%
dplyr::group_by(Reference) %>%
dplyr::slice_max(order_by = median_Score,
n = max_celltypes),
aes(x=Score, y=Cell_Type, fill=Reference)) +
# geom_bar(show.legend = F, stat="identity") +
geom_boxplot(show.legend = FALSE) +
# geom_violin(show.legend = F) +
geom_point(show.legend = FALSE, alpha=.2) +
# Create a dicionary mapping the new labels
scale_y_discrete(labels = setNames(gsub("@.*","",levels(plot_celltypes$Cell_Type )),
levels(plot_celltypes$Cell_Type) ) ) +
# facet_wrap(facets = .~paste(gsub("[(]","\n(",Reference),"\n\n n tests =",n_tests),
# scales = "free", drop = F, nrow = 1) +
facet_grid(facets = paste0(gsub("[(]",
"\n(",Sheet),
"\n (tests = ",
formatC(n_tests,big.mark = ","),")")~.,
scales = "free", space="free", drop = F) +
theme_bw() +
theme(strip.background = element_rect(fill = "black"),
strip.text = element_text(color = "white", angle = 0),
strip.text.y = element_text(color = "white", angle = 0),
panel.grid.minor.x = element_blank())
print(celltype_plot)
# ggsave(here::here("plots/metaanalysis/celltype_metaanalysis.pdf"),celltype_plot, dpi = 300, height = 15)2 Merge tissues
2.1 Automated merging
tissue_data <- merge_data(googledrive_url = "https://docs.google.com/spreadsheets/d/18bKV3mUuyIv1yPN7gps9HPmhROb4jz0x7KxtA8mf0iM/edit#gid=743954199",
file_name = "TableS1.xlsx",
sheet_search = "tissues")## File downloaded:## • 'TableS1' <id: 18bKV3mUuyIv1yPN7gps9HPmhROb4jz0x7KxtA8mf0iM>## Saved locally as:## • '/Users/schilder/Desktop/Mount_Sinai/PD_omics_review/data/metaanalysis/TableS1.xlsx'## [1] "Bryois2020_tissues"## Warning: Missing target_cols:
## - PP.H4
## - Dataset## [1] "Reynolds2019_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Corces2020_tissues"## Warning: Missing target_cols:
## - PP.H4
## - Dataset## [1] "Schilder2020_tissues"
## [1] "Nalls2019_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Agarwal2020_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Nott2019_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Coetzee2016_tissues"## Warning: Missing target_cols:
## - PP.H4
## - Dataset## [1] "Li2019_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Yao2021_tissues"## Warning: Missing target_cols:
## - PP.H4## [1] "Kia2021_tissues"## Warning: Missing target_cols:
## - PP.H4## Harmonising labels:
## - Tissue## Dropping dataset-specific columns.## 20,408 rows merged.## Saving merged data: ==> /Users/schilder/Desktop/Mount_Sinai/PD_omics_review/data/metaanalysis/merged_tissues.csv2.2 Preprocess
2.2.1 FDR plots
plot_tissues <- tissue_data %>%
subset(!(Tissue %in% c(NA,"NA")) & (!is.na(Sig))) %>%
dplyr::mutate(Sig_neglog = -log1p(Sig)) %>%
## Calculate zscore for fine-mapping overlap
dplyr::group_by(Reference, .drop = FALSE) %>%
dplyr::mutate(Tissue= paste0(Tissue,"@",group_indices())) %>%
dplyr::summarise(Tissue,Source,Sheet,Dataset,Sig,PP.H4,
FDR = p.adjust(p = Sig, method = "fdr"),
bonf = p.adjust(p = Sig, method = "bonf"),
n_tests = n(),
.groups = "keep") %>%
# dplyr::ungroup() %>%
dplyr::mutate(Score = ifelse(Reference=="Schilder et al, 2020 (bioRxiv)",
as.numeric(cut(PP.H4, 10))/10,
scales::rescale(-log(FDR), c(0.0000000001,1))) ) %>%
dplyr::ungroup() %>%
dplyr::mutate(FDR_all = p.adjust(p = Sig, method = "fdr")) %>%
subset((FDR_all<.05 & FDR<.05 & Sig < 0.05) | (PP.H4>.8))
# Sort by medians
plot_tissues <- plot_tissues %>%
dplyr::group_by(Reference, Tissue, .drop = F) %>%
dplyr::summarise(mean_Score = mean(Score, na.rm=T),
median_Score = median(Score, na.rm=T),
n_tissues = n_distinct(Tissue),
.groups = "keep") %>%
merge(plot_tissues, by=c("Reference","Tissue"), all.y = T) %>%
dplyr::arrange(Reference, desc(median_Score), bonf)
plot_tissues$Tissue <- factor(plot_tissues$Tissue,
levels = rev(unique(plot_tissues$Tissue)),
ordered = TRUE)
gg_FDR1 <- plot_fdr(dat = plot_tissues, y="FDR", label = "Tissue")
plotly::ggplotly(gg_FDR1)gg_FDR2 <- plot_fdr(dat = plot_tissues, label = "Tissue")
plotly::ggplotly(gg_FDR2) 2.2.2 Tissues plot data
createDT(plot_tissues)2.2.3 Tissues counts
tissue_counts <- plot_tissues %>% dplyr::group_by(Reference) %>%
dplyr::summarise(n_tissues=dplyr::n_distinct(Tissue)) %>%
dplyr::arrange(desc(n_tissues))
createDT(tissue_counts)2.3 Summary plot
# plot_tissues[is.na(plot_tissues$Dataset),"Dataset"] <- plot_tissues[is.na(plot_tissues$Dataset),"Sheet"]
max_tissues <- 15
tissue_plot <- ggplot(plot_tissues %>%
dplyr::group_by(Reference) %>%
dplyr::slice_max(order_by = median_Score,
n = max_tissues),
aes(x=Score, y=Tissue, fill=Reference)) +
# geom_bar(show.legend = F, stat="identity") +
geom_boxplot(show.legend = FALSE) +
# geom_violin(show.legend = F) +
geom_point(show.legend = FALSE, alpha=.2) +
# facet_wrap(facets = .~paste(gsub("[(]","\n(",Reference),"\n\n n tests =",n_tests),
# scales = "free", drop = F, nrow = 1) +
facet_grid(facets = paste0(gsub("[(]","\n(",Sheet),
"\n (tests = ",
formatC(n_tests,big.mark = ","),")")~.,
scales = "free", space="free", drop = FALSE) +
scale_x_continuous(limits = c(0,1)) +
# Create a dicionary mapping the new labels
scale_y_discrete(labels = setNames(gsub("@.*","",levels(plot_tissues$Tissue)),
levels(plot_tissues$Tissue) ) ) +
theme_bw() +
theme(strip.background = element_rect(fill = "black"),
strip.text = element_text(color = "white", angle = 0),
strip.text.y = element_text(color = "white", angle = 0),
panel.grid.minor.x = element_blank())
print(tissue_plot)
# ggsave("plots/metaanalysis/tissue_metaanalysis.pdf",tissue_plot, dpi = 300, height = 15)3 Fuse plots
fused_plot <- (celltype_plot + labs(title="Cell-types") +
tissue_plot + labs(title="Tissues")) +
patchwork::plot_annotation(tag_levels = letters)
print(fused_plot)## Warning: Removed 32 rows containing non-finite values (stat_boxplot).
ggsave(here::here("plots/metaanalysis/metaanalysis.pdf"),fused_plot, height = 14, width = 15, dpi = 400)## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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utils::sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.1 googledrive_2.0.0 ggplot2_3.3.5 dplyr_1.0.7
## [5] BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 here_1.0.1 tidyr_1.1.4
## [4] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.28
## [7] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
## [10] evaluate_0.14 highr_0.9 httr_1.4.2
## [13] pillar_1.6.4 rlang_0.4.12 lazyeval_0.2.2
## [16] curl_4.3.2 readxl_1.3.1 rstudioapi_0.13
## [19] data.table_1.14.2 jquerylib_0.1.4 magick_2.7.3
## [22] DT_0.19 rmarkdown_2.11 textshaping_0.3.6
## [25] labeling_0.4.2 stringr_1.4.0 htmlwidgets_1.5.4
## [28] munsell_0.5.0 compiler_4.1.0 xfun_0.28
## [31] systemfonts_1.0.3 pkgconfig_2.0.3 askpass_1.1
## [34] htmltools_0.5.2 openssl_1.4.5 tidyselect_1.1.1
## [37] tibble_3.1.6 bookdown_0.24 fansi_0.5.0
## [40] viridisLite_0.4.0 crayon_1.4.2 withr_2.4.2
## [43] rappdirs_0.3.3 grid_4.1.0 jsonlite_1.7.2
## [46] gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.1
## [49] magrittr_2.0.1 scales_1.1.1 cli_3.1.0
## [52] stringi_1.7.5 farver_2.1.0 fs_1.5.0
## [55] bslib_0.3.1 ragg_1.2.0 ellipsis_0.3.2
## [58] generics_0.1.1 vctrs_0.3.8 tools_4.1.0
## [61] glue_1.5.0 purrr_0.3.4 crosstalk_1.2.0
## [64] fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-2
## [67] gargle_1.2.0 BiocManager_1.30.16 plotly_4.10.0
## [70] knitr_1.36 sass_0.4.0