Overview
In this workshop, we explore some options to customize ggplot. This document contains the basic plot to start and then addtional plots with modifications in response to attendee questions.
Prior to the workshop
Please install R, RStudio, and the following packages.
#Data manipulation
#install.packages("tidyverse")
library(tidyverse)
#Example data
#install.packages("devtools")
#devtools::install_github("BIGslu/kimma")
library(kimma)Load data
Briefly, these data are from RNA-sequencing of human dendritic cells cultured with and without virus. Samples are from 3 donors and a random subset of 1000 genes were selected. Expression data are in an limma EList object (named example.voom) containing expression (E), sample/patient metadata (targets), and gene metadata (genes). Expression is expressed as TMM-normalized log2 counts per million (CPM).
We combine the expression, sample, and gene metadata in a single table for use in plotting.
dat <- as.data.frame(example.voom$E) %>%
rownames_to_column("geneName") %>%
pivot_longer(-geneName, names_to = "libID") %>%
inner_join(example.voom$targets, by = "libID") %>%
inner_join(example.voom$genes, by = "geneName")
dat## # A tibble: 12,000 × 15
## geneName libID value group lib.size norm.factors donorID median_cv_cover…
## <chr> <chr> <dbl> <fct> <dbl> <dbl> <chr> <dbl>
## 1 ENSG0000000… lib1 6.11 1 79646. 1.00 donor1 0.514
## 2 ENSG0000000… lib2 8.00 1 88008. 0.951 donor1 0.435
## 3 ENSG0000000… lib3 7.32 1 178020. 1.09 donor2 0.374
## 4 ENSG0000000… lib4 7.33 1 133836. 0.943 donor2 0.388
## 5 ENSG0000000… lib5 7.92 1 192547. 1.00 donor3 0.353
## 6 ENSG0000000… lib6 7.99 1 175144. 0.974 donor3 0.349
## 7 ENSG0000000… lib7 8.29 1 205377. 1.02 donor4 0.339
## 8 ENSG0000000… lib8 8.05 1 149311. 0.995 donor4 0.350
## 9 ENSG0000000… lib9 7.13 1 182080. 1.04 donor5 0.342
## 10 ENSG0000000… lib10 8.58 1 181755. 0.980 donor5 0.330
## # … with 11,990 more rows, and 7 more variables: virus <fct>, asthma <chr>,
## # batch <dbl>, hgnc_symbol <chr>, Previous symbols <chr>,
## # Alias symbols <chr>, gene_biotype <chr>Additionally, we load table with GSEA results for these data. See the example_gsea.R script for the code to run GSEA.
gsea <- read_csv("data/example_gsea.csv", show_col_types = FALSE)
class(gsea)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"gsea## # A tibble: 48 × 8
## pathway pval padj ES NES nMoreExtreme size leadingEdge
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 HALLMARK_ADIPOGENE… 0.0933 0.320 -0.555 -1.38 49 15 NA
## 2 HALLMARK_ALLOGRAFT… 0.413 0.763 0.412 1.04 190 14 NA
## 3 HALLMARK_ANDROGEN_… 0.0807 0.298 0.789 1.41 37 4 NA
## 4 HALLMARK_ANGIOGENE… 0.137 0.411 0.885 1.31 64 2 NA
## 5 HALLMARK_APICAL_JU… 0.124 0.396 -0.632 -1.38 67 9 NA
## 6 HALLMARK_APICAL_SU… 0.260 0.568 -0.867 -1.16 132 1 NA
## 7 HALLMARK_APOPTOSIS 0.0521 0.243 0.750 1.48 23 6 NA
## 8 HALLMARK_BILE_ACID… 0.255 0.568 -0.544 -1.19 139 9 NA
## 9 HALLMARK_CHOLESTER… 0.470 0.806 -0.634 -1.04 243 3 NA
## 10 HALLMARK_COAGULATI… 0.715 0.895 -0.397 -0.810 387 7 NA
## # … with 38 more rowsPlots
Boxplot
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = virus, y = value)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter()
Only show some points
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = virus, y = value)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(data=filter(dat,
hgnc_symbol == "IFIT3" & donorID=="donor1")) +
#theme_classic() +
theme_bw()
See ggthemes for more
Save to file
plot1 <- dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = virus, y = value)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter()
ggsave(filename = "plot1.png", plot1,
width=3, height=3)Width of boxplot relative to sample size.
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
arrange(virus) %>%
slice_head(n=9) %>%
ggplot(aes(x = virus, y = value)) +
geom_boxplot(varwidth = TRUE,
outlier.shape = NA) +
geom_jitter()
Violin version with scale to sample size
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
arrange(virus) %>%
slice_head(n=9) %>%
ggplot(aes(x = virus, y = value)) +
geom_violin(scale = "count")
Barplot
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = donorID, y = value)) +
geom_bar(aes(fill = virus), stat = "identity",
position = 'dodge')
Stacked bars
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = donorID, y = value)) +
geom_bar(aes(fill = virus), stat = "identity")
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = donorID, y = value)) +
geom_bar(aes(fill = virus), stat = "identity",
position = 'dodge') +
scale_fill_manual(values = c('HRV'='black',
'none'='grey60'))
Color ideas at https://colorbrewer2.org/
Plot mean with error
dat %>%
filter(hgnc_symbol == "IFIT3") %>%
ggplot(aes(x = virus, y = value)) +
geom_bar(aes(fill = virus),
stat = "summary", fun = "mean") +
stat_summary(fun.data="mean_sdl")
“Lollipop” plot
gsea %>%
filter(padj < 0.3) %>%
mutate(Significance = ifelse(padj < 0.2, "FDR < 0.2", "NS")) %>%
ggplot() +
geom_segment(aes(x=pathway, xend=pathway,
y=0, yend=NES)) +
geom_point(aes(x=pathway, y=NES,
color = Significance)) +
geom_hline(yintercept = 0) +
coord_flip()
Sort
gsea %>%
filter(padj < 0.3) %>%
mutate(Significance = ifelse(padj < 0.2, "FDR < 0.2", "NS")) %>%
ggplot() +
geom_segment(aes(y=pathway, yend=pathway,
x=0, xend=NES)) +
geom_point(aes(y=pathway, x=NES,
color = Significance)) +
geom_vline(xintercept = 0)
Reorder by NES
gsea %>%
filter(padj < 0.3) %>%
mutate(Significance = ifelse(padj < 0.2, "FDR < 0.2", "NS")) %>%
ggplot() +
geom_segment(aes(y=reorder(pathway,NES), yend=reorder(pathway,NES),
x=0, xend=NES)) +
geom_point(aes(y=reorder(pathway,NES), x=NES,
color = Significance)) +
geom_vline(xintercept = 0)
gsea %>%
filter(padj < 0.3) %>%
mutate(Significance = ifelse(padj < 0.2, "FDR < 0.2", "NS")) %>%
ggplot() +
geom_segment(aes(y=reorder(pathway,NES),
yend=reorder(pathway,NES),
x=0, xend=NES), size=1) +
geom_point(aes(y=reorder(pathway,NES), x=NES,
color = Significance), size=2) +
geom_vline(xintercept = 0, lty="dashed") +
theme_bw() +
theme(legend.position = c(0.1,0.9),
legend.direction = "vertical",
plot.title = element_text(hjust = 0.5),
legend.justification = "left") +
labs(x="Normalized enrichment score",
y="", title="Hallmark pathways") +
lims(x=c(-2.1,2.1))
R session
sessionInfo()## R version 4.1.1 (2021-08-10)
## 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.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] limma_3.48.3 kimma_1.0.0 forcats_0.5.1 stringr_1.4.0
## [5] dplyr_1.0.7 purrr_0.3.4 readr_2.1.0 tidyr_1.1.4
## [9] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.0 lubridate_1.8.0 bit64_4.0.5
## [4] RColorBrewer_1.1-2 httr_1.4.2 tools_4.1.1
## [7] backports_1.3.0 bslib_0.3.1 utf8_1.2.2
## [10] R6_2.5.1 rpart_4.1-15 Hmisc_4.6-0
## [13] DBI_1.1.1 colorspace_2.0-2 nnet_7.3-16
## [16] withr_2.4.2 gridExtra_2.3 tidyselect_1.1.1
## [19] bit_4.0.4 compiler_4.1.1 textshaping_0.3.6
## [22] cli_3.1.0 rvest_1.0.2 htmlTable_2.3.0
## [25] xml2_1.3.2 labeling_0.4.2 sass_0.4.0
## [28] checkmate_2.0.0 scales_1.1.1 systemfonts_1.0.2
## [31] digest_0.6.28 foreign_0.8-81 rmarkdown_2.11
## [34] base64enc_0.1-3 jpeg_0.1-9 pkgconfig_2.0.3
## [37] htmltools_0.5.2 dbplyr_2.1.1 fastmap_1.1.0
## [40] highr_0.9 htmlwidgets_1.5.4 rlang_0.4.12
## [43] readxl_1.3.1 rstudioapi_0.13 jquerylib_0.1.4
## [46] farver_2.1.0 generics_0.1.1 jsonlite_1.7.2
## [49] vroom_1.5.6 magrittr_2.0.1 Formula_1.2-4
## [52] Matrix_1.3-4 Rcpp_1.0.7 munsell_0.5.0
## [55] fansi_0.5.0 lifecycle_1.0.1 stringi_1.7.5
## [58] yaml_2.2.1 grid_4.1.1 parallel_4.1.1
## [61] crayon_1.4.2 lattice_0.20-45 haven_2.4.3
## [64] splines_4.1.1 hms_1.1.1 knitr_1.36
## [67] pillar_1.6.4 codetools_0.2-18 reprex_2.0.1
## [70] glue_1.5.0 evaluate_0.14 latticeExtra_0.6-29
## [73] data.table_1.14.2 modelr_0.1.8 vctrs_0.3.8
## [76] png_0.1-7 tzdb_0.2.0 foreach_1.5.1
## [79] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
## [82] xfun_0.28 broom_0.7.10 ragg_1.1.3
## [85] survival_3.2-13 iterators_1.0.13 cluster_2.1.2
## [88] ellipsis_0.3.2