Fibrosis patient cohort by Ramnath et al.
Last updated: 2021-03-29
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Knit directory: liver-disease-atlas/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c5a4d0c | christianholland | 2021-03-29 | added pca of z-scores |
| html | 24c0c74 | christianholland | 2021-02-28 | Build site. |
| html | 5e36b25 | christianholland | 2021-02-28 | Build site. |
| Rmd | 7f331d0 | christianholland | 2021-02-28 | wflow_publish("analysis/*", delete_cache = TRUE, republish = TRUE) |
Introduction
Here we analysis a patient cohort covering patients suffering from HCV and NAFLD generated by Ramnath et al..
Libraries and sources
These libraries and sources are used for this analysis.
library(tidyverse)
library(tidylog)
library(here)
library(edgeR)
library(biobroom)
library(AachenColorPalette)
library(cowplot)
library(lemon)
library(patchwork)
options("tidylog.display" = list(print))
source(here("code/utils-rnaseq.R"))
source(here("code/utils-utils.R"))
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/human-ramnath-fibrosis"
output_path <- "output/human-ramnath-fibrosis"
# graphical parameters
# fontsize
fz <- 9Preliminary exploratory analysis
Library size
Barplot of the library size (total counts) for each of the samples.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
plot_libsize(count_matrix) +
my_theme(fsize = fz)
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Count distribution
Violin plots of the raw read counts for each of the samples.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
count_matrix %>%
tdy("gene", "sample", "count", meta) %>%
arrange(disease) %>%
ggplot(aes(
x = fct_reorder(sample, as.numeric(disease)), y = log10(count + 1),
group = sample, fill = disease
)) +
geom_violin() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
legend.position = "top"
) +
labs(x = NULL) +
my_theme(grid = "no", fsize = fz)
#> gather: reorganized (sample01, sample02, sample03, sample04, sample05, …) into (sample, count) [was 25369x68, now 1699723x3]
#> left_join: added 9 columns (age, gender, disease, stage, infect, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 1,699,723
#> > ===========
#> > rows total 1,699,723
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
PCA of raw data
PCA plot of raw read counts contextualized based on etiology stages. Before gene with a constant expression across all samples are removed and count values are transformed to log2 scale. Only the top 1000 most variable genes are used as features.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(count_matrix) == meta$sample)
# remove constant expressed genes and transform to log2 scale
preprocessed_count_matrix <- preprocess_count_matrix(count_matrix)
#> Discarding 1920 genes
#> Keeping 23449 genes
pca_result <- do_pca(preprocessed_count_matrix, meta, top_n_var_genes = 1000)
#> left_join: added 9 columns (age, gender, disease, stage, infect, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 67
#> > ====
#> > rows total 67
plot_pca(pca_result, feature = "disease") +
plot_pca(pca_result, feature = "stage") &
my_theme(fsize = fz)
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Data processing
Normalization
Raw read counts are normalized by first filtering out lowly expressed genes, TMM normalization and finally logCPM transformation.
count_matrix <- readRDS(here(data_path, "count_matrix.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(meta$sample == colnames(count_matrix))
dge_obj <- DGEList(count_matrix, group = meta$group)
# filter low read counts, TMM normalization and logCPM transformation
norm <- voom_normalization(dge_obj)
#> Discarding 8654 genes
#> Keeping 16715 genes
saveRDS(norm, here(output_path, "normalized_expression.rds"))PCA of normalized data
PCA plot of normalized expression data contextualized based on etiology stages. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 9 columns (age, gender, disease, stage, infect, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 67
#> > ====
#> > rows total 67
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "disease") +
plot_pca(pca_result, feature = "stage") &
my_theme(fsize = fz)
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the transcriptomic signatures of HCV and NAFLD.
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$sample)
# build design matrix
design <- model.matrix(~ 0 + group, data = meta)
rownames(design) <- meta$sample
colnames(design) <- levels(meta$group)
# define contrasts
contrasts <- makeContrasts(
hcv_adv_vs_early = hcv_advanced - hcv_early,
nafld_adv_vs_early = nafld_advanced - nafld_early,
levels = design
)
limma_result <- run_limma(expr, design, contrasts) %>%
assign_deg()
#> Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> select: renamed 3 variables (contrast, logFC, pval) and dropped one variable
#> group_by: one grouping variable (contrast)
#> mutate (grouped): new variable 'fdr' (double) with 13,118 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'regulation' (character) with 3 unique values and 0% NA
#> mutate: converted 'regulation' from character to factor (0 new NA)
deg_df <- limma_result %>%
mutate(contrast = factor(contrast)) %>%
mutate(contrast_reference = contrast)
#> mutate: no changes
#> mutate: new variable 'contrast_reference' (factor) with 2 unique values and 0% NA
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the transcriptomic signatures of HCV and NAFLD.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> rename: renamed one variable (p)
| Version | Author | Date |
|---|---|---|
| 3340593 | christianholland | 2021-02-28 |
z-scores
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(data_path, "meta_data.rds"))
diseases <- c("hcv", "nafld")
z_scores <- map_dfc(diseases, function(d) {
ctrl_samples <- meta %>%
filter(disease == d & stage == "early") %>%
pull(sample)
treated_samples <- meta %>%
filter(disease == d & stage == "advanced") %>%
pull(sample)
# compute mean and standard deviation of gene expression in control samples
ctrl_mean <- expr[, ctrl_samples] %>%
apply(1, mean)
ctrl_sd <- expr[, ctrl_samples] %>%
apply(1, sd)
# check whether genes are in correct order
stopifnot(names(ctrl_mean) == colnames(t(expr)))
stopifnot(names(ctrl_mean) == colnames(t(expr)))
# z-score transformation of gene expression w.r.t control samples
z_scores <- expr[, treated_samples] %>%
t() %>%
scale(center = ctrl_mean, scale = ctrl_sd) %>%
t() %>%
data.frame(check.names = FALSE)
})
#> filter: removed 35 rows (52%), 32 rows remaining
#> filter: removed 44 rows (66%), 23 rows remaining
#> filter: removed 59 rows (88%), 8 rows remaining
#> filter: removed 63 rows (94%), 4 rows remaining
saveRDS(z_scores, here(output_path, "z_scores.rds"))Time spend to execute this analysis: 00:23 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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 datasets utils methods base
#>
#> other attached packages:
#> [1] patchwork_1.1.1 lemon_0.4.5 cowplot_1.1.0
#> [4] AachenColorPalette_1.1.2 biobroom_1.20.0 broom_0.7.3
#> [7] edgeR_3.30.3 limma_3.44.3 here_1.0.1
#> [10] tidylog_1.0.2 forcats_0.5.0 stringr_1.4.0
#> [13] dplyr_1.0.2 purrr_0.3.4 readr_1.4.0
#> [16] tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2
#> [19] tidyverse_1.3.0 workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] Biobase_2.48.0 httr_1.4.2 jsonlite_1.7.2
#> [4] modelr_0.1.8 assertthat_0.2.1 renv_0.12.3
#> [7] cellranger_1.1.0 yaml_2.2.1 pillar_1.4.7
#> [10] backports_1.2.1 lattice_0.20-41 glue_1.4.2
#> [13] digest_0.6.27 promises_1.1.1 rvest_0.3.6
#> [16] colorspace_2.0-0 htmltools_0.5.0 httpuv_1.5.4
#> [19] plyr_1.8.6 clisymbols_1.2.0 pkgconfig_2.0.3
#> [22] haven_2.3.1 scales_1.1.1 whisker_0.4
#> [25] later_1.1.0.1 git2r_0.27.1 farver_2.0.3
#> [28] generics_0.1.0 ellipsis_0.3.1 withr_2.3.0
#> [31] BiocGenerics_0.34.0 cli_2.2.0 magrittr_2.0.1
#> [34] crayon_1.3.4 readxl_1.3.1 evaluate_0.14
#> [37] fs_1.5.0 fansi_0.4.1 xml2_1.3.2
#> [40] tools_4.0.2 hms_0.5.3 lifecycle_0.2.0
#> [43] munsell_0.5.0 reprex_0.3.0 locfit_1.5-9.4
#> [46] compiler_4.0.2 rlang_0.4.9 grid_4.0.2
#> [49] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.6
#> [52] codetools_0.2-18 gtable_0.3.0 DBI_1.1.0
#> [55] R6_2.5.0 gridExtra_2.3 lubridate_1.7.9.2
#> [58] knitr_1.30 rprojroot_2.0.2 stringi_1.5.3
#> [61] parallel_4.0.2 Rcpp_1.0.5 vctrs_0.3.6
#> [64] dbplyr_2.0.0 tidyselect_1.1.0 xfun_0.19