Merge branch 'main' of github.com:bcbio/bcbioR

inst/rmarkdown/templates/rnaseq/skeleton/params_de.R

@@ -10,6 +10,7 @@ root = "../.."
 date = "YYYYMMDD"
 column = "sample_type"
 subset_column = NA
-metadata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv"
+coldata_fn = "https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/coldata.csv"
 counts_fn = 'https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/tximport-counts.csv'
+se_object=url("https://raw.githubusercontent.com/bcbio/bcbioR-test-data/main/rnaseq/bcbio/bcbio-se.rds")
 basedir <- 'results'

inst/rmarkdown/templates/rnaseq/skeleton/reports/DEG/DEG.Rmd

@@ -40,6 +40,7 @@ library(fgsea)
 library(org.Hs.eg.db)
 library(knitr)
 library(EnhancedVolcano)
+library(bcbioR)
 
 ggplot2::theme_set(theme_light(base_size = 14))
 opts_chunk[["set"]](
@@ -96,7 +97,7 @@ name_pathways_fn=file.path(root,
 ```
 
 ```{r read_counts_data}
-coldata=read.csv(metadata_fn)
+coldata=read.csv(coldata_fn)
 stopifnot(column %in% names(coldata))
 
 # use only some samples, by default use all
@@ -124,12 +125,31 @@ rdata = AnnotationDbi::select(org.Hs.eg.db, rownames(counts), 'SYMBOL', 'ENSEMBL
 
 When performing differential expression analysis, it is important to ensure that any detected differences are truly a result of the experimental comparison being made and not any additional variability in the data. 
 
-```{r before_RUV}
+
+
+
+```{r setup_RUV}
 
 dds_before <- DESeqDataSetFromMatrix(counts, coldata, design = ~1)
 
 vsd_before <- vst(dds_before)
 
+```
+
+```{r covariates, fig.height = 6, fig.width = 10}
+
+se <- readRDS(se_object) #local
+metrics <- metadata(se)$metrics %>% mutate(sample = toupper(sample)) %>% 
+  left_join(coldata %>% rownames_to_column('sample')) %>% column_to_rownames('sample')
+
+degCovariates(
+  assay(vsd_before),
+  metrics
+)
+```
+
+```{r before_RUV} 
+
 pca1 <- degPCA(assay(vsd_before), colData(vsd_before), 
        condition = column) + ggtitle('Before RUV')
 pca1 + scale_color_cb_friendly()
@@ -193,7 +213,8 @@ res <- as.data.frame(resLFCS) %>%
   relocate(gene_name) %>% dplyr::rename(lfc = log2FoldChange) %>%
   mutate(pi = abs(lfc) * -log10(padj)) %>% arrange(-pi)
 
-res_sig <- res %>% filter(padj < 0.05) %>% arrange(padj)
+res_sig <- res %>% filter(padj < 0.05) %>% arrange(padj) %>%
+  mutate(gene_name = ifelse(is.na(gene_name), gene_id, gene_name))
 
 res_mod <- res %>% mutate(lfc = replace(lfc, lfc < -5, -5)) %>% mutate(lfc = replace(lfc, lfc > 5, 5))
 show <- as.data.frame(res_mod[1:10, c("lfc", "padj", "gene_name")])
@@ -227,6 +248,22 @@ EnhancedVolcano(res_mod,
 res_sig %>% sanitize_datatable
 ```
 
+```{r top n DEGs, fig.height = 6, fig.width = 8}
+n = 16
+top_n <- res_sig %>% slice_min(order_by = padj, n = n, with_ties = F) %>% 
+  dplyr::select(gene_name, gene_id)
+top_n_exp <- assays(vsd_after) %>% as.data.frame() %>% 
+  rownames_to_column('gene_id') %>%
+  dplyr::select(-group, -group_name) %>% 
+  pivot_longer(!gene_id, names_to = 'sample', values_to = 'log2_expression') %>%
+  right_join(top_n) %>%
+  left_join(coldata, by = c('sample' = 'description'))
+
+ggplot(top_n_exp, aes_string(x = column, y = 'log2_expression')) + geom_boxplot() + 
+  facet_wrap(~gene_name) + ggtitle(str_interp('Expression of Top ${n} DEGs'))
+
+```
+
 # Pathway Enrichment
 
 From the set of differentially expressed genes and using publicly available information about gene sets involved in biological processes and functions, we can calculate which biological processes and functions are significantly perturbed as a result of the treatment.