In stemangiola/bioc2021_tidytranscriptomics: Introduction to Tidy Transcriptomics

Which sample has the largest no. of genes with zero counts?

airway %>% 
filter(counts == 0) %>% 
group_by(sample) %>% 
summarise(n=n()) %>% 
arrange(n) 

Which method detects the most differentially abundant transcripts, p value adjusted for multiple testing < 0.05 (FDR, adj.P.Val, padj)?

Option 1) Filter each method's output separately and check no. of rows

de_all %>%
  pivot_transcript() %>%
  filter(edgerQLT_FDR < 0.05) %>%
  nrow()

de_all %>%
  pivot_transcript() %>%
  filter(edgerLR_FDR < 0.05) %>%
  nrow()

de_all %>%
  pivot_transcript() %>%
  filter(voom_adj.P.Val < 0.05) %>%
  nrow()

de_all %>%
  pivot_transcript() %>%
  filter(deseq2_padj < 0.05) %>%
  nrow()  

Option 2) Use pivot_longer to reshape and filter all together

de_all %>%

  # Subset transcript information
  pivot_transcript() %>%

  # Reshape for nesting
  pivot_longer(
    cols = -c(feature, symbol, .abundant, group:exon_name),
    names_sep = "_",
    names_to = c("method", "statistic"),
    values_to = "value"
  ) %>%

  # Filter statistic
  filter(statistic %in% c("FDR", "adj.P.Val", "padj")) %>%  
  filter(value < 0.05) %>%

  # Counting
  count(method) %>%

  # Sort
  arrange(desc(n))

What is the most abundant cell type overall in BRCA samples?

BRCA_cell_type_long %>%
  group_by(cell_type) %>%
  summarise(m = median(proportion)) %>%
  arrange(desc(m))

Reducing dimension - UMAP

UMAP 1 of 2 components has more variability than 3 components

left_join(
    pbmc %>%
    runUMAP(ncomponents = 2, dimred="corrected") %>% 
        as_tibble() %>% 
        select(cell, UMAP1),
    pbmc %>%
        runUMAP(ncomponents = 3, dimred="corrected") %>% 
        as_tibble() %>% 
        select(cell, UMAP1),
    by="cell"
) %>%
    summarise(sd(UMAP1.x), sd(UMAP1.y))

stemangiola/bioc2021_tidytranscriptomics documentation built on Aug. 29, 2021, 11:35 p.m.