DIPG: Analysis

J. Taroni 2018

Analyze the DIPG B matrices from the MultiPLIER model

Set up

`%>%` <- dplyr::`%>%`
source(file.path("util", "test_LV_differences.R"))

Directories for this notebook

# plot and result directory setup for this notebook
plot.dir <- file.path("plots", "36")
dir.create(plot.dir, recursive = TRUE, showWarnings = FALSE)
results.dir <- file.path("results", "36")
dir.create(results.dir, recursive = TRUE, showWarnings = FALSE)

Read in files

GSE50021

gse50021.b.file <- file.path("results", "35", "GSE50021_recount2_B.RDS")
gse50021.b <- readRDS(gse50021.b.file)
gse50021.meta.file <- file.path("data", "sample_info", 
                                "GSE50021_cleaned_metadata.tsv")
gse50021.meta.df <- readr::read_tsv(gse50021.meta.file)

Parsed with column specification:
cols(
  sample_accession = col_character(),
  source_name = col_character(),
  tissue = col_character(),
  gender = col_character(),
  age_at_diagnosis_yrs = col_double(),
  overall_survival_yrs = col_double()
)

GSE26576

gse26576.b.file <- file.path("results", "35", "E-GEOD-26576_recount2_B.RDS")
gse26576.b <- readRDS(gse26576.b.file)
gse26576.meta.file <- file.path("data", "sample_info", 
                                "E-GEOD-26576_cleaned_metadata.tsv")
gse26576.meta.df <- readr::read_tsv(gse26576.meta.file)

Parsed with column specification:
cols(
  sample_id = col_character(),
  sample_file = col_character(),
  sample_title = col_character(),
  age_at_diagnosis = col_double(),
  disease_state = col_character(),
  histology = col_character(),
  sample_collection = col_character()
)

Metadata

We should explore the metadata a little bit, because these datasets are both quite small. So, we’ll need to sort out what kinds of analyses are possible.

gse50021.meta.df %>%
  dplyr::group_by(tissue) %>%
  dplyr::tally()

gse26576.meta.df %>%
  dplyr::group_by(disease_state) %>%
  dplyr::tally()

GSE26576 does not have enough normal samples for a DIPG-normal comparison and the other groups in GSE26576 are not present in GSE50021.

We can do a comparison of DIPG-normal in GSE50021.

Differential expression in GSE50021

gse50021.results <- 
  LVTestWrapper(b.matrix = gse50021.b,
                sample.info.df = dplyr::mutate(gse50021.meta.df,
                                               Sample = sample_accession),
                phenotype.col = "tissue",
                file.lead = "GSE50021_normal_dipg",
                plot.dir = plot.dir,
                results.dir = results.dir)

Column `Sample` joining factor and character vector, coercing into character vector

gse50021.results$limma %>%
  dplyr::filter(adj.P.Val < 0.05)

After revisiting Buczkowicz, et al. and the GSE50021 accession, there is no information about which part of the brain the normal brain came from or how it was obtained. So, I am a bit concerned about drawing any conclusions from this analysis, as the differences we see may not be attributable to disease state.