Kidney differential expression analysis

J. Taroni 2018

In this notebook, we work with microdissected glomeruli data from patients with nephrotic syndrome, patients with ANCA-associated vasculitis, and living donors.

First, we’ll apply the recount2 PLIER model and then we will test those LVs for differential expression between the three groups.

Functions and directory set up

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

# plot and result directory setup for this notebook
plot.dir <- file.path("plots", "20")
dir.create(plot.dir, recursive = TRUE, showWarnings = FALSE)
results.dir <- file.path("results", "20")
dir.create(results.dir, recursive = TRUE, showWarnings = FALSE)

Read in data

Expression data

ercb.file <- file.path("data", "expression_data", 
                       "ERCB_Glom_CustCDF19_forVCRC.txt")
exprs.df <- readr::read_tsv(ercb.file)

Parsed with column specification:
cols(
  .default = col_double(),
  EntrezGeneID = col_integer(),
  GeneSymbol = col_character()
)
See spec(...) for full column specifications.


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exprs.df <- dplyr::select(exprs.df, -EntrezGeneID)
colnames(exprs.df)[1] <- "Gene"

agg.exprs.df <- PrepExpressionDF(exprs.df)
# any genes that don't have a gene symbol need to be dropped
agg.exprs.df <- dplyr::filter(agg.exprs.df, !(is.na(Gene)))
readr::write_tsv(agg.exprs.df, path = file.path("data", "expression_data", 
                                                "ERCB_Glom_mean_agg.pcl"))
# as a matrix for use with PLIER
exprs.mat <- as.matrix(dplyr::select(agg.exprs.df, -Gene))
rownames(exprs.mat) <- agg.exprs.df$Gene

Clinical data

clinical.file <- file.path("data", "sample_info", "ERCB_glom_diagnosis.tsv")
diagnosis.df <- readr::read_tsv(clinical.file)

Parsed with column specification:
cols(
  Sample = col_character(),
  Diagnosis = col_character()
)

Apply recount2 model

# load model itself
recount.plier <- readRDS(file.path("data", "recount2_PLIER_data", 
                                   "recount_PLIER_model.RDS"))
# project
recount.b <- GetNewDataB(exprs.mat = as.matrix(exprs.mat),
                         plier.model = recount.plier)

Loading required package: PLIER
Loading required package: RColorBrewer
Loading required package: gplots

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess

Loading required package: pheatmap
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-13

Loading required package: knitr
Loading required package: rsvd
Loading required package: qvalue

# save B matrix to file
b.file <- file.path(results.dir, "ERCB_glom_recount2_B.RDS")
saveRDS(recount.b, file = b.file)

Differential expression

LVTestWrapper(b.matrix = recount.b,
              sample.info.df = diagnosis.df,
              phenotype.col = "Diagnosis",
              file.lead = "ERCB_glom_recount2_model",
              plot.dir = plot.dir,
              results.dir = results.dir)

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

$limma


$b.df

NA

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