NACHO Analysis

A NAnostring quality Control dasHbOard

Mickaël Canouil, Ph.D., Gerard A. Bouland and Roderick C. Slieker, Ph.D.

January 12, 2024

1 Installation

# Install NACHO from CRAN:
install.packages("NACHO")

# Or the the development version from GitHub:
# install.packages("remotes")
remotes::install_github("mcanouil/NACHO")

2 Overview

NACHO (NAnostring quality Control dasHbOard) is developed for NanoString nCounter data.
NanoString nCounter data is a messenger-RNA/micro-RNA (mRNA/miRNA) expression assay and works with fluorescent barcodes.
Each barcode is assigned a mRNA/miRNA, which can be counted after bonding with its target.
As a result each count of a specific barcode represents the presence of its target mRNA/miRNA.

NACHO is able to load, visualise and normalise the exported NanoString nCounter data and facilitates the user in performing a quality control.
NACHO does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.

With the use of two functions, RCC files are summarised and visualised, namely: load_rcc() and visualise().

NACHO also includes a function normalise(), which (re)calculates sample specific size factors and normalises the data.

In addition (since v0.6.0) NACHO includes two (three) additional functions:

For more vignette("NACHO") and vignette("NACHO-analysis").

Canouil M, Bouland GA, Bonnefond A, Froguel P, Hart L, Slieker R (2019). “NACHO: an R package for quality control of NanoString nCounter data.” Bioinformatics. ISSN 1367-4803, doi:10.1093/bioinformatics/btz647.

@Article{,
  title = {{NACHO}: an {R} package for quality control of {NanoString} {nCounter} data},
  author = {Mickaël Canouil and Gerard A. Bouland and Amélie Bonnefond and Philippe Froguel and Leen Hart and Roderick Slieker},
  journal = {Bioinformatics},
  address = {Oxford, England},
  year = {2019},
  month = {aug},
  issn = {1367-4803},
  doi = {10.1093/bioinformatics/btz647},
}

3 Analyse NanoString data

3.1 Load packages

library(NACHO)
library(GEOquery, quietly = TRUE, warn.conflicts = FALSE)
## Error in library(GEOquery, quietly = TRUE, warn.conflicts = FALSE): there is no package called 'GEOquery'

3.2 Download GSE70970 from GEO (or use your own data)

data_directory <- file.path(tempdir(), "GSE70970", "Data")

# Download data
gse <- getGEO("GSE70970")
## Error in getGEO("GSE70970"): could not find function "getGEO"
getGEOSuppFiles(GEO = "GSE70970", baseDir = tempdir())
## Error in getGEOSuppFiles(GEO = "GSE70970", baseDir = tempdir()): could not find function "getGEOSuppFiles"
# Unzip data
untar(
  tarfile = file.path(tempdir(), "GSE70970", "GSE70970_RAW.tar"),
  exdir = data_directory
)
## Warning in untar(tarfile = file.path(tempdir(), "GSE70970",
## "GSE70970_RAW.tar"), : '/usr/bin/tar -xf
## '/var/folders/gn/mxv05rj52wd1yg1hb018s4s40000gn/T//RtmpQG4XyK/GSE70970/GSE70970_RAW.tar'
## -C
## '/var/folders/gn/mxv05rj52wd1yg1hb018s4s40000gn/T//RtmpQG4XyK/GSE70970/Data''
## returned error code 1
# Get phenotypes and add IDs
targets <- pData(phenoData(gse[[1]]))
## Error in pData(phenoData(gse[[1]])): could not find function "pData"
targets$IDFILE <- list.files(data_directory)
## Error: object 'targets' not found

3.3 Import RCC files

GSE70970 <- load_rcc(data_directory, targets, id_colname = "IDFILE")
## Error in eval(expr, envir, enclos): object 'targets' not found

3.4 Perform the analyses using limma

library(limma)
## Error in library(limma): there is no package called 'limma'

3.4.1 Get the phenotypes

selected_pheno <- GSE70970[["nacho"]][
  j = lapply(unique(.SD), function(x) ifelse(x == "NA", NA, x)),
  .SDcols = c("IDFILE", "age:ch1", "gender:ch1", "chemo:ch1", "disease.event:ch1")
]
## Error in eval(expr, envir, enclos): object 'GSE70970' not found
selected_pheno <- na.exclude(selected_pheno)
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found

3.4.2 Get the normalised counts

expr_counts <- GSE70970[["nacho"]][
  i = grepl("Endogenous", CodeClass),
  j = as.matrix(
    dcast(.SD, Name ~ IDFILE, value.var = "Count_Norm"),
    "Name"
  ),
  .SDcols = c("IDFILE", "Name", "Count_Norm")
]
## Error in eval(expr, envir, enclos): object 'GSE70970' not found
## Error in eval(expr, envir, enclos): object 'expr_counts' not found

Alternatively, "Accession" number is also available.

GSE70970[["nacho"]][
  i = grepl("Endogenous", CodeClass),
  j = as.matrix(
    dcast(.SD, Accession ~ IDFILE, value.var = "Count_Norm"),
    "Accession"
  ),
  .SDcols = c("IDFILE", "Accession", "Count_Norm")
]

3.4.3 Select phenotypes and counts

  1. Make sure count matrix and phenotypes have the same samples
samples_kept <- intersect(selected_pheno[["IDFILE"]], colnames(expr_counts))
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found
expr_counts <- expr_counts[, samples_kept]
## Error in eval(expr, envir, enclos): object 'expr_counts' not found
selected_pheno <- selected_pheno[IDFILE %in% c(samples_kept)]
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found
  1. Build the numeric design matrix
design <- model.matrix(~ `disease.event:ch1`, selected_pheno)
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found
  1. limma
eBayes(lmFit(expr_counts, design))
## Error in eBayes(lmFit(expr_counts, design)): could not find function "eBayes"

3.5 Perform the analyses using lm (or any other model)

GSE70970[["nacho"]][
  i = grepl("Endogenous", CodeClass),
  j = lapply(unique(.SD), function(x) ifelse(x == "NA", NA, x)),
  .SDcols = c(
    "IDFILE", "Name", "Accession", "Count", "Count_Norm",
    "age:ch1", "gender:ch1", "chemo:ch1", "disease.event:ch1"
  )
][
  Name %in% head(unique(Name), 10)
][
  j = as.data.table(
    coef(summary(lm(
      formula = Count_Norm ~ `disease.event:ch1`,
      data = na.exclude(.SD)
    ))),
    "term"
  ),
  by = c("Name", "Accession")
]
## Error in eval(expr, envir, enclos): object 'GSE70970' not found