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()
.
load_rcc()
function is used to preprocess the
data.visualise()
function initiates a Shiny-based dashboard that visualises
all relevant QC plots.NACHO also includes a function normalise()
,
which (re)calculates sample specific size factors and normalises the
data.
normalise()
function creates a list in which your
settings, the raw counts and normalised counts are stored.In addition (since v0.6.0) NACHO includes two (three) additional functions:
render()
function renders a full quality-control
report (HTML) based on the results of a call to load_rcc()
or normalise()
(using print()
in a Rmarkdown
chunk).autoplot()
function draws any quality-control
metrics from visualise()
and render()
.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},
}
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
limma
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
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.
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
design <- model.matrix(~ `disease.event:ch1`, selected_pheno)
## Error in eval(expr, envir, enclos): object 'selected_pheno' not found
limma
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