load_rcc | R Documentation |
This function is used to preprocess the data from NanoString nCounter.
load_rcc(
data_directory,
ssheet_csv,
id_colname = NULL,
housekeeping_genes = NULL,
housekeeping_predict = FALSE,
housekeeping_norm = TRUE,
normalisation_method = "GEO",
n_comp = 10
)
data_directory |
[character] A character string of the directory where the data are stored. |
ssheet_csv |
[character] or [data.frame] Either a string with the name of the CSV
of the samplesheet or the samplesheet as a |
id_colname |
[character] Character string of the column in |
housekeeping_genes |
[character] A vector of names of the miRNAs/mRNAs
that should be used as housekeeping genes. Default is |
housekeeping_predict |
[logical] Boolean to indicate whether the housekeeping genes
should be predicted ( |
housekeeping_norm |
[logical] Boolean to indicate whether the housekeeping normalisation
should be performed. Default is |
normalisation_method |
[character] Either |
n_comp |
[numeric] Number indicating the number of principal components to compute.
Cannot be more than n-1 samples. Default is |
[list] A list object of class "nacho"
:
access
[character] Value passed to load_rcc()
in id_colname
.
housekeeping_genes
[character] Value passed to load_rcc()
.
housekeeping_predict
[logical] Value passed to load_rcc()
.
housekeeping_norm
[logical] Value passed to load_rcc()
.
normalisation_method
[character] Value passed to load_rcc()
.
remove_outliers
[logical] FALSE
.
n_comp
[numeric] Value passed to load_rcc()
.
data_directory
[character] Value passed to load_rcc()
.
pc_sum
[data.frame] A data.frame
with n_comp
rows and four columns:
"Standard deviation", "Proportion of Variance", "Cumulative Proportion" and "PC".
nacho
[data.frame] A data.frame
with all columns from the sample sheet ssheet_csv
and all computed columns, i.e., quality-control metrics and counts, with one sample per row.
outliers_thresholds
[list] A list
of the (default) quality-control thresholds used.
if (interactive()) {
library(GEOquery)
library(NACHO)
# Import data from GEO
gse <- GEOquery::getGEO(GEO = "GSE74821")
targets <- Biobase::pData(Biobase::phenoData(gse[[1]]))
GEOquery::getGEOSuppFiles(GEO = "GSE74821", baseDir = tempdir())
utils::untar(
tarfile = file.path(tempdir(), "GSE74821", "GSE74821_RAW.tar"),
exdir = file.path(tempdir(), "GSE74821")
)
targets$IDFILE <- list.files(
path = file.path(tempdir(), "GSE74821"),
pattern = ".RCC.gz$"
)
targets[] <- lapply(X = targets, FUN = iconv, from = "latin1", to = "ASCII")
utils::write.csv(
x = targets,
file = file.path(tempdir(), "GSE74821", "Samplesheet.csv")
)
# Read RCC files and format
nacho <- load_rcc(
data_directory = file.path(tempdir(), "GSE74821"),
ssheet_csv = file.path(tempdir(), "GSE74821", "Samplesheet.csv"),
id_colname = "IDFILE"
)
}
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