NACHO Analysis
A NAnoString quality Control dasHbOard
Mickaël Canouil, Ph.D., Gerard A. Bouland and Roderick C. Slieker, Ph.D.
October 01, 2026
Source:vignettes/NACHO-analysis.Rmd
NACHO-analysis.Rmd
Installation
# Install NACHO from CRAN:
install.packages("NACHO")
# Or the development version from GitHub:
# install.packages("pak")
pak::pak("mcanouil/NACHO")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 an 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().
- The
load_rcc()function is used to preprocess the data. - The
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.
- The
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:
- The
render()function renders a full quality-control report (HTML) based on the results of a call toload_rcc()ornormalise()(usingprint()in an R Markdown chunk). - The
autoplot()function draws any quality-control metrics fromvisualise()andrender().
For more vignette("NACHO") and
vignette("NACHO-analysis").
Canouil M, Bouland GA, Bonnefond A, Froguel P, ’t Hart LM, Slieker RC (2020). “NACHO: an R package for quality control of NanoString nCounter data.” Bioinformatics, 36(3), 970–971. 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 M. {'t Hart} and Roderick C. Slieker},
journal = {Bioinformatics},
year = {2020},
month = {feb},
volume = {36},
number = {3},
pages = {970--971},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btz647},
}Analyse NanoString data
Load packages
library(NACHO)
library(data.table)
##
## Attaching package: 'data.table'
## The following object is masked from 'package:base':
##
## %notin%
library(GEOquery, quietly = TRUE, warn.conflicts = FALSE)
##
## Attaching package: 'generics'
## The following object is masked from 'package:NACHO':
##
## visualize
## The following objects are masked from 'package:base':
##
## as.difftime, as.factor, as.ordered, intersect, is.element, setdiff,
## setequal, union
##
## Attaching package: 'BiocGenerics'
## The following object is masked from 'package:NACHO':
##
## normalize
## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs
## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, saveRDS, table, tapply, unique,
## unsplit, which.max, which.min
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
## Warning: replacing previous import 'S4Arrays::makeNindexFromArrayViewport' by
## 'DelayedArray::makeNindexFromArrayViewport' when loading 'SummarizedExperiment'
## Setting options('download.file.method.GEOquery'='auto')
## Setting options('GEOquery.inmemory.gpl'=FALSE)Download GSE70970 from GEO (or use your own data)
The samples in GSE70970 were measured with two versions of the miRNA
CodeSet. Each CodeSet was exported by a different nSolver version, and
load_rcc() refuses files that mix versions, so the code
keeps the samples measured with NS_H_miR_1.4.
data_directory <- file.path(tempdir(), "GSE70970", "Data")
# Download data
gse <- getGEO("GSE70970")
## Found 1 file(s)
## GSE70970_series_matrix.txt.gz
getGEOSuppFiles(GEO = "GSE70970", baseDir = tempdir())
## size isdir
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 1986560 FALSE
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 672 FALSE
## mode
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 644
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 644
## mtime
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 2026-10-01 17:07:33
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 2026-10-01 17:07:33
## ctime
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 2026-10-01 17:07:33
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 2026-10-01 17:07:33
## atime
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 2026-10-01 17:07:33
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 2026-10-01 17:07:33
## uid gid
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar 1001 1001
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz 1001 1001
## uname grname
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar runner runner
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz runner runner
## fname
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar GSE70970_RAW.tar
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz GSE70970_characteristics_readme.txt.gz
## destdir
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar /tmp/RtmpF6sYqR/GSE70970
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz /tmp/RtmpF6sYqR/GSE70970
## filepath
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz
## GEO
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_RAW.tar GSE70970
## /tmp/RtmpF6sYqR/GSE70970/GSE70970_characteristics_readme.txt.gz GSE70970
# Unzip data
untar(
tarfile = file.path(tempdir(), "GSE70970", "GSE70970_RAW.tar"),
exdir = data_directory
)
# Get phenotypes and add IDs
targets <- pData(phenoData(gse[[1]]))
rcc_files <- list.files(data_directory, pattern = "\\.RCC(\\.gz)?$", ignore.case = TRUE)
targets$IDFILE <- rcc_files[match(targets$geo_accession, sub("_.*", "", rcc_files))]
targets <- targets[!is.na(targets$IDFILE), ]
# Keep the samples measured with the same CodeSet
codeset <- vapply(
X = file.path(data_directory, targets$IDFILE),
FUN = function(file) {
header <- grep("^GeneRLF,", readLines(file, n = 40), value = TRUE)
if (length(header) == 0) NA_character_ else header[1]
},
FUN.VALUE = character(1)
)
targets <- targets[codeset %in% "GeneRLF,NS_H_miR_1.4", ]Import RCC files
GSE70970 <- load_rcc(data_directory, targets, id_colname = "IDFILE")
## [NACHO] Importing RCC files.
## [NACHO] Performing QC and formatting data.
## [NACHO] Computing normalisation factors using "GEO" method.
## [NACHO] Missing values have been replaced with zeros for PCA.
## [NACHO] Normalising data using "GEO" method with housekeeping genes.
## [NACHO] Returning a list.
## $ access : character
## $ housekeeping_genes : character
## $ housekeeping_predict: logical
## $ housekeeping_norm : logical
## $ normalisation_method: character
## $ remove_outliers : logical
## $ n_comp : numeric
## $ data_directory : character
## $ pc_sum : data.frame
## $ nacho : data.frame
## $ outliers_thresholds : listPerform the analyses using limma
library(limma)
##
## Attaching package: 'limma'
## The following object is masked from 'package:BiocGenerics':
##
## plotMAGet 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")
]
selected_pheno <- na.exclude(selected_pheno)## IDFILE age:ch1 gender:ch1 chemo:ch1
## <char> <char> <char> <char>
## 1: GSM1824143_NPC-T-1.RCC.gz 45.97260274 Male 0
## 2: GSM1824144_NPC-T-10.RCC.gz 46.4 Male 1
## 3: GSM1824145_NPC-T-100.RCC.gz 50.36438356 Male 0
## 4: GSM1824146_NPC-T-101.RCC.gz 64.09041096 Female 1
## 5: GSM1824147_NPC-T-102.RCC.gz 27.57808219 Male 1
## 6: GSM1824148_NPC-T-103.RCC.gz 67.01369863 Male 1
## disease.event:ch1
## <char>
## 1: 1
## 2: 1
## 3: 0
## 4: 0
## 5: 1
## 6: 0Get 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")
]## GSM1824143_NPC-T-1.RCC.gz
## bkv-miR-B1-3p+jcv-miR-J1-3p 4.0
## bkv-miR-B1-5p 0.1
## ebv-miR-BART1-3p 3526.0
## ebv-miR-BART1-5p 196.0
## ebv-miR-BART10 22936.0
## GSM1824144_NPC-T-10.RCC.gz
## bkv-miR-B1-3p+jcv-miR-J1-3p 20.0
## bkv-miR-B1-5p 0.1
## ebv-miR-BART1-3p 2799.0
## ebv-miR-BART1-5p 95.0
## ebv-miR-BART10 5833.0
## GSM1824145_NPC-T-100.RCC.gz
## bkv-miR-B1-3p+jcv-miR-J1-3p 49
## bkv-miR-B1-5p 13
## ebv-miR-BART1-3p 1627
## ebv-miR-BART1-5p 92
## ebv-miR-BART10 3717
## GSM1824146_NPC-T-101.RCC.gz
## bkv-miR-B1-3p+jcv-miR-J1-3p 7.0
## bkv-miR-B1-5p 0.1
## ebv-miR-BART1-3p 2880.0
## ebv-miR-BART1-5p 73.0
## ebv-miR-BART10 5084.0
## GSM1824147_NPC-T-102.RCC.gz
## bkv-miR-B1-3p+jcv-miR-J1-3p 0.1
## bkv-miR-B1-5p 0.1
## ebv-miR-BART1-3p 4877.0
## ebv-miR-BART1-5p 44.0
## ebv-miR-BART10 6195.0Alternatively, "Accession" number is also available.
Select phenotypes and counts
- Make sure count matrix and phenotypes have the same samples
samples_kept <- intersect(selected_pheno[["IDFILE"]], colnames(expr_counts))
expr_counts <- expr_counts[, samples_kept]
selected_pheno <- selected_pheno[IDFILE %in% c(samples_kept)]- Build the numeric design matrix
design <- model.matrix(~ `disease.event:ch1`, selected_pheno)limma
eBayes(lmFit(expr_counts, design))
## An object of class "MArrayLM"
## $coefficients
## (Intercept) `disease.event:ch1`1
## bkv-miR-B1-3p+jcv-miR-J1-3p 17.138372 -2.843500
## bkv-miR-B1-5p 6.459302 2.961210
## ebv-miR-BART1-3p 3133.732558 211.446929
## ebv-miR-BART1-5p 197.898837 5.365265
## ebv-miR-BART10 7537.651163 2137.528324
## 730 more rows ...
##
## $stdev.unscaled
## (Intercept) `disease.event:ch1`1
## bkv-miR-B1-3p+jcv-miR-J1-3p 0.1078328 0.1930516
## bkv-miR-B1-5p 0.1078328 0.1930516
## ebv-miR-BART1-3p 0.1078328 0.1930516
## ebv-miR-BART1-5p 0.1078328 0.1930516
## ebv-miR-BART10 0.1078328 0.1930516
## 730 more rows ...
##
## $sigma
## [1] 29.29642 13.41464 4850.90726 351.44148 10106.31634
## 730 more elements ...
##
## $df.residual
## [1] 123 123 123 123 123
## 730 more elements ...
##
## $cov.coefficients
## (Intercept) `disease.event:ch1`1
## (Intercept) 0.01162791 -0.01162791
## `disease.event:ch1`1 -0.01162791 0.03726893
##
## $pivot
## [1] 1 2
##
## $rank
## [1] 2
##
## $Amean
## bkv-miR-B1-3p+jcv-miR-J1-3p bkv-miR-B1-5p
## 16.2512 7.3832
## ebv-miR-BART1-3p ebv-miR-BART1-5p
## 3199.7040 199.5728
## ebv-miR-BART10
## 8204.5600
## 730 more elements ...
##
## $method
## [1] "ls"
##
## $design
## (Intercept) `disease.event:ch1`1
## 1 1 1
## 2 1 1
## 3 1 0
## 4 1 0
## 5 1 1
## 120 more rows ...
##
## $df.prior
## [1] 0.5116399
##
## $s2.prior
## [1] 638.9296
##
## $var.prior
## [1] 2.504188e-02 1.565118e-05
##
## $proportion
## [1] 0.01
##
## $s2.post
## [1] 8.573719e+02 1.818537e+02 2.343383e+07 1.230021e+05 1.017145e+08
## 730 more elements ...
##
## $t
## (Intercept) `disease.event:ch1`1
## bkv-miR-B1-3p+jcv-miR-J1-3p 5.427929 -0.50303151
## bkv-miR-B1-5p 4.441951 1.13745622
## ebv-miR-BART1-3p 6.003293 0.22625923
## ebv-miR-BART1-5p 5.232824 0.07924309
## ebv-miR-BART10 6.930966 1.09785983
## 730 more rows ...
##
## $df.total
## [1] 123.5116 123.5116 123.5116 123.5116 123.5116
## 730 more elements ...
##
## $p.value
## (Intercept) `disease.event:ch1`1
## bkv-miR-B1-3p+jcv-miR-J1-3p 2.895225e-07 0.6158380
## bkv-miR-B1-5p 1.955768e-05 0.2575494
## ebv-miR-BART1-3p 1.996900e-08 0.8213738
## ebv-miR-BART1-5p 6.931537e-07 0.9369675
## ebv-miR-BART10 2.049447e-10 0.2744014
## 730 more rows ...
##
## $lods
## (Intercept) `disease.event:ch1`1
## bkv-miR-B1-3p+jcv-miR-J1-3p 3.6097221 -4.595276
## bkv-miR-B1-5p 0.9808053 -4.595059
## ebv-miR-BART1-3p 5.2607434 -4.595319
## ebv-miR-BART1-5p 3.0672696 -4.595328
## ebv-miR-BART10 8.0438100 -4.595077
## 730 more rows ...
##
## $F
## [1] 19.37878 19.38162 27.33149 20.24126 41.96523
## 730 more elements ...
##
## $F.p.value
## [1] 4.788440e-08 4.778104e-08 1.486979e-10 2.492262e-08 1.239564e-14
## 730 more elements ...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")
]
## Name Accession term Estimate Std. Error
## <char> <char> <char> <num> <num>
## 1: hsa-miR-758 MIMAT0003879 (Intercept) 17.68255814 3.147292
## 2: hsa-miR-758 MIMAT0003879 `disease.event:ch1`1 -1.33896840 5.634557
## 3: hsa-miR-1296 MIMAT0005794 (Intercept) 4.79418605 1.006124
## 4: hsa-miR-1296 MIMAT0005794 `disease.event:ch1`1 -0.06598092 1.801250
## 5: hsa-miR-548e MIMAT0005874 (Intercept) 11.71395349 2.760618
## 6: hsa-miR-548e MIMAT0005874 `disease.event:ch1`1 -2.88574836 4.942300
## 7: hsa-miR-874 MIMAT0004911 (Intercept) 45.03604651 6.305683
## 8: hsa-miR-874 MIMAT0004911 `disease.event:ch1`1 21.01779964 11.288983
## 9: hsa-miR-106b MIMAT0000680 (Intercept) 2707.67441860 256.404262
## 10: hsa-miR-106b MIMAT0000680 `disease.event:ch1`1 627.19737627 459.037265
## 11: hsa-miR-1825 MIMAT0006765 (Intercept) 33.87325581 5.550548
## 12: hsa-miR-1825 MIMAT0006765 `disease.event:ch1`1 2.97802624 9.937075
## 13: hsa-miR-133a MIMAT0000427 (Intercept) 40.52441860 13.164696
## 14: hsa-miR-133a MIMAT0000427 `disease.event:ch1`1 17.55763268 23.568586
## 15: hsa-miR-203 MIMAT0000264 (Intercept) 584.37209302 241.138855
## 16: hsa-miR-203 MIMAT0000264 `disease.event:ch1`1 747.44841980 431.707801
## 17: hsa-miR-222 MIMAT0000279 (Intercept) 3400.08139535 378.018063
## 18: hsa-miR-222 MIMAT0000279 `disease.event:ch1`1 -326.59421586 676.760893
## 19: hsa-miR-1973 MIMAT0009448 (Intercept) 276.46511628 45.990903
## 20: hsa-miR-1973 MIMAT0009448 `disease.event:ch1`1 211.45796064 82.336925
## Name Accession term Estimate Std. Error
## <char> <char> <char> <num> <num>
## t value Pr(>|t|)
## <num> <num>
## 1: 5.61833992 1.222143e-07
## 2: -0.23763507 8.125595e-01
## 3: 4.76500738 5.224757e-06
## 4: -0.03663063 9.708389e-01
## 5: 4.24323540 4.296251e-05
## 6: -0.58388778 5.603651e-01
## 7: 7.14213626 7.053164e-11
## 8: 1.86179739 6.501839e-02
## 9: 10.56017710 5.986499e-19
## 10: 1.36633216 1.743274e-01
## 11: 6.10268682 1.251554e-08
## 12: 0.29968842 7.649207e-01
## 13: 3.07826469 2.567805e-03
## 14: 0.74495909 4.577175e-01
## 15: 2.42338421 1.683288e-02
## 16: 1.73137575 8.589213e-02
## 17: 8.99449453 3.561126e-15
## 18: -0.48258435 6.302486e-01
## 19: 6.01129999 1.937975e-08
## 20: 2.56820328 1.141876e-02
## t value Pr(>|t|)
## <num> <num>