The nCounter® platform provides a simple and cost effective solution for multiplex analysis of up to 800 RNA, DNA, or protein targets from your precious samples.

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  • Expertly curated pre-formatted panels for human, mouse and non-human primate
  • ~15-minutes total hands-on time with no amplification, cDNA conversion or library prep required
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  • Combine RNA, DNA, and protein panels for a comprehensive 3D Biology™ view of each sample
  • Optimized performance on difficult sample types including FFPE, tissue, lysates and biofluid samples

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  • Advanced analysis tools included with system reduce the need for Bioinformatics support

nSolver 4.0 ressources:

• Quick Start guide
• User Manual

• NACHO on CRAN (Since 2019-04-28; Next 2019-10)

NACHO (NAnostring quality Control dasHbOard) is developed for NanoString nCounter data.

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 in an interactive web application:

• quality control metrics
• expression of control genes
• principal components
• sample specific size factors

RCC files are summarised and visualised using two functions:

• The summarise() 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 this example we use a mRNA dataset from the study of Bruce et al. (2015) with the GEO accession number: GSE70970

library(GEOquery)
gse <- getGEO("GSE70970")
targets <- pData(phenoData(gse[[1]]))
getGEOSuppFiles(GEO = "GSE70970", baseDir = ".")
untar(
  tarfile = "./GSE70970/GSE70970_RAW.tar", 
  exdir = "./GSE70970/Data"
)
targets$IDFILE <- list.files("./GSE70970/Data")

The housekeeping_genes and normalisation_method arguments respectively indicate which housekeeping genes and normalisation method should be used.

library(NACHO)
GSE70970_sum <- summarise(
  data_directory = "./GSE70970/Data", 
  ssheet_csv = targets, 
  id_colname = "IDFILE",
  housekeeping_genes = NULL,
  housekeeping_predict = TRUE,
  normalisation_method = "GEO",
  n_comp = 5
)

GSE70970_sum

#> List of 13
#>  $ access              : chr "IDFILE"
#>  $ housekeeping_genes  : chr [1:5] "hsa-miR-103" "hsa-let-7e" "hsa-miR-1260" "hsa-miR-500+hsa-miR-501-5p" ...
#>  $ housekeeping_predict: logi TRUE
#>  $ housekeeping_norm   : logi TRUE
#>  $ normalisation_method: chr "GEO"
#>  $ remove_outliers     : logi FALSE
#>  $ n_comp              : num 5
#>  $ data_directory      : chr "/home/travis/build/mcanouil/NACHO_slides/GSE70970/Data"
#>  $ pc_sum              :'data.frame':    5 obs. of  4 variables:
#>  $ nacho               :'data.frame':    198170 obs. of  112 variables:
#>  $ outliers_thresholds :List of 6
#>  $ raw_counts          :'data.frame':    792 obs. of  265 variables:
#>  $ normalised_counts   :'data.frame':    792 obs. of  265 variables:
#>  - attr(*, "RCC_type")= chr "n1"
#>  - attr(*, "class")= chr "nacho"

NACHO allows the discovery of housekeeping genes within your own dataset.

NACHO finds the five best suitable housekeeping genes, however, it is possible that one of these five genes might not be suitable.

The discovered housekeeping genes are saved in a global variable named predicted_housekeeping.

GSE70970_sum[["housekeeping_genes"]]

#> [1] "hsa-miR-103"                "hsa-let-7e"                
#> [3] "hsa-miR-1260"               "hsa-miR-500+hsa-miR-501-5p"
#> [5] "hsa-miR-1274b"

Let’s say "GEO" is not the best normalisation for our dataset and we want to use "GLM" instead.

GSE70970_norm <- normalise(
  nacho_object = GSE70970_sum,
  normalisation_method = "GLM", 
  remove_outliers = TRUE
)

GSE70970_norm

#> List of 13
#>  $ access              : chr "IDFILE"
#>  $ housekeeping_genes  : chr [1:5] "hsa-let-7e" "hsa-miR-1260" "hsa-miR-1274b" "hsa-miR-103" ...
#>  $ housekeeping_predict: logi TRUE
#>  $ housekeeping_norm   : logi TRUE
#>  $ normalisation_method: chr "GLM"
#>  $ remove_outliers     : logi TRUE
#>  $ n_comp              : num 5
#>  $ data_directory      : chr "/home/travis/build/mcanouil/NACHO_slides/GSE70970/Data"
#>  $ pc_sum              :'data.frame':    5 obs. of  4 variables:
#>  $ nacho               :'data.frame':    115271 obs. of  112 variables:
#>  $ raw_counts          :'data.frame':    792 obs. of  155 variables:
#>  $ normalised_counts   :'data.frame':    792 obs. of  155 variables:
#>  $ outliers_thresholds :List of 6
#>  - attr(*, "RCC_type")= chr "n1"
#>  - attr(*, "class")= chr "nacho"

visualise(GSE70970_sum)

#> [NACHO] Custom "outliers_thresholds" can be loaded for later use with:
#>   outliers_thresholds <- readRDS("outliers_thresholds.rds")

NACHO includes two (three) additional functions:

• The render() function renders a full quality-control report (HTML) based on the results of a call to summarise() or normalise() (using print() in a Rmarkdown chunk).

• The autoplot() function draws any quality-control metrics from visualise() and render().

The autoplot() function provides an easy way to plot any quality-control from the visualise() function.

autoplot(GSE70970_sum, x = "BD")

autoplot(GSE70970_sum, x = "Positive")

autoplot(GSE70970_sum, x = "Housekeeping")

autoplot(GSE70970_sum, x = "NORM")

The render() function renders (using print(..., echo = TRUE) a comprehensive HTML report which includes all quality-control metrics and description of those metrics.

render(
  nacho_object = GSE70970_sum,
  colour = "CartridgeID",
  output_file = "NACHO_QC.html",
  output_dir = "./GSE70970/",
  size = 0.5,
  show_legend = TRUE,
  clean = TRUE
)

The underneath function print() can be used directly within any Rmakrdown chunk, setting the parameter echo = TRUE.

print(
  x = GSE70970_sum, 
  colour = "CartridgeID", 
  size = 0.5, 
  show_legend = TRUE, 
  echo = TRUE, 
  title_level = 3
)

• Code rewrite and optimisation

• Normalisation method template

CARoT (Centralised and Automated Reporting Tools) is an under development set of Quality-Control reporting tools and some other functions.

Currently CARoT includes the following main functions:

• estimate_ethnicity() Compute the genomic component (ethnicity)
• pca_report() Compute a principal component analysis
• qc_idats() QC of methylation array
• qc_plink() QC of genotyping array
• qc_impute() QC of imputated genotyping array