NACHO

In NACHO: NanoString Quality Control Dashboard

knitr::opts_chunk$set(
  eval = TRUE,
  collapse = TRUE,
  # results = "asis",
  include = TRUE,
  echo = TRUE,
  warning = TRUE,
  message = TRUE,
  error = TRUE,
  # tidy = FALSE,
  # crop = TRUE,
  # autodep = TRUE,
  fig.align = "center",
  fig.pos = "!h",
  cache = FALSE
)

knitr::include_graphics(path = "nacho_hex.png")

Installation

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

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

# Load NACHO
library(NACHO)

Overview

cat(readLines(system.file("app", "www", "about-nacho.md", package = "NACHO"))[-c(1, 2)], sep = "\n")

print(citation("NACHO"), "html")

print(citation("NACHO"), "bibtex")

An example

To display the usage and utility of NACHO, we show three examples in which the above mentioned functions are used and the results are briefly examined.

NACHO comes with presummarised data and in the first example we use this dataset to call the interactive web application using visualise().
In the second example, we show the process of going from raw RCC files to visualisations with a dataset queried from GEO using GEOquery.
In the third example, we use the summarised dataset from the second example to calculate the sample specific size factors using normalise() and its added functionality to predict housekeeping genes.

Besides creating interactive visualisations, NACHO also identifies poorly performing samples which can be seen under the Outlier Table tab in the interactive web application.
While calling normalise(), the user has the possibility to remove these outliers before size factor calculation.

Get NanoString nCounter data

Presummarised data from NACHO

This example shows how to use summarised data to call the interactive web application.
The raw data used is from a study of @liu_pam50_2016 and was acquired from the NCBI GEO public database [@barrett_ncbi_2013].

library(NACHO)
data(GSE74821)
visualise(GSE74821)

knitr::include_graphics(path = "README-visualise.png")

Raw data from GEO

Numerous NanoString nCounter datasets are available from GEO [@barrett_ncbi_2013].
In this example, we use a mRNA dataset from the study of @bruce_identification_2015 with the GEO accession number: GSE70970. The data is extracted and prepared using the following code.

gse <- try(GEOquery::getGEO("GSE70970"), silent = TRUE)
if (inherits(gse, "try-error")) { # when GEOquery is down
  cons <- showConnections(all = TRUE)
  icons <- which(grepl("GSE70970", cons[, "description"])) - 1
  for (icon in icons) close(getConnection(icon))
  cat(
    "Note: `GEOquery` seems to be currently down. Thus, the following code was not executed.\n"
  )
}

library(GEOquery)
# Download data
gse <- getGEO("GSE70970")
getGEOSuppFiles(GEO = "GSE70970", baseDir = tempdir())
# Unzip data
untar(
  tarfile = file.path(tempdir(), "GSE70970", "GSE70970_RAW.tar"),
  exdir = file.path(tempdir(), "GSE70970", "Data")
)
# Get phenotypes and add IDs
targets <- pData(phenoData(gse[[1]]))
targets$IDFILE <- list.files(file.path(tempdir(), "GSE70970", "Data"))

targets[1:5, unique(c("IDFILE", names(targets)))]

After we extracted the dataset to the `r file.path(tempdir(), "GSE70970", "Data")` directory, a Samplesheet.csv containing a column with the exact names of the files for each sample can be written or use as is.

The load_rcc() function

The first argument requires the path to the directory containing the RCC files, the second argument is the location of samplesheet followed by third argument with the column name containing the exact names of the files.
The housekeeping_genes and normalisation_method arguments respectively indicate which housekeeping genes and normalisation method should be used.

GSE70970_sum <- load_rcc(
  data_directory = file.path(tempdir(), "GSE70970", "Data"), # Where the data is
  ssheet_csv = targets, # The samplesheet
  id_colname = "IDFILE", # Name of the column that contains the unique identfiers
  housekeeping_genes = NULL, # Custom list of housekeeping genes
  housekeeping_predict = TRUE, # Whether or not to predict the housekeeping genes
  normalisation_method = "GEO", # Geometric mean or GLM
  n_comp = 5 # Number indicating how many principal components should be computed.
)

unlink(file.path(tempdir(), "GSE70970"), recursive = TRUE)

The visualise() function

When the summarisation is done, the summarised (or normalised) data can be visualised using the visualise() function as can be seen in the following chunk of code.

#| eval: false
visualise(GSE70970_sum)

The sidebar includes widgets to control quality-control thresholds. These widgets differ according to the selected tab. Each sample in the plots can be coloured based on either technical specifications which are included in the RCC files or based on specifications of your own choosing, though these specifications need to be included in the samplesheet.

The normalise() function

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, which is why a subset of these discovered housekeeping genes might work better in some cases. For this example, we use the GSE70970 dataset from the previous example. The discovered housekeeping genes are saved in the result object as predicted_housekeeping.

print(GSE70970_sum[["housekeeping_genes"]])

cat(
  "Let's say _", GSE70970_sum[["housekeeping_genes"]][1],
  "_ and _", GSE70970_sum[["housekeeping_genes"]][2],
  "_ are not suitable, therefore, you want to exclude these genes from the normalisation process.",
  sep = ""
)

my_housekeeping <- GSE70970_sum[["housekeeping_genes"]][-c(1, 2)]
print(my_housekeeping)

The next step is the actual normalisation. The first argument requires the summary which is created with the load_rcc() function. The second argument requires a vector of gene names. In this case, it is a subset of the discovered housekeeping genes we just made. With the third argument the user has the choice to remove the outliers. Lastly, the normalisation method can be choosed.
Here, the user has a choice between "GLM" or "GEO". The differences between normalisation methods are nuanced, however, a preference for either method are use case specific.
In this example, "GLM" is used.

GSE70970_norm <- normalise(
  nacho_object = GSE70970_sum,
  housekeeping_genes = my_housekeeping,
  housekeeping_predict = FALSE,
  housekeeping_norm = TRUE,
  normalisation_method = "GEO",
  remove_outliers = TRUE
)

normalise() returns a list object (same as load_rcc()) with raw_counts and normalised_counts slots filled with the raw and normalised counts. Both counts are also in the NACHO data.frame.

The autoplot() function

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

autoplot(
  object = GSE74821,
  x = "BD",
  colour = "CartridgeID",
  size = 0.5,
  show_legend = TRUE
)

The possible metrics (x) are:

• "BD" (Binding Density)
• "FoV" (Imaging)
• "PCL" (Positive Control Linearity)
• "LoD" (Limit of Detection)
• "Positive" (Positive Controls)
• "Negative" (Negative Controls)
• "Housekeeping" (Housekeeping Genes)
• "PN" (Positive Controls vs. Negative Controls)
• "ACBD" (Average Counts vs. Binding Density)
• "ACMC" (Average Counts vs. Median Counts)
• "PCA12" (Principal Component 1 vs. 2)
• "PCAi" (Principal Component scree plot)
• "PCA" (Principal Components planes)
• "PFNF" (Positive Factor vs. Negative Factor)
• "HF" (Housekeeping Factor)
• "NORM" (Normalisation Factor)

metrics <- c(
  "BD" = "Binding Density",
  "FoV" = "Imaging",
  "PCL" = "Positive Control Linearity",
  "LoD" = "Limit of Detection",
  "Positive" = "Positive Controls",
  "Negative" = "Negative Controls",
  "Housekeeping" = "Housekeeping Genes",
  "PN" = "Positive Controls vs. Negative Controls",
  "ACBD" = "Average Counts vs. Binding Density",
  "ACMC" = "Average Counts vs. Median Counts",
  "PCA12" = "Principal Component 1 vs. 2",
  "PCAi" = "Principal Component scree plot",
  "PCA" = "Principal Components planes",
  "PFNF" = "Positive Factor vs. Negative Factor",
  "HF" = "Housekeeping Factor",
  "NORM" = "Normalisation Factor"
)

for (imetric in seq_along(metrics)) {
  cat("\n\n###", metrics[imetric], "\n\n")
  print(autoplot(object = GSE74821, x = names(metrics[imetric])))
  cat("\n")
}

NACHO as a standalone app

NACHO is also available as a standalone app to be used in a shiny server configuration. A convenience function deploy() is available to directly copy the NACHO app to the default directory of a shiny server.

deploy(directory = "/srv/shiny-server", app_name = "NACHO")

The app can also be run directly, without manually summarising and normalising RCC files:

shiny::runApp(system.file("app", package = "NACHO"))

knitr::include_graphics(path = "README-app.png")

The render() function

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

render(
  nacho_object = GSE74821,
  colour = "CartridgeID",
  output_file = "NACHO_QC.html",
  output_dir = ".",
  size = 0.5,
  show_legend = TRUE,
  clean = TRUE
)

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

print(
  x = GSE74821,
  colour = "CartridgeID",
  size = 0.5,
  show_legend = TRUE,
  echo = TRUE,
  title_level = 3
)

References

NACHO documentation built on Aug. 8, 2023, 1:08 a.m.