Package {NACHO}

Type:	Package
Title:	NanoString Quality Control Dashboard
Version:	3.0.0
Description:	NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse 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.
License:	GPL-3
Copyright:	See file inst/COPYRIGHTS.
URL:	https://github.com/mcanouil/NACHO/, https://m.canouil.dev/NACHO/
BugReports:	https://github.com/mcanouil/NACHO/issues
Depends:	R (≥ 4.3)
Imports:	methods, utils, grDevices, bslib (≥ 0.10.0), brand.yml (≥ 0.1.0), cli (≥ 3.6.0), data.table, ggplot2 (≥ 4.0.0), ggforce (≥ 0.5.0), ggrepel (≥ 0.9.6), htmltools (≥ 0.5.9), promises (≥ 1.3.2), rlang (≥ 1.1.0), S7 (≥ 0.2.2), scales (≥ 1.4.0), shiny (≥ 1.11.0), yaml (≥ 2.3.12)
Suggests:	chromote (≥ 0.5.1), colorspace (≥ 2.1-3), gdtools (≥ 0.5.2), ggiraph (≥ 0.9.6), jsonlite (≥ 2.0.0), knitr (≥ 1.39), later, mirai (≥ 2.7.3), parallel, png (≥ 0.1-9), quarto (≥ 1.5.1), reactable (≥ 0.4.5), rmarkdown (≥ 1.16), roxygen2 (≥ 8.1.0), shinytest2 (≥ 0.5.1), testthat (≥ 3.1.5), vdiffr (≥ 1.0.9), withr, covr (≥ 3.3.2), Biobase, GEOquery, limma, NanoStringNCTools, NormqPCR, RUVSeq, S4Vectors, SummarizedExperiment
VignetteBuilder:	knitr
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
SystemRequirements:	Quarto command-line interface (>= 1.9.18), pandoc (>= 2.11)
Config/Needs/website:	pdftools
Config/testthat/edition:	3
Config/roxygen2/version:	8.1.0
Collate:	'GSE74821.R' 'NACHO-package.R' 'thresholds.R' 'qc.R' 'nacho-class.R' 'accessors.R' 'daemon.R' 'render.R' 'mod_export.R' 'qc-table.R' 'report.R' 'mod_qc_plot.R' 'mod_batch.R' 'mod_outliers.R' 'mod_overview.R' 'mod_thresholds.R' 'load_rcc.R' 'mod_data.R' 'tour.R' 'app.R' 'autoplot.R' 'stability.R' 'ruv.R' 'mirna.R' 'batch.R' 'brand.R' 'conditions.R' 'deploy.R' 'detection.R' 'interop.R' 'methods.R' 'normalise.R' 'plot_worker.R' 'read_nacho.R' 'read_rcc.R' 'utils.R' 'visualise.R' 'zzz.R'
NeedsCompilation:	no
Packaged:	2026-11-01 21:14:38 UTC; runner
Author:	Mickaël Canouil [aut, cre], Roderick Slieker [aut], Gerard Bouland [aut]
Maintainer:	Mickaël Canouil <pro@mickael.canouil.dev>
Repository:	CRAN
Date/Publication:	2026-11-01 21:50:02 UTC

NACHO: NanoString Quality Control Dashboard

Description

NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) doi:10.1038/nbt1385). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse 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.

Author(s)

Maintainer: Mickaël Canouil pro@mickael.canouil.dev (ORCID)

Authors:

• Mickaël Canouil pro@mickael.canouil.dev (ORCID)

• Roderick Slieker R.C.Slieker@lumc.nl (ORCID)

• Gerard Bouland G.A.Bouland@lumc.nl

See Also

Useful links:

• https://github.com/mcanouil/NACHO/

• https://m.canouil.dev/NACHO/

• Report bugs at https://github.com/mcanouil/NACHO/issues

A "nacho" object with 48 samples from the GSE74821 dataset

Description

The 48 samples come from four cartridges, normalised with the "GLM" method.

Usage

GSE74821

Format

A nacho object with 48 samples and 72 probes.

Details

NanoString nCounter RUO-PAM50 Gene Expression Custom CodeSet

Source

GSE74821

Convert between nacho objects and Bioconductor containers

Description

as_summarized_experiment() gives a SummarizedExperiment with the raw and normalised counts as assays counts and normalised, nacho_samples() as colData, nacho_probes() as rowData, and the settings, thresholds, RCC type and provenance in metadata()$nacho.

Usage

as_summarized_experiment(x)

as_nacho(x, id_colname = "IDFILE")

Arguments

Details

as_nacho() goes the other way, from a SummarizedExperiment or from a NanoStringRccSet read by NanoStringNCTools::readNanoStringRccSet(). The dates of a NanoStringRccSet are written as YYYYMMDD, as in RCC files. Its probe names lose a trailing numeric suffix such as ⁠|0⁠, as in load_rcc(), so both give the same probe names for the same RCC files. It computes the quality-control metrics and the normalisation from the raw counts; settings and thresholds come from metadata()$nacho when present, and are the defaults otherwise. They are checked like the arguments of normalise(). The result always carries the current object schema. A nacho object is checked and returned as is, and a NACHO 2 object gets a hint to use upgrade_nacho().

Value

A SummarizedExperiment, or a nacho object.

Examples

se <- as_summarized_experiment(GSE74821)
as_nacho(se)

autoplot generic

Description

See ggplot2::autoplot for details.

Plot the quality control of a nacho object

Description

Draws any of the quality-control figures of the Shiny app (visualise()) and of the HTML or PDF report (render()).

Value

A ggplot object.

Usage

autoplot(
  object,
  type,
  colour = "CartridgeID",
  size = 0.5,
  show_legend = TRUE,
  show_outliers = TRUE,
  outliers_factor = 1,
  outliers_labels = NULL,
  dark = FALSE
)

Arguments

• object: A nacho object from load_rcc() or normalise().

• type: The plot to draw, one of:

  • "BD": Binding Density.

  • "FoV": Field of View (imaging).

  • "PCL": Positive Control Linearity.

  • "LoD": Limit of Detection.

  • "Positive": Positive controls.

  • "Negative": Negative controls.

  • "Housekeeping": Housekeeping genes.

  • "PN": Positive controls against negative controls.

  • "ACBD": Average counts against binding density.

  • "ACMC": Average counts against median counts.

  • "PCA12": Principal component 1 against 2.

  • "PCAi": Scree plot of the principal components.

  • "PCA": Planes of the first principal components.

  • "PFNF": Positive factor against negative factor.

  • "HF": Housekeeping factor.

  • "NORM": Normalisation factor. The line is a loess trend of the counts, fitted on the sample means once there are enough samples. The band around it is the 95% confidence interval of that trend, not the spread of the genes, so it is thin once the counts are normalised.

  • "Stability": geNorm stability of the housekeeping genes, see housekeeping_stability(). The dashed line marks M = 1.5, the limit geNorm suggests for homogeneous samples. This plot ignores colour, show_legend, show_outliers and outliers_labels.

  • "RLE": Relative log expression of the normalised endogenous genes. This plot ignores size, show_outliers, outliers_factor and outliers_labels.

  • "BatchFactors": Normalisation factors by cartridge. This plot ignores show_outliers, outliers_factor and outliers_labels.

  • "PCBatch": Share of each principal component explained by cartridge and date; see batch_diagnostics(). Tiles with no value stay grey and unlabelled. This plot ignores colour, size, show_legend, show_outliers, outliers_factor and outliers_labels.

• colour: The column of nacho_samples(object) that colours the points.

• size: The point size, and the line width in the "NORM" plot.

• show_legend: If FALSE, hide the colour legend.

• show_outliers: If TRUE, draw the flagged samples as triangles in the accent colour: rust on a light background, amber on a dark one.

• outliers_factor: The size of the flagged samples, relative to size.

• outliers_labels: The column of nacho_samples(object) that labels the flagged samples, or NULL for no labels. Labels imply show_outliers = TRUE.

• dark: If TRUE, draw the plot for a dark background.

Examples

autoplot(GSE74821, type = "BD")

Batch and confounding diagnostics

Description

Checks whether the study design, the quality-control metrics and the main axes of variation follow technical batches such as cartridges and run dates.

Usage

batch_diagnostics(x, group = NULL, batch = c("CartridgeID", "Date"))

Arguments

Details

Start with design: when a batch level holds a single group, batch and biology cannot be told apart, and no normalisation can fix it.

Value

A list:

• design: NULL without group; otherwise, for each batch variable, its number of levels, Cramér's V with group, the number of levels that hold a single group, and confounded, TRUE when at least one does. A level counts as holding a single group only when it has two or more samples with a group, so a cartridge with one sample never makes the design confounded. Cramér's V uses only the samples that have both a group and a batch, and is NA when the batch or group has fewer than two levels among them.

• crosstabs: NULL without group; otherwise the table of group against each batch variable.

• metrics: Kruskal-Wallis tests of each quality-control metric against each batch variable.

• pc_batch: the share of each principal component's variance explained by each batch variable.

metrics and pc_batch are data frames with these columns, and have no rows when there is nothing to report. A batch variable with one level gives NA in statistic, p_value and r_squared, and a constant metric gives NA in statistic and p_value. A batch variable whose levels each hold one sample with a value also gives NA in r_squared. For PlexSet data, BD and FoV are tested once per lane.

Examples

data(GSE74821)
batch_diagnostics(GSE74821)$pc_batch

Deploy (copy) the Shiny application to the specified directory

Description

Deploy (copy) the Shiny application to the specified directory

Usage

deploy(directory, app_name = "NACHO")

Arguments

Details

The deployed app calls NACHO::nacho_app(), so the server needs NACHO installed. The app sets the nacho.plot_workers option to 1 when the option is not set, so it starts one background process for the interactive plots. That process and the plot cache belong to the R process that runs the app, and each process keeps a copy of the study, so its memory grows with the size of the study (about 170 MB for GSE74821). To change the number of processes, set the option before the app starts, for example in .Rprofile on the server. See the "Plot workers" section of nacho_app().

Value

[logical] A logical indicating whether the deployment is successful (TRUE) or not (FALSE).

Examples

deploy(directory = tempdir())

if (interactive()) {
  shiny::runApp("NACHO")
}

Drop outliers and normalise the other samples again

Description

Removes the samples whose status in nacho_qc() is "fail" and runs the normalisation again on the samples that are left, with the same settings and thresholds. The new factors can flag more samples; call exclude_outliers() again to drop those too.

Usage

exclude_outliers(nacho_object)

Arguments

Value

A nacho object without the flagged samples.

Examples

data(GSE74821)
ncol(exclude_outliers(GSE74821))

Keep genes detected in enough samples

Description

A gene is detected in a sample when its raw count is above the mean plus two standard deviations of that sample's negative controls. Control and housekeeping probes are always kept, where housekeeping probes are the probes of CodeClass Housekeeping. Endogenous genes chosen as housekeeping genes are filtered like any other gene, and the dropped ones are removed from the housekeeping_genes setting, which becomes NULL when none is left. Then the object uses its Housekeeping probes and warns, or, with no Housekeeping probe either, turns housekeeping_norm off and warns. The samples' Detection_rate and Housekeeping_detected are not recomputed after filtering. A gene's detection rate is the share of the samples with a detection limit in which it is detected, so a sample with fewer than two negative probes does not count. Endogenous genes whose detection rate is missing, because every sample with a detection limit has a missing count for them, are dropped, even with min_rate = 0. With all-zero negative controls the detection limit is 0, so every non-zero count counts as detected. An object without endogenous genes comes back unchanged. When no sample has a detection limit, filter_detected() stops with an error.

Usage

filter_detected(x, min_rate = 0.5)

Arguments

Value

A nacho object with fewer probes.

Examples

data(GSE74821)
dim(filter_detected(GSE74821, min_rate = 0.5))

Rank genes by expression stability

Description

Ranks candidate reference genes with geNorm (Vandesompele et al. 2002) and NormFinder (Andersen et al. 2004), after keeping the genes detected in most samples. With group, NormFinder accounts for the groups, and a Kruskal-Wallis test tells whether each gene's expression differs between them, which makes a poor reference gene.

Usage

housekeeping_stability(x, genes = NULL, group = NULL, min_detection = 0.9)

Arguments

Details

The two most stable genes share the same geNorm M by construction, so their order, ranks 1 and 2, is arbitrary. Without group, NormFinder_rho is the standard deviation of the residual variation of the gene. With group, it also counts the variation between the groups, shrunk towards zero as NormFinder does.

Value

A list with ranking, a data frame with one row per gene from the most to the least stable: Name, CodeClass, detection_rate, mean_log2, geNorm_M, geNorm_rank, NormFinder_rho and, with group, group_p_value and group_p_adjusted (Benjamini-Hochberg); and pairwise_v, the geNorm pairwise variation V for each number of genes. A V below 0.15 is the usual sign that adding a gene no longer helps.

Examples

data(GSE74821)
housekeeping_stability(GSE74821)$ranking

Produce a "nacho" object from RCC NanoString files

Description

This function is used to preprocess the data from NanoString nCounter.

Usage

load_rcc(
  data_directory,
  ssheet_csv,
  id_colname = NULL,
  housekeeping_genes = NULL,
  housekeeping_predict = FALSE,
  housekeeping_norm = NULL,
  normalisation_method = "GEO",
  background = "none",
  background_mode = "threshold",
  ruv_k = NULL,
  instrument = NULL,
  preset = "nsolver",
  n_comp = 10
)

Arguments

Value

A nacho object; read its content with nacho_counts(), nacho_samples(), nacho_probes() and nacho_qc().

Examples

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"
  )
}

Get the content of a nacho object

Description

These accessors are the only public way into a nacho object.

Usage

nacho_counts(x, normalised = FALSE, log2 = FALSE)

nacho_samples(x)

nacho_probes(x)

nacho_qc(x)

Arguments

Value

• nacho_counts(): a matrix with one row per probe and one column per sample.

• nacho_samples(): a data frame with one row per sample; the first column holds the sample ids, and the PCA scores come last (PC01, PC02, ...). Detection_rate (the share of endogenous genes detected) and Housekeeping_detected (the number of housekeeping genes detected) are NA for a sample without a detection limit, and Housekeeping_detected is also NA for a miRNA panel that skips housekeeping normalisation. miRNA panels add Ligation_order, Ligation_R2 and Ligation_NEG (the ligation controls), and Haemolysis, the log2 ratio of ⁠miR-451a⁠ to ⁠miR-23a-3p⁠, when those probes are present.

• nacho_probes(): a data frame with one row per probe: CodeClass, Name, Accession, detection_rate (the share of samples with a detection limit in which the probe is above it), is_housekeeping and is_excluded.

• nacho_qc(): a data frame with one row per sample: the id, lane, CartridgeID, each quality-control metric with its status ("pass", "fail" or NA when it cannot be assessed), the other factors, lane_status for PlexSet files, n_flags (the sample's own failing metrics, not those inherited from its lane), the overall status and a readable reason (which also names the lane metrics a sample inherits).

Examples

data(GSE74821)
nacho_counts(GSE74821)[1:5, 1:3]
head(nacho_samples(GSE74821)[, 1:5])
head(nacho_probes(GSE74821))
head(nacho_qc(GSE74821))

Basic methods for a nacho object

Description

Basic methods for a nacho object

Usage

print(x)
format(x)
summary(object)
dim(x)
as.data.frame(x, long = FALSE)
x[i, j]

Arguments

• x, object: A nacho object.

• long: If TRUE, as.data.frame() returns one row per probe and sample (the NACHO 2 ⁠$nacho⁠ layout) instead of one row per sample.

• i: Probes to keep.

• j: Samples to keep.

Examples

data(GSE74821)
print(GSE74821)
summary(GSE74821)
dim(GSE74821)
head(as.data.frame(GSE74821, long = TRUE)[, 1:5])
GSE74821[, 1:12]

Run the NACHO app

Description

Opens the quality-control app on a nacho object, or on an empty upload page. Use visualise() to get the tuned object back in the R session.

Usage

nacho_app(x = NULL, done = FALSE)

Arguments

Value

A Shiny app object; print it or pass it to shiny::runApp().

Plot workers

When the mirai and ggiraph packages are installed, the app builds the interactive plots of a page at the same time, in background R processes. The processes and the plot cache belong to the R process that runs the app. All users of that R process share them. The app starts the processes when the first user opens a page with interactive plots, and stops them when the app stops. By default, the app starts one process for each CPU core minus one, with a maximum of four. The app from deploy() starts one process.

Each process keeps a copy of the study that it draws, so its memory grows with the size of the study. For GSE74821, each process uses about 170 MB. To find the number of processes, divide the memory that you can give to the plots by the memory of one process. Set the nacho.plot_workers option to that number before you start the app, for example options(nacho.plot_workers = 2). To build all plots in the app process, set the option to 0. If the processes do not start, the app builds the plots in its own process.

The processes use the installed NACHO package.

Examples

if (interactive()) {
  shiny::runApp(nacho_app(GSE74821))
}

Quality-control thresholds

Description

Builds the thresholds that load_rcc() and normalise() use to flag samples.

Usage

nacho_thresholds(
  instrument = c("max", "flex", "pro", "sprint"),
  preset = c("nsolver", "legacy"),
  haemolysis = FALSE
)

Arguments

Details

"nsolver" follows the current Bruker nCounter knowledge base (https://brukerspatialbiology.com/support/, "nCounter QC flags"): binding density 0.05 to 2.25 on MAX, FLEX and PRO and 0.1 to 1.8 on SPRINT, field of view at least 75 %, positive control linearity at least 0.95 (POS_A to POS_E, with log2(count + 1)), limit of detection at least 2, positive factor 0.3 to 3 and housekeeping factor 0.1 to 10. It also asks for at least three housekeeping genes above background, as Bruker asks (Housekeeping_detected).

"legacy" reproduces NACHO 2: binding density 0.1 to 2.25 on every instrument, positive control linearity with POS_F, positive factor 1/4 to 4 and housekeeping factor 1/11 to 11. With ⁠background = "geo", background_mode = "subtract"⁠, it gives back the NACHO 2 outlier calls.

The preset also chooses how negative probes are excluded and how the positive control linearity is computed, so changing it normalises again.

For miRNA panels, Ligation_order (the three ligation positive controls in decreasing order), Ligation_R2 (their log2 counts on a line) and Ligation_NEG (the largest ligation negative minus the detection limit) are NACHO's own definitions, since Bruker publishes no threshold. "legacy" never flags them.

Whole-blood RNA raises LIG_NEG_A, and to a lesser extent LIG_NEG_B, together with red blood cell miRNAs such as ⁠miR-451a⁠. The ligation negatives of such samples are above the detection limit, so most samples of a whole-blood study fail Ligation_NEG. This pattern occurs in independent PAXgene whole-blood studies on GEO (GSE303669 and GSE317496), and not in plasma, serum or extracellular vesicle studies. When more than half of the samples fail Ligation_NEG, load_rcc() warns about this pattern. For whole-blood samples, set a higher upper bound for Ligation_NEG and pass the thresholds to normalise(outliers_thresholds = ). Do not remove these samples with exclude_outliers() because of this flag only.

Each element can be changed afterwards; two numbers are a lower and an upper bound, and one number is a lower bound. Use -Inf or Inf for an open bound.

Value

A named list: preset, instrument, then one element per metric.

Examples

nacho_thresholds()
nacho_thresholds("sprint")
nacho_thresholds(preset = "legacy")

(Re)normalise a nacho object

Description

(Re)normalise a nacho object

Usage

normalise(
  nacho_object,
  housekeeping_genes = nacho_object@settings[["housekeeping_genes"]],
  housekeeping_predict = nacho_object@settings[["housekeeping_predict"]],
  housekeeping_norm = nacho_object@settings[["housekeeping_norm"]],
  normalisation_method = nacho_object@settings[["normalisation_method"]],
  background = nacho_object@settings[["background"]],
  background_mode = nacho_object@settings[["background_mode"]],
  ruv_k = nacho_object@settings[["ruv_k"]],
  n_comp = nacho_object@settings[["n_comp"]],
  outliers_thresholds = nacho_object@thresholds,
  ...
)

Arguments

Details

When only the limits in outliers_thresholds change and the preset stays the same, normalise() keeps the counts and stores the new thresholds; the flags follow them whenever you read nacho_qc(). Otherwise it computes the quality-control metrics, the normalisation factors, the normalised counts and the PCA again.

nacho_qc() lists the flags, and exclude_outliers() drops the flagged samples.

With normalisation_method = "GLM", each sample's positive and negative control counts plus 1 are fitted against their known concentrations with a Poisson GLM and an identity link, and the positive factor is the mean slope over the sample's slope. When the fit fails for any sample, NACHO warns and uses the geometric mean for every sample, so all samples stay on the same scale. The recorded normalisation_method then becomes "GEO", and the failed samples are listed in provenance$glm_fallback.

The normalisation runs in this order: raw counts, negative probe exclusion, background (background and background_mode), positive control factor (normalisation_method), then content factor (housekeeping genes when housekeeping_norm is TRUE). RUVg works on log2(count + 1) after the positive factor and returns counts on the count scale, floored at 0. It replaces the housekeeping scaling, so housekeeping_norm has no effect and House_factor is not computed, and it corrects only the endogenous and housekeeping probes. The miRNA methods "stable_mirna", "total_mirna", "spike_in" and "ligation" also ignore housekeeping_norm, housekeeping_genes and housekeeping_predict. They store their factor in House_factor, which is judged against the House_factor threshold, and the probes they used in provenance$content_probes. Normalised counts are otherwise neither rounded nor floored; use nacho_counts(x, normalised = TRUE, log2 = TRUE) for log2(count + 1).

Value

A nacho object.

Examples

data(GSE74821)
GSE74821_geo <- normalise(GSE74821, normalisation_method = "GEO")
nacho_qc(GSE74821_geo)

Read a saved NACHO object

Description

Reads an .rds file written with saveRDS() from any NACHO version. NACHO 2 objects go through upgrade_nacho(). NACHO 3 objects are rebuilt with the current class, because each saved S7 object carries a copy of the class it was made with.

Usage

read_nacho(path)

Arguments

Details

Only read files you trust: readRDS() can run code stored in a crafted file, notably in R before 4.4.0.

Value

A nacho object.

See Also

upgrade_nacho()

Examples

path <- tempfile(fileext = ".rds")
saveRDS(GSE74821, path)
read_nacho(path)

Render the quality-control report of a nacho object

Description

Writes a quality-control report with Quarto. The report starts with a cover that gives the study details. Then comes a decision summary, with the flagged samples and notes on how the data were processed. A table gives each setting and limit, with its meaning and its source. Each plot of autoplot() and each table has a short "How to read this". A methods appendix gives the citation and the package versions.

Usage

render(
  x,
  format = c("html", "typst"),
  output_dir = ".",
  colour = "CartridgeID",
  group = NULL,
  size = 1,
  show_legend = TRUE,
  outliers_factor = 1,
  outliers_labels = NULL,
  title = NULL,
  author = NULL
)

Arguments

Details

The report needs the Quarto command-line interface 1.9.18 or newer, and the quarto, knitr and rmarkdown packages. RStudio and Positron bundle Quarto; elsewhere, install it from https://quarto.org/docs/get-started/. The PDF goes through Typst, which Quarto bundles, so no LaTeX is needed.

The PDF is tagged and declares PDF/UA-1. When Typst compiles the PDF, it checks the PDF/UA-1 rules that it can enforce. When Quarto shows the warning "verapdf is not installed", you can ignore it: it only means that Quarto did not do the extra validation step.

Value

The path of the report, invisibly.

Examples

if (interactive()) {
  data(GSE74821)
  render(GSE74821, output_dir = tempdir())
  render(GSE74821, format = "typst", output_dir = tempdir())
}

Suggest how many unwanted factors RUVg should remove

Description

For each k from 0 to max_k, corrects the log2 positive-normalised counts with RUVg, using the housekeeping genes as controls, and reports the mean interquartile range of the relative log expression (RLE) and the share of variance on the first principal component. The suggested k is the smallest whose RLE spread is within 5 % of the smallest one: removing more factors risks removing biology.

Usage

suggest_ruv_k(x, max_k = 5)

Arguments

Value

A data frame with k, rle_iqr, pc1_variance and suggested. pc1_variance is NA when the corrected values have no variance, as with one sample.

Examples

data(GSE74821)
suggest_ruv_k(GSE74821)

Convert a NACHO 2 object to NACHO 3

Description

NACHO 3 stores data in an S7 nacho object instead of the NACHO 2 list. upgrade_nacho() converts a NACHO 2 object once: it keeps the raw counts, the sample sheet, the settings and the thresholds, and computes the quality-control metrics, the normalised counts, the PCA and the outlier flags again with the NACHO 3 definitions.

Usage

upgrade_nacho(x)

Arguments

Value

A nacho object.

See Also

read_nacho()

Examples

# A NACHO 2 object is converted; GSE74821 is already a NACHO 3 object,
# so it comes back unchanged.
data(GSE74821)
identical(upgrade_nacho(GSE74821), GSE74821)

Explore and tune quality control in the NACHO app

Description

Opens nacho_app() on nacho_object. Change thresholds and normalisation settings in the sidebar, then click "Done" to close the app and get the tuned object back.

Usage

visualise(nacho_object)

Arguments

Value

The tuned nacho object, invisibly, once you click "Done".

Examples

if (interactive()) {
  data(GSE74821)
  tuned <- visualise(GSE74821)
  nacho_qc(tuned)
}

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"
  )
  visualise(nacho)

  # Drop the outliers and normalise the other samples again
  visualise(exclude_outliers(nacho))

  # Normalise with the "GLM" method, then drop the outliers
  nacho_glm <- normalise(nacho, normalisation_method = "GLM")
  visualise(exclude_outliers(nacho_glm))
}

Write and read quality-control thresholds

Description

write_thresholds() saves thresholds to a YAML file, and read_thresholds() reads them back. Use the file to apply the same quality-control policy to each study, or to load thresholds that you tuned in nacho_app(). The Export page of the app writes the same file.

Usage

write_thresholds(thresholds, file)

read_thresholds(file)

Arguments

Details

In the file, .inf and -.inf are open bounds, and .na.character is an unknown instrument. You can edit the file by hand. read_thresholds() reads whole numbers such as 80 as numbers, and checks the thresholds as normalise() does. If the file does not give a miRNA threshold (Ligation_order, Ligation_R2, Ligation_NEG or Haemolysis), read_thresholds() uses the value of the preset in the file.

Value

write_thresholds() returns file, invisibly. read_thresholds() returns the list of thresholds.

Examples

file <- tempfile(fileext = ".yml")
thresholds <- nacho_thresholds("sprint")
thresholds$FoV <- 80
write_thresholds(thresholds, file)
read_thresholds(file)
unlink(file)