NanoStringRccSet-autoplot {NanoStringNCTools} | R Documentation |
Generate common plots to visualize and QC NanoStringRccSet data.
## S3 method for class 'NanoStringRccSet' autoplot(object, type = c("boxplot-feature", "boxplot-signature", "bindingDensity-mean", "bindingDensity-sd", "ercc-linearity", "ercc-lod", "heatmap-genes", "heatmap-signatures", "housekeep-geom", "lane-bindingDensity", "lane-fov", "mean-sd-features", "mean-sd-samples"), log2scale = TRUE, elt = "exprs", index = 1L, geomParams = list(), tooltipDigits = 4L, heatmapGroup = NULL, blacklist = NULL, tooltipID = NULL, qcCutoffs = list( Housekeeper = c("failingCutoff" = 32,"passingCutoff" = 100) , Imaging = c("fovCutoff" = 0.75) , BindingDensity = c("minimumBD" = 0.1, "maximumBD" = 2.25, "maximumBDSprint" = 1.8) , ERCCLinearity = c("correlationValue" = 0.95) , ERCCLoD = c("standardDeviations" = 2) ), scalingFactor=1L, show_rownames_gene_limit=60L, show_colnames_gene_limit=36L, show_rownames_sig_limit=60L, show_colnames_sig_limit=36L, subSet = NULL , ...)
object |
A NanoStringRccSet object |
type |
Character string referencing the type of plot to generate |
log2scale |
An optional boolean indicating expression data is on log2 scale |
elt |
An optional character string of the expression matrix name |
index |
An optional integer giving the feature of interest row location |
geomParams |
An option |
tooltipDigits |
An optional integer for number of tooltip decimal places to display |
heatmapGroup |
An optional character string referencing |
blacklist |
An optional character vector of features not to plot |
tooltipID |
An optional character string referencing |
qcCutoffs |
An optional |
scalingFactor |
An optional numeric value indicating a scaling factor to apply to plot drawing |
show_rownames_gene_limit |
An optional integer limit on number of features to display row-wise |
show_colnames_gene_limit |
An optional integer limit on number of features to display column-wise |
show_rownames_sig_limit |
An optional integer limit on number of signatures to display row-wise |
show_colnames_sig_limit |
An optional integer limit on number of signatures to display column-wise |
subSet |
An optional subset to plot on |
... |
Additional arguments to pass on to autoplot function |
"boxplot-feature"
Generate feature boxplots
"boxplot-signature"
Generate signature boxplots
"bindingDensity-mean"
Plot binding density displayed as average expression
"bindingDensity-sd"
Plot binding density displayed as standard deviation of expression
"ercc-linearity"
Assess linearity of ERCCs
"ercc-lod"
Assess limit of detection based on ERCC expression
"heatmap-genes"
Generate a heatmap from feature expression
"heatmap-signatures"
Generate a heatmap from signature expression
"housekeep-geom"
Plot geometric mean of housekeeper genes
"lane-bindingDensity"
View binding density by lane
"lane-fov"
Assess image quality by lane
"mean-sd-features"
Plot mean versus standard deviation feature-wise
"mean-sd-samples"
Plot mean versus standard deviation sample-wise
A ggplot
or pheatmap
plot depending on the type of plot generated
# Create NanoStringRccSet from data files datadir <- system.file("extdata", "3D_Bio_Example_Data", package = "NanoStringNCTools") rccs <- dir(datadir, pattern = "SKMEL.*\\.RCC$", full.names = TRUE) rlf <- file.path(datadir, "3D_SolidTumor_Sig.rlf") pheno <- file.path(datadir, "3D_SolidTumor_PhenoData.csv") solidTumor <- readNanoStringRccSet(rccs, rlfFile = rlf, phenoDataFile = pheno) # Assess experiment linearity #autoplot(solidTumor, "ercc-linearity") # Plot a feature's expression across all samples #autoplot(solidTumor, "boxplot-feature", index=2)