create_PCA_deconvolution: create_PCA_deconvolution

In RaikOtto/artdeco: Alikeness Based on Regression on Transciptome DECOnvolution

Description

create_PCA_deconvolution visualizes the differentiation stage predictions as PCA. Please note that the rows of the expression data matrix are expected to be HGNC symbols and columns are expected to contain the samples.

Usage

create_PCA_deconvolution(
   visualization_data,
   deconvolution_results,
   aggregate_differentiated_stages,
   confidence_threshold,
   Graphics_parameters,
   high_threshold,
   low_threshold,
   utilize_sadanandam_genes
)

Arguments

visualization_data	Transcriptome data that shall be visualized. Notice the convention that the row names have to contain the HGNC identifier.
deconvolution_results	The dataframe returned by the deconvolution analysis.
aggregate_differentiated_stages	Whether to aggregate the deconvolution results. Default vale FALSE.
confidence_threshold	Threshold at which to discard a prediction. Default value 1.1
Graphics_parameters	Pheatmap visualization paramters. You can customize visualization colors. Read the vignette for more information.
high_threshold	Threshold at which to define a deconvolution results as 'high'. Default value 101.
low_threshold	Threshold at which to define a deconvolution results as 'low'. Default value 0.
utilize_sadanandam_genes	Whether to utilize the same genes as the Sadanandam et al publication which have been utilize to create the DECO manuscript visualizations. Default value FALSE.

Value

PCA plot

Examples

data(deconvolution_results, envir = environment())
data(visualization_data, envir = environment())

create_PCA_deconvolution(
    visualization_data = visualization_data,
    deconvolution_results = deconvolution_results,
    Graphics_parameters = "",
    utilize_sadanandam_genes = TRUE
)

RaikOtto/artdeco documentation built on Nov. 3, 2021, 6:18 p.m.