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

Description Usage Arguments Value Examples

create_heatmap_deconvolution visualizes the deconvolution results as heatmap. Please note that the rows of the expression data matrix are expected to be HGNC symbols and columns are expected to contain the samples.

create_heatmap_deconvolution(
    visualization_data,
    deconvolution_results,
    aggregate_differentiated_stages,
    confidence_threshold,
    show_colnames,
    Graphics_parameters,
    high_threshold,
    low_threshold,
    utilize_sadanandam_genes
)

`visualization_data`	Matrix of the 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`	Show the differentiation stage similarities aggregated over all differentiated stages - alpha, beta, gamma, delta, accinar and ductal - or specific for each differentiation stage. Default value FALSE, alternative value TRUE.
`confidence_threshold`	Threshold above which deconvolutions are deemed unsuccessful and corresponding results being masked on the the plots. Default value 1.1.
`show_colnames`	Whether to show the sample column names. Default value FALSE.
`Graphics_parameters`	Pheatmap visualization paramters. You can customize visualization colors. Read the vignette for more information.
`high_threshold`	Threshold depending on which a deconvolution result is interpreted as 'high'. If not set, a statistical estimation will approximately identify a signficance threshold for a high similarity.
`low_threshold`	Threshold depending on which a deconvolution result is interpreted as 'low'. If not set, a statistical estimation will approximately identify a signficance threshold for a low similarity.
`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.

Plots

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

create_heatmap_deconvolution(
    visualization_data = visualization_data,
    deconvolution_results = deconvolution_results,
    aggregate_differentiated_stages = FALSE,
    confidence_threshold = 1.1,
    show_colnames = FALSE,
    Graphics_parameters = "",
    high_threshold = 66,
    low_threshold = 33,
    utilize_sadanandam_genes = FALSE
)