plotDiffHeatmap {CATALYST} | R Documentation

## Plot differential heatmap

### Description

Heatmaps summarizing differental abundance & differential state testing results.

### Usage

    plotDiffHeatmap(x, y, ...)

    ## S4 method for signature 'matrix,SummarizedExperiment'
    plotDiffHeatmap(x, y, top_n = 20,
      all = FALSE, order = TRUE, th = 0.1, hm1 = TRUE,
      normalize = TRUE, row_anno = TRUE, ...)

    ## S4 method for signature 'daFrame,SummarizedExperiment'
    plotDiffHeatmap(x, y,
      top_n = 20, all = FALSE, order = TRUE, th = 0.1, hm1 = TRUE,
      normalize = TRUE, row_anno = TRUE, ...)

    ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment'
    plotDiffHeatmap(x, y,
      top_n = 20, all = FALSE, order = TRUE, th = 0.1, hm1 = TRUE,
      normalize = TRUE, row_anno = TRUE, ...)

    ## S4 method for signature 'ANY,list'
    plotDiffHeatmap(x, y, top_n = 20, all = FALSE,
      order = TRUE, th = 0.1, hm1 = TRUE, normalize = TRUE,
      row_anno = TRUE, ...)

### Arguments

`x` | a `daFrame` or `SummarizedExperiment`.
`y` | a `SummarizedExperiment` containing differential testing results as returned by one of `testDA_edgeR`, `testDA_voom`, `testDA_GLMM`, `testDS_limma`, or `testDS_LMM`. Alternatively, a list as returned by `diffcyt`.
`...` | optional arguments.
`top_n` | numeric. Number of top clusters (if `type = "DA"`) or cluster-marker combinations (if `type = "DS"`) to display.
`all` | logical. Specifies whether all clusters or cluster-marker combinations should be displayed. If `TRUE`, `top_n` will be ignored.
`order` | logical. Should results be ordered by significance?
`th` | numeric. Threshold on adjusted p-values below which clusters (DA) or cluster-marker combinations (DS) should be considered significant.
`hm1` | logical. Specifies whether the left-hand side heatmap should be plotted.
`normalize` | logical. Specifies whether Z-score normalized values should be plotted in the right-hand side heatmap. If `y` contains DA analysis results, relative population abundances will be arcsine-square-root scaled prior to normalization.
`row_anno` | logical. Should a row annotation indicating whether cluster (DA) or cluster-marker combinations (DS) are significant, as well as adjusted p-values be included?

### Details

For DA tests, `plotDiffHeatmap` will display

- median (arcsinh-transformed) cell-type marker expressions (across all samples)

- cluster abundances by samples

- row annotations indicating if detected clusteres are significant (i.e. adj. p-value >= `th`)

For DS tests, `plotDiffHeatmap` will display

- median (arcsinh-transformed) cell-type marker expressions (across all samples)

- median (arcsinh-transformed) cell-state marker expressions by sample

- row annotations indicating if detected cluster-marker combinations are significant (i.e. adj. p-value >= `th`)

### Value

a `HeatmapList-class` object.

### Author(s)

Lukas M Weber and Helena Lucia Crowell helena.crowell@uzh.ch

### Examples

    # construct daFrame
    data(PBMC_fs, PBMC_panel, PBMC_md)
    re <- daFrame(PBMC_fs, PBMC_panel, PBMC_md)

    # run clustering
    re <- cluster(re)

    ## differential analysis
    library(diffcyt)

    # create design & constrast matrix
    design <- createDesignMatrix(PBMC_md, cols_design=3:4)
    contrast <- createContrast(c(0, 1, 0, 0, 0))

    # test for
    # - differential abundance (DA) of clusters
    # - differential states (DS) within clusters
    da <- diffcyt(re, design = design, contrast = contrast,
        analysis_type = "DA", method_DA = "diffcyt-DA-edgeR",
        clustering_to_use = "meta20")
    ds <- diffcyt(re, design = design, contrast = contrast,
        analysis_type = "DS", method_DS = "diffcyt-DS-limma",
        clustering_to_use = "meta20")

    # display test results for
    # - top DA clusters
    # - top DS cluster-marker combintations
    plotDiffHeatmap(re, da)
    plotDiffHeatmap(re, ds)

[Package CATALYST version 1.6.7 Index]