Create heatmap for pairwise DEG analysis result

Usage

plotPairwiseDEGHeatmap(
  object,
  result,
  group = NULL,
  topN = 20,
  absLFCThresh = 1,
  padjThresh = 0.05,
  pctInThresh = 50,
  pctOutThresh = 50,
  downsampleSize = 200,
  useCellMeta = NULL,
  column_title = NULL,
  seed = 1,
  ...
)

Arguments

object

A liger object, with normalized data and metadata to annotate available.

result

The data.frame returned by runPairwiseDEG.

group

The test group name among the result to be shown. Must specify only one if multiple tests are available (i.e. split test). Default NULL works with single-test result and raises error with split-test result.

topN

Maximum number of top significant features to be plot for up- and down-regulated genes. Default 20.

absLFCThresh

Hard threshold on absolute logFC value. Default 1.

padjThresh

Hard threshold on adjusted P-value. Default 0.05.

pctInThresh, pctOutThresh

Threshold on expression percentage. These mean that a feature will only pass the filter if it is expressed in more than pctInThresh percent of cells in the corresponding cluster. Similarly for pctOutThresh. Only applied when these metrics are available. Default 50 percent for both.

downsampleSize

Maximum number of downsampled cells to be shown in the heatmap. The downsampling is balanced on the cells involved in the test specified. Default 200.

useCellMeta

Cell metadata variable names for cell grouping. Default NULL includes dataset source and the default cluster.

column_title

Title on the column. Default NULL.

seed

Random seed for reproducibility. Default 1.

...

Arguments passed on to .plotHeatmap

transpose: Logical, whether to "rotate" the heatmap by 90 degrees so that cell information is displayed by row. Default FALSE.
showCellLabel,showFeatureLabel: Logical, whether to show cell barcodes, gene symbols or factor names. Default TRUE for gene/factors but FALSE for cells.
cellAnnColList,featureAnnColList: List object, with each element a named vector of R-interpretable color code. The names of the list elements are used for matching the annotation variable names. The names of the colors in the vectors are used for matching the levels of a variable (factor object, categorical). Default NULL generates ggplot-flavor categorical colors.
scale: Logical, whether to take z-score to scale and center gene expression. Applied after dataScaleFunc. Default FALSE.
trim: Numeric vector of two values. Limit the z-score value into this range when scale = TRUE. Default c(-2, 2).
baseSize: One-parameter control of all text sizes. Individual text element sizes can be controlled by other size arguments. "Title" sizes are 2 points larger than "text" sizes when being controlled by this.
cellTextSize,featureTextSize,legendTextSize: Size of cell barcode labels, gene/factor labels, or legend values. Default NULL.
cellTitleSize,featureTitleSize,legendTitleSize: Size of titles of the cell slices, gene/factor slices, or the legends. Default NULL.
viridisOption,viridisDirection: See argument option and direction of viridis. Default "A" and -1.
RColorBrewerOption: When scale = TRUE, heatmap color will be mapped with brewer.pal. This is passed to name. Default "RdBu".

Value

A HeatmapList object.

Examples

defaultCluster(pbmc) <- pbmcPlot$leiden_cluster
#> ℹ Storing given cluster labels to `cellMeta(x)` field: "defaultCluster".
degTest <- runPairwiseDEG(
    pbmc,
    groupTest = "stim",
    groupCtrl = "ctrl",
    variable1 = "dataset",
    splitBy = "defaultCluster"
)
#> ℹ Running DEG within: "0"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "1"
#> ℹ Calling pairwise DESeq2 Wald test
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#>    function: y = a/x + b, and a local regression fit was automatically substituted.
#>    specify fitType='local' or 'mean' to avoid this message next time.
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "2"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "3"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "4"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "5"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "6"
#> ℹ Calling pairwise DESeq2 Wald test
#> ✔ Calling pairwise DESeq2 Wald test ... done
#> 
#> ℹ Running DEG within: "7"
#> ℹ Calling pairwise DESeq2 Wald test
#> ! Ignoring replicates (size in bracket) with too few cells: "others.ctrl.rep1 (2)"
#> ℹ Calling pairwise DESeq2 Wald test

#> ℹ Consider decrease minCellPerRep to exclude less replicates or/and lower nPsdRep to generate larger pseudo-replicates.
#> ℹ Calling pairwise DESeq2 Wald test

#> ✖ Error when computing on "7.stim": Too few replicates with more than 3 cells (`minCellPerRep`) for condition "others".
#> ℹ Calling pairwise DESeq2 Wald test

#> ! Empty result returned for this test.
#> ℹ Calling pairwise DESeq2 Wald test

#> ✖ Calling pairwise DESeq2 Wald test ... failed
#> 
pbmc <- normalize(pbmc)
#> ℹ Normalizing datasets "ctrl"
#> ℹ Normalizing datasets "stim"
#> ✔ Normalizing datasets "stim" ... done
#> 
#> ℹ Normalizing datasets "ctrl"

#> ✔ Normalizing datasets "ctrl" ... done
#> 
plotPairwiseDEGHeatmap(pbmc, degTest, '4.stim')
#> ℹ Subsetting dataset: "ctrl"
#> ℹ Subsetting dataset: "stim"
#> ✔ Subsetting dataset: "stim" ... done
#> 
#> ℹ Subsetting dataset: "ctrl"

#> ✔ Subsetting dataset: "ctrl" ... done
#>