plotDiffHeatmap {CATALYST} | R Documentation |
Heatmaps summarizing differental abundance & differential state testing results.
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, col_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, col_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, col_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, col_anno = TRUE, ...)
x |
a |
y |
a |
... |
optional arguments. |
top_n |
numeric. Number of top clusters (if |
all |
logical. Specifies whether all clusters or cluster-marker combinations
should be displayed. If |
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 |
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? |
col_anno |
logical. Should column annotations for each factor
in |
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
)
a HeatmapList-class
object.
Lukas M Weber and Helena Lucia Crowell helena.crowell@uzh.ch
# 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)