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Plot a heatmap

plot_heatmap.Rd

plot_heatmap generates a heatmap of all or selected features for DEP or DEG data.

Usage

# S4 method for SummarizedExperiment
plot_heatmap(
  object,
  type = c("centered", "contrast"),
  manual_contrast = NULL,
  kmeans = FALSE,
  k = 6,
  color = c("RdBu", "RdYlBu", "RdYlGn", "BrBG", "PiYG", "PRGn", "PuOr", "RdGy",
    "Spectral"),
  col_limit = 6,
  indicate = NULL,
  row_font_size = 6,
  col_font_size = 10,
  clustering_distance = c("euclidean", "maximum", "manhattan", "canberra", "binary",
    "minkowski", "pearson", "spearman", "kendall", "gower"),
  split_order = NULL,
  label_few_peptide_rows = FALSE,
  chooseToshow = NULL,
  plot = TRUE,
  seed = 42,
  ...
)

# S4 method for DEGdata
plot_heatmap(
  object,
  type = c("centered", "contrast"),
  manual_contrast = NULL,
  kmeans = FALSE,
  k = 6,
  color = c("RdBu", "RdYlBu", "RdYlGn", "BrBG", "PiYG", "PRGn", "PuOr", "RdGy",
    "Spectral"),
  col_limit = 6,
  indicate = NULL,
  row_font_size = 6,
  col_font_size = 10,
  clustering_distance = c("euclidean", "maximum", "manhattan", "canberra", "binary",
    "minkowski", "pearson", "spearman", "kendall", "gower"),
  split_order = NULL,
  label_few_peptide_rows = FALSE,
  chooseToshow = NULL,
  plot = TRUE,
  seed = 42,
  ...
)

Arguments

object

SummarizedExperiment or DEGDdata object for which differentially enriched proteins are annotated (output from test_diff() and add_rejections()).

type

type 'contrast' or 'centered', The type of data scaling used for plotting. Either the fold change ('contrast') or the centered log2-intensity ('centered').

manual_contrast

Character. Only plot significant proteins in certain contrasts.

kmeans

Logical(1), Whether or not to perform k-means clustering.

k

Integer(1), Sets the number of k-means clusters, k must less than row of heatmap

color

Character(1), sets the color panel (from RColorBrewer). It can be a palette name in "RdBu", "RdYlBu", "RdYlGn", "BrBG", "PiYG", "PRGn", "PuOr", "RdGy", "Spectral".

col_limit

Integer(1), Sets the outer limits of the color scale.

indicate

Character, Sets additional annotation on the top of the heatmap based on columns from the experimental design (colData). Only applicable to type = 'centered'.

row_font_size

Integer(1), Font size of row name

col_font_size

Integer(1), Font size of columns label.

clustering_distance

Character(1)."euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman", "kendall" or "gower", Or a Function used to calculate clustering distance (for proteins and samples). Based on Heatmap and daisy.

split_order

a integer vector in 1:k. The order of cluster slices in the heatmap. If NULL, slices order is based clustering distance. Work if Kmeans = TRUE.

label_few_peptide_rows

Logic(1) If TRUE, label proteins with few peptide by color. Proteins with only one peptide show a purple name in heatmap, with two peptide show a blue name.

chooseToshow

Character vector of the chosen proteins/peptides names. If is not null, only plot the chosen proteins/peptides in it

plot

Logical(1) If TRUE (default) the heatmap is produced. Otherwise (if FALSE), the data which the heatmap is based on are returned.

seed

Integer(1), the random seed that effect kmeans cluster in heatmap. Preset a seed could a repeatable cluster result.

...

Other parameters to Heatmap.

Value

return a heatmap (generated by Heatmap) or a data.frame if plot = F.

Examples


# Load example
data(Silicosis_pg)
data <- Silicosis_pg
data_unique <- make_unique(data, "Gene.names", "Protein.IDs", delim = ";")

# Differential test
ecols <- grep("LFQ.", colnames(data_unique))
se <- make_se_parse(data_unique, ecols,mode = "delim")
filt <- filter_se(se, thr = 0, fraction = 0.4, filter_formula = ~ Reverse != "+" & Potential.contaminant!="+")
#> filter base on missing number is <= 0 in at least one condition.
#> filter base on missing number fraction < 0.4 in each row
#> filter base on giving formula 
norm <- normalize_vsn(filt)
#> vsn2: 8762 x 20 matrix (1 stratum). 
#> Please use 'meanSdPlot' to verify the fit.
imputed <- impute(norm, fun = "MinProb", q = 0.05)
#> Imputing along margin 2 (samples/columns).
#> [1] 0.3026531
diff <- test_diff(imputed, type = "manual", control  = c("PBS"), fdr.type = "Storey's qvalue")
#> Error in test_diff(imputed, type = "manual", control = c("PBS"), fdr.type = "Storey's qvalue"): run test_diff(type = 'manual') with a 'test' argument
dep <- add_rejections(diff, alpha = 0.01,lfc = 2)
#> Error in (function (classes, fdef, mtable) {    methods <- .findInheritedMethods(classes, fdef, mtable)    if (length(methods) == 1L)         return(methods[[1L]])    else if (length(methods) == 0L) {        cnames <- paste0("\"", vapply(classes, as.character,             ""), "\"", collapse = ", ")        stop(gettextf("unable to find an inherited method for function %s for signature %s",             sQuote(fdef@generic), sQuote(cnames)), domain = NA)    }    else stop("Internal error in finding inherited methods; didn't return a unique method",         domain = NA)})(list(structure("standardGeneric", package = "methods")), new("nonstandardGenericFunction",     .Data = function (diff, alpha = 0.05, lfc = 1, thresholdmethod = "intersect",         curvature = 1, x0_fold = 2, ...)     {        standardGeneric("add_rejections")    }, generic = structure("add_rejections", package = "DEP2"),     package = "DEP2", group = list(), valueClass = character(0),     signature = c("diff", "alpha", "lfc", "thresholdmethod",     "curvature", "x0_fold"), default = NULL, skeleton = (function (diff,         alpha = 0.05, lfc = 1, thresholdmethod = "intersect",         curvature = 1, x0_fold = 2, ...)     stop(gettextf("invalid call in method dispatch to '%s' (no default method)",         "add_rejections"), domain = NA))(diff, alpha, lfc, thresholdmethod,         curvature, x0_fold, ...)), <environment>): unable to find an inherited method for function ‘add_rejections’ for signature ‘"standardGeneric"’

# Heatmap
plot_heatmap(dep)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plot_heatmap': object 'dep' not found