knitr::opts_chunk$set(comment = "#>", collapse = TRUE)
Gvmap provide several clustering methods to calculate distance between samples and genes. You can
input it through a YAML format file with parameter distfun
or hclustfun
. Or you can modify it through
function gvmapModAttr
.
The basic paramters are as follow:
[param] distfun
character, function used to compute the distance (dissimilarity) between both rows
and columns. Defaults to dist. Also, there are other several parameters in distfun, such as "euclidean",
"maximum", "manhattan", "canberra", "binary" or "minkowski". You can also have a
user-defined function by inputing a function parameter through gvmapModAttr()
.
[param] hclustfun
character, function used to compute the hierarchical clustering when Rowv or
Colv are not dendrograms. Defaults to hclust. Also, there are other several parameters in hclustfun,
such as ward.D, ward.D2, single, complete, average, mcquitty, median, centroid. You can also have a
user-defined function by inputing a function parameter through gvmapModAttr()
.
# prepare heatmap data heatmap_file <- system.file("extdata", "heatmap.txt", package = "gvmap") heatmap_data_1 <- read.table(heatmap_file, header = T) rownames(heatmap_data_1) <- heatmap_data_1$X heatmap_data_1 <- heatmap_data_1[, -1] head(heatmap_data_1) heatmap_data_2 <- heatmap_data_1[20:50, ] heatmap_data_3 <- heatmap_data_1[80:100, ] # if you have multiple heatmaps, the heatmap data must be a list # heatmap_1 is the main heatmap heatmap_data_mtp <- list(heatmap_1 = heatmap_data_1, heatmap_2 = heatmap_data_2, heatmap_3 = heatmap_data_3) # prepare legend data legend_file <- system.file("extdata", "legend.txt", package = "gvmap") legend_data <- read.table(legend_file, header = T) row.names(legend_data) <- legend_data$SampleName legend_data
library(easySVG) library(configr) library(gvmap) # read config file config_file <- system.file('extdata', 'config.mtp.yaml', package = 'gvmap') config_file <- gvmapConfig(config_file = config_file) output_svg_name <- paste0(tempdir(), "/o7.svg") gvmap(legend_data = legend_data, heatmap_data = heatmap_data_mtp, config_file = config_file, output_svg_name = output_svg_name, plot_width = 800, plot_height = 1200)
# using a different cluster method for dist config_file <- gvmapModAttr(attr_name = "distfun", value = "manhattan", target = "heatmap_1", config_file = config_file) output_svg_name <- paste0(tempdir(), "/o8.svg") gvmap(legend_data = legend_data, heatmap_data = heatmap_data_mtp, config_file = config_file, output_svg_name = output_svg_name, plot_width = 800, plot_height = 1200)
# using a different cluster method for dist config_file <- gvmapModAttr(attr_name = "hclustfun", value = "ward.D", target = "heatmap_1", config_file = config_file) output_svg_name <- paste0(tempdir(), "/o9.svg") gvmap(legend_data = legend_data, heatmap_data = heatmap_data_mtp, config_file = config_file, output_svg_name = output_svg_name, plot_width = 800, plot_height = 1200)
sessionInfo()
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