degPlotCluster {DEGreport} | R Documentation

## Plot clusters from degPattern function output

### Description

This function helps to format the cluster plots from `degPatterns()`. It allows to control the layers and it returns a ggplot object that can accept more ggplot functions to allow customization.

### Usage

    degPlotCluster(
      table,
      time,
      color = NULL,
      min_genes = 10,
      process = FALSE,
      points = TRUE,
      boxes = TRUE,
      smooth = TRUE,
      lines = TRUE,
      facet = TRUE,
      cluster_column = "cluster",
      prefix_title = "Group: "
    )

### Arguments

`table` | `normalized` element from `degPatterns()` output. It can be a data.frame with the following columns in there: `genes, sample, expression, cluster, xaxis_column, color_column`.
`time` | column name to use in the x-axis.
`color` | column name to use to color and divide the samples.
`min_genes` | minimum number of genes to be added to the plot.
`process` | whether to process the table if it is not ready for plotting.
`points` | Add points to the plot.
`boxes` | Add boxplot to the plot.
`smooth` | Add regression line to the plot.
`lines` | Add gene lines to the plot.
`facet` | Split figures based on cluster ID.
`cluster_column` | column name if cluster is in a column with a different name. Usefull, to plot cluster with different cutoffs used when grouping genes from the clustering step.
`prefix_title` | text to add before the cluster ID in the figure title.

### Value

ggplot2 object.

### Examples

    data(humanGender)
    library(SummarizedExperiment)
    library(ggplot2)
    ma <- assays(humanGender)[[1]][1:100,]
    des <- colData(humanGender)
    des[["other"]] <- sample(c("a", "b"), 85, replace = TRUE)
    res <- degPatterns(ma, des, time="group", col = "other", plot = FALSE)
    degPlotCluster(res$normalized, "group", "other")
    degPlotCluster(res$normalized, "group", "other", lines = FALSE)

    library(dplyr)
    library(tidyr)
    library(tibble)
    table <- rownames_to_column(as.data.frame(ma), "genes") %>%
        gather("sample", "expression", -genes) %>%
        right_join(distinct(res$df[,c("genes", "cluster")]),
                   by = "genes") %>%
        left_join(rownames_to_column(as.data.frame(des), "sample"),
                  by = "sample") %>%
                  as.data.frame()
    degPlotCluster(table, "group", "other", process = TRUE)

[Package DEGreport version 1.28.0 Index]