Plot clusters from degPattern function output

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.

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

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.
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.

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)
#> Working with 100 genes.
#> Working with 71 genes after filtering: minc > 15
#> Joining, by = "merge"
#> Joining, by = "merge"
degPlotCluster(res$normalized, "group", "other")
degPlotCluster(res$normalized, "group", "other", lines = FALSE)

library(dplyr)
library(tidyr)
#> 
#> Attaching package: ‘tidyr’
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     expand
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)