# Plot clusters from degPattern function output

`degPlotCluster.Rd`

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’:
    #>
    #>     expandlibrary(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)