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Commits (2)
......@@ -489,27 +489,32 @@ distMatrix %>% d3heatmap(xaxis_height = 1, color = col)
#' This allows to see general trends, for example if one sample or replicate is really different compared to the others.
#' Additionally, the clustering of samples (columns) can indicate closer related samples and clustering of proteins (rows) indicates similarly behaving proteins. The proteins can be clustered by k-means clustering (_kmeans_ argument) and the number of clusters can be defined by argument _k_.
if(n_hits >= 6){
#' A second heatmap shows the contrasts, i.e. the direct sample comparisons, as columns. Here, this emphasises the enrichment of proteins compared to the control sample.
if(n_hits >= 0){
plot_heatmap(
dep,
column_title = "Protein Expression in samples",
type = "centered",
col_limit = 4,
show_row_names = FALSE,
indicate = c("condition")
show_row_names = TRUE,
indicate = c("condition"),
row_font_size = 10,
show_row_dend = FALSE
)
#' The heatmap shows a clustering of replicates.
#' Alternatively, a heatmap can be plotted using the contrasts, i.e. the direct sample comparisons, as columns. Here, this emphasises the enrichment of ubiquitin interactors compared to the control sample.
# Plot a heatmap of all significant proteins (rows) and the tested contrasts (columns)
plot_heatmap(dep,
plot_heatmap(
dep,
column_title = "Fold-changes in comparisons",
type = "contrast",
col_limit = 10,
show_row_names = FALSE,
indicate = c("condition")
show_row_names = TRUE,
row_font_size = 10,
show_row_dend = FALSE
)
} else {
print("No plot was generated because fewer than 6 differentially abundant proteins were found")
print("No plot was generated because no differentially abundant proteins were found")
}
#' ## Volcano plots of all contrasts
......