fix docs

R/circular_CN_plot.R

@@ -3,9 +3,17 @@
 #'
 #' @param pretty_CN_heatmap_output 
 #' @param ideogram 
-#' @param labelTheseGenes 
+#' @param track_height Change this to increase/decrease the height of the tracks. (0.1)
+#' @param labelTheseGenes Specify a vector of gene names to label in the plot
+#' @param del_col Optionally specify a different colour to use for the CNV deletion track
+#' @param gain_col Optionally specify a different colour to use for the CNV gain track
+#' @param calculate_correlations Experimental! Calculate the correlation between CNVs between different chromosomes and link highly correlated regions
+#' @param min_correlation Minimum correlation to consider when plotting links
+#' @param max_neg_correlation Maximum negative value for correlations <1 to consider when plotting links
+#' @param link_transparency Specify a different alpha to increase or decrease the transparency of links
+#' 
 #'
-#' @return
+#' @return Nothing
 #' @export
 #'
 #' @examples
@@ -22,7 +30,6 @@ circular_CN_plot = function(pretty_CN_heatmap_output,
                             del_col="#0000FF80",
                             gain_col="#FF000080",
                             calculate_correlations = FALSE,
-                            return_correlations = FALSE,
                             link_transparency=0.8,
                             labelTheseGenes = c("CD58","TLR2",
                                                 "MCL1","CDKN2A",
@@ -134,9 +141,6 @@ circular_CN_plot = function(pretty_CN_heatmap_output,
 
 
 
-  #return(list(bed1,bed2,correlations))
-  #Heatmap(correlations,row_split=bin_chroms,
-  #        column_split = bin_chroms,cluster_rows=F,cluster_columns=F,show_row_names = F,show_column_names = F)
   chroms_u = unique(bin_chroms)
   xlims = matrix(ncol=2,nrow=length(chroms_u))
   rownames(xlims)=chroms_u
@@ -294,12 +298,23 @@ circular_CN_plot = function(pretty_CN_heatmap_output,
 
 #' Categorize arm-level and chromosomal CNV events
 #'
-#' @param pretty_CN_heatmap_output 
+#' @param pretty_CN_heatmap_output The output from running the pretty_CN_heatmap function
 #'
-#' @return
+#' @return List of data frames
 #' @export
 #'
 #' @examples
+#' 
+#' cn_out = pretty_CN_heatmap(cn_state_matrix=all_states_binned,
+#'     scale_by_sample = T,
+#'     these_samples_metadata = all_genome_meta,
+#'     metadataColumns = c("pathology","seq_type"),
+#'     return_data = T)
+#'     
+#' aneuploidies = categorize_CN_events(cn_out)
+#' 
+#' 
+#' 
 categorize_CN_events = function(pretty_CN_heatmap_output){
   CN_mat = pretty_CN_heatmap_output$data
   labels = pretty_CN_heatmap_output$labels

R/pretty_CN_heatmap.R

@@ -17,7 +17,7 @@
 #' @param cluster_columns Set to TRUE to enable clustering of genomic regions (columns) based on their CN value across all patients in the heatmap
 #' @param cluster_rows Set to TRUE to enable clustering of genomic regions (columns) based on their CN value across all regions in the heatmap
 #' @param sortByBins Optional: A vector containing one or more names of genomic bins that will be used to order the heatmap rows.
-#' @param sortByPGA
+#' @param sortByPGA Optional: Sort the rows based on percent genome altered (PGA) instead of the other options
 #' @param splitByBinState Optional: A single genomic bin that will be used to split the heatmap based on the CN state of that bin
 #' @param sortByMetadataColumns A vector containing one or more names of columns from your metadata that will be used to order the rows overall or within slices (if combined with splitByBinState or geneBoxPlot)
 #' @param labelTheseGenes A vector of Hugo gene symbols whose location will be indicated on the top of the heatmap

R/pretty_circular_mutation_frequency_heatmap.R

@@ -1,11 +1,13 @@
 #' pretty_circular_mutation_frequency_heatmap
 #'
-#' @param prettyOncoplot_output
-#' @param genes
-#' @param keep_these_pathologies
-#' @param min_sample_num
+#' @param prettyOncoplot_output The output of the prettyOncoplot function
+#' @param cn_status_matrix The output of get_cn_states
+#' @param collated_results A list of data frames with sample_id as rownames and features as column names
+#' @param genes A vector of genes to label
+#' @param keep_these_pathologies A vector of pathology values to show in the plot. All the remaining rows will be ignored.
+#' @param min_sample_num Minimum number of samples in a pathology to be considered for the plot. Pathologies with less than this number will be excluded. (20)
 #'
-#' @return
+#' @return Nothing or a list of data frames (when return_data = TRUE)
 #' @export
 #'
 #' @examples