update documentation with examples

R/met_propensity.R

@@ -6,6 +6,11 @@
 #' @param gene The gene on which patient's stratification is based.
 #' @return An object of class \code{'INCOMMON'} containing an additional object `survival`.
 #' @export
+#' @examples
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Estimate the metastatic propensity associated with mutant TP53 with vs without CNA in BRCA.
+#' MSK_classified = met_propensity(x = MSK_classified, tumor_type = 'BRCA', gene = 'TP53')
 #' @importFrom dplyr filter mutate rename select %>% as_tibble
 #' @importFrom survival Surv survfit
 #' @importFrom stats glm binomial confint

R/met_tropism.R

@@ -13,6 +13,12 @@
 #' @param gene The gene on which patient's stratification is based.
 #' @param metastatic_site The target organ of metastatic diffusion.
 #' @export
+#' @examples
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Estimate the metastatic propensity associated with mutant TP53 with vs without CNA in BRCA,
+#' with respect to the Liver.
+#' MSK_classified = met_tropism(x = MSK_classified, tumor_type = 'BRCA', gene = 'TP53', metastatic_site = 'Liver')
 #' @importFrom dplyr filter mutate rename select %>%
 #' @importFrom survival Surv survfit
 

R/plot_class_fraction.R

@@ -8,17 +8,10 @@
 #' @return An object or a list of class \code{'ggplot2'}.
 #' @export
 #' @examples
-#' # First load example data
-#' data(MSK_genomic_data)
-#' data(MSK_clinical_data)
-#' # Initialize the INCOMMON object (note the outputs to screen)
-#' x = init(genomic_data = MSK_genomic_data, clinical_data = MSK_clinical_data)
-#' # Run INCOMMON classification
-#' x = classify(x = x, priors = pcawg_priors, entropy_cutoff = NULL, rho = 0.01)
-#' # Use the genome interpreter to get intepreted classifications
-#' x = genome_interpreter(x = x)
-#' # Plot the fraction of class
-#' x = genome_interpreter(x = x)
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Plot class fraction for a specific gene and tumour type
+#' plot_class_fraction(x = MSK_classified, tumor_type = 'LUAD', gene = 'KRAS')
 #' @importFrom dplyr filter mutate rename select %>%
 #' @importFrom patchwork wrap_plots
 plot_class_fraction = function(x, tumor_type, gene, ...){

R/plot_classification.R

@@ -8,16 +8,10 @@
 #' @return An object or a list of objects of class \code{'ggplot2'}.
 #' @export
 #' @examples
-#' # First load example data
-#' data(MSK_genomic_data)
-#' data(MSK_clinical_data)
-#' # Initialize the INCOMMON object with a specific sample
-#' sample = 'P-0002081'
-#' x = init(genomic_data = MSK_genomic_data %>% filter(sample == !!sample), clinical_data = MSK_clinical_data %>% filter(sample == !!sample))
-#' # Run INCOMMON classification
-#' x = classify(x = x, priors = pcawg_priors, entropy_cutoff = NULL, rho = 0.01)
-#' # An S3 method can be used to report to screen what is in the object
-#' plot_classification(x = x, sample = sample)
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Plot classification results for a specific sample
+#' plot_classification(x = MSK_classified, sample = 'P-0002081')
 #' @importFrom dplyr filter mutate rename select %>%
 plot_classification = function(x, sample, assembly = F){
 

R/plot_met_volcano.R

@@ -6,6 +6,13 @@
 #' it pools from multiple tumour types into a pan-cancer visualisation.
 #' @return An object or a list of objects of class \code{'ggplot2'}.
 #' @export
+#' @examples
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Estimate the metastatic propensity associated with mutant TP53, PIK3CA and CDH1 with vs without CNA in BRCA.
+#' for(g in c('TP53', 'PIK3CA', 'CDH1')){MSK_classified = met_propensity(x = MSK_classified, tumor_type = 'BRCA', gene = g)}
+#' # Plot results in a volcano plot
+#' plot_met_volcano(x = MSK_classified, tumor_type = 'BRCA')
 #' @importFrom dplyr filter mutate rename select %>%
 #' @importFrom ggrepel geom_label_repel
 

R/plot_prior.R

@@ -6,6 +6,11 @@
 #' @param gene Gene for gene-specific prior.
 #' @return An object or a list of objects of class \code{'ggplot2'}.
 #' @export
+#' @examples
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Plot classification results for a specific sample
+#' plot_prior(x = MSK_classified, gene = 'TP53', tumor_type = 'PAAD')
 #' @importFrom dplyr filter mutate rename select %>%
 plot_prior = function(x, gene, tumor_type){
 

R/plot_tropism.R

@@ -5,18 +5,26 @@
 #' @param tumor_type The tumour type for which classified data is available.
 #' @return An object or a list of objects of class \code{'ggplot2'}.
 #' @export
+#' @examples
+#' # First load example classified data
+#' data(MSK_classified)
+#' # Estimate the metastatic tropism associated with mutant TP53, PIK3CA and CDH1 with vs without CNA,
+#' # in BRCA, with respect to Liver and Lymph
+#' for(g in c('TP53', 'PIK3CA', 'CDH1')){for(m in c('Liver', 'Lymph')){MSK_classified = met_tropism(x = MSK_classified, gene = g, tumor_type = 'BRCA', metastatic_site = m) }
+#' # Plot results in a volcano plot
+#' plot_tropism(x = MSK_classified, tumor_type = 'BRCA')
 #' @importFrom dplyr filter mutate rename select %>%
 #' @importFrom ggrepel geom_label_repel
 
 plot_tropism = function(x, tumor_type){
   stopifnot(inherits(x, 'INCOMMON'))
   stopifnot('metastatic_tropism' %in% names(x))
-  
+
   stopifnot(tumor_type %in% names(x$metastatic_tropism))
   toplot = x$metastatic_tropism[[tumor_type]] %>%
     unlist(recursive = F) %>%
     do.call(rbind, .)
-  
+
   toplot = toplot %>%
     dplyr::mutate(
       prevalent = dplyr::case_when(
@@ -27,10 +35,10 @@ plot_tropism = function(x, tumor_type){
         TRUE ~ 'ns'
       )
     )
-  
+
   toplot %>%
     dplyr::filter(!is.na(low),
-                  !is.na(up)) %>% 
+                  !is.na(up)) %>%
     ggplot2::ggplot(ggplot2::aes(
       y = gene, x = log2(OR))) +
     ggplot2::geom_vline(xintercept = 0, linetype = 'longdash', color = 'indianred')+