getResults_OC.RdThis functions let's the user study the output of the OncoCast function. This function takes as input one of the (or the one) objects returned from the different machine learning algorithms chosen previously. Only one such object can be inputted at a time in the getResults_OC function.
getResults_OC( OC_object, data, cuts = NULL, geneList = NULL, mut.data = F, plotQuant = 1, plot.cuts = T, timeType = "Months", ... )
| OC_object | A list object outputed by the OncoCast function. |
|---|---|
| data | A dataframe that corresponds to the data used to generate the OncoCast output. |
| cuts | Numeric vector of the points in the distribution of risk scores where groups will be splitted. Needs to be of length numGroups - 1. eg : c(0.25,0.5,0.75) when numgroups is 4. Default is 0.5. |
| geneList | Optional character vector of features of potential higher interest. Default is NULL, which leads to using the 5 most frequently selected features. |
| mut.data | Boolean argument indicating if the user is using mutation predictors (binary data). Default is FALSE. |
| plotQuant | A numeric entry between 0-1 that defines what proportion of patients will be represented on the Kaplan-Meier plot. Particularly useful when a lot of patients with long survival are censored. Default is 1 (all patients are plotted). |
| plot.cuts | Boolean specifying the cuts made in the risk score should be plotted on the risk histogram |
| timeType | Character value to be printed on the kaplan meier representing the time unit used |
| ... | Futher arguments |
CPE Summary of the distribution of the concordance probability estimate (see phcpe function) accross all runs. (Recommended)
CI Summary of the distribution of the concordance index accross all runs. (Depreciated)
inflPlot Bar plot of frequency of the 20 most selected features.
topHits Character vector of the top 10 most selected features.
average.risk Average predicted risk score for each patient in the data set.
RiskScore The average.risk output rescaled between 0-10.
data.out The data that was used for the analysis.
selectInflPlot Volcano plot of the selection frequency against the average mean coefficient of each feature accross all runs. Note that this plot is interactive.
RiskRefit Refitted cox proportional hazard model with the predicted average risk score as the continuous covariate.
RiskHistogram Histogram of the density distribution of the average predicted risk score. Note it has been rescaled from 0 to 10 for simplicity.
Fits Data frame reporting the coefficients found for each feature at each run.
time.type Time unit used. Options are Days or Months.
RiskScoreSummary Distribution summary of the average predicted risk score.
KM_Plot Kaplan-Meier plot stratified by risk group.
SurvSum Summary of survival metrics per risk group.
RiskGroup The risk group assigned to each patients (ordered in the order of the input dataset).
mut_Plot Mutation distribution by features bar plot.
PieChart Interactive pie chart of the mutation dsitribution using either the most frequently selected features or the manually inputted gene list. Each of the pies represent one of the risk groups.
GenesUsed Character vector of the features used to make the pie charts.
library(OncoCast) test <- OncoCast(data=survData[1:100,],formula = Surv(time,status)~., method = "LASSO",runs = 30, save = FALSE,nonPenCol = NULL,cores =1)#> Warning: We do not recommend using a number of cross-validation/bootstraps lower than 50.#> [1] "Data check performed, ready for analysis." #> [1] "LASSO SELECTED"results <- getResults_OC(OC_object=test$LASSO,data=survData[1:100,], cuts=c(0.2,0.4,0.6,0.8), geneList=NULL,mut.data=TRUE)#> Warning: Vectorized input to `element_text()` is not officially supported. #> Results may be unexpected or may change in future versions of ggplot2.#>