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Showing content from https://github.com/DadongZ/OncoSubtype below:

GitHub - DadongZ/OncoSubtype

Provide functionality for cancer subtyping using existing published methods or machine learning based on TCGA data.

Currently support mRNA subtyping:

• LUSC using nearest centroids method or random forest method by training TCGA data.
• LUAD using nearest centroids method or random forest method by training TCGA data.
• HNSC using nearest centroids method or random forest method by training TCGA data.
• BLCA using random forest (rf) method by training TCGA data.
• ESCA using random forest (rf) method by training TCGA data.
• ESCC using random forest (rf) method by training TCGA data.
• BRCA using PAM50 method based on R package genefu.
• STAD using random forest (rf) method by training TCGA data.

1.0.0

You can install the released version through:

install.packages("OncoSubtype")

This is a basic example for predicting the subtypes for Lung Squamous Cell Carcinoma (LUSC).

library(OncoSubtype)
library(tidyverse)
data <- get_median_centered(example_fpkm)
data <- assays(data)$centered
rownames(data) <- rowData(example_fpkm)$external_gene_name
# use default wilkerson's method
output1 <- centroids_subtype(data, disease = 'LUSC')
table(output1@subtypes)
#> 
#>     basal classical primitive secretory 
#>        44        65        26        44

output2 <- ml_subtype(data, disease = 'LUSC', method = 'rf', seed = 123)
table(output2@subtypes)
#> 
#>     basal classical primitive secretory 
#>        43        65        27        44

confusionMatrix(as.factor(tolower(output1@subtypes)),
                as.factor(tolower(output2@subtypes)))
#> Confusion Matrix and Statistics
#> 
#>            Reference
#> Prediction  basal classical primitive secretory
#>   basal        43         1         0         0
#>   classical     0        64         1         0
#>   primitive     0         0        26         0
#>   secretory     0         0         0        44
#> 
#> Overall Statistics
#>                                           
#>                Accuracy : 0.9888          
#>                  95% CI : (0.9602, 0.9986)
#>     No Information Rate : 0.3631          
#>     P-Value [Acc > NIR] : < 2.2e-16       
#>                                           
#>                   Kappa : 0.9846          
#>                                           
#>  Mcnemar's Test P-Value : NA              
#> 
#> Statistics by Class:
#> 
#>                      Class: basal Class: classical Class: primitive
#> Sensitivity                1.0000           0.9846           0.9630
#> Specificity                0.9926           0.9912           1.0000
#> Pos Pred Value             0.9773           0.9846           1.0000
#> Neg Pred Value             1.0000           0.9912           0.9935
#> Prevalence                 0.2402           0.3631           0.1508
#> Detection Rate             0.2402           0.3575           0.1453
#> Detection Prevalence       0.2458           0.3631           0.1453
#> Balanced Accuracy          0.9963           0.9879           0.9815
#>                      Class: secretory
#> Sensitivity                    1.0000
#> Specificity                    1.0000
#> Pos Pred Value                 1.0000
#> Neg Pred Value                 1.0000
#> Prevalence                     0.2458
#> Detection Rate                 0.2458
#> Detection Prevalence           0.2458
#> Balanced Accuracy              1.0000

vi <- varImp(output2@method, scale = TRUE)
plot(vi, top = 20) 

PlotHeat(object = output2, set = 'both', fontsize = 10,
        show_rownames = FALSE, show_colnames = FALSE)

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