Description Usage Arguments Value Author(s) Examples
Get the AUC-ROC, AUC-PR, and ROC/PR curves for plotting.
1 | get_classification_accuracy(sample_scores, positive_val)
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sample_scores |
This is a data.frame containing the sample id, score, and true label Y. This object is returned by the method get_gene_weights. |
positive_val |
This is the value that will denote a true positive. It must be one of the two values in the Y column in sample_scores. |
This returns a list of performance metrics
auc_pr |
Area under the PR-curve |
auc_roc |
Area under the ROC-curve |
perf_pr |
ROCR object for plotting the PR-curve |
perf_roc |
ROCR object for plotting the ROC-curve |
Natalie R. Davidson
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | data(tcga_expr_df)
# transform from data.frame to SummarizedExperiment
tcga_se <- SummarizedExperiment(t(tcga_expr_df[ , -(1:4)]),
colData=tcga_expr_df[ , 2:4])
colnames(tcga_se) <- tcga_expr_df$tcga_id
colData(tcga_se)$sample_id <- tcga_expr_df$tcga_id
hypoxia_gene_ids <- get_hypoxia_genes()
hypoxia_gene_ids <- intersect(hypoxia_gene_ids, rownames(tcga_se))
colData(tcga_se)$Y <- ifelse(colData(tcga_se)$is_normal, 0, 1)
# now we can get the gene weightings
res <- get_gene_weights(tcga_se, hypoxia_gene_ids, unidirectional=TRUE)
sample_scores <- res[[2]]
# check how well we did
training_res <- get_classification_accuracy(sample_scores, positive_val=1)
print(training_res[[2]])
plot(training_res[[3]], col="orange", ylim=c(0, 1))
legend(0.1,0.8,c(training_res$auc_pr,"\n"), border="white", cex=1.7,
box.col = "white")
plot(training_res[[4]], col="blue", ylim=c(0, 1))
legend(0.1,0.8,c(training_res$auc_roc,"\n"),border="white",cex=1.7,
box.col = "white")
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