ogcc: O-glycan Cancer Classifier (OGCC)
In aymanabuelela/ogcc: O-glycan Cancer Classifier (OGCC)

Description Usage Arguments Details Value Author(s) See Also

ogcc performs a group of cancer class predictions based on the expression of O-glycan-forming glycosyltransferase genes.

1	ogcc(x, model = "types", d = "RSEM", output = "raw")

`x`	a numeric data frame or matrix of expression data where columns are the features and rows are cases/samples. The set of features must contain the minimum set of features for the specified model. See 'Details'.
`model`	a character string indicating the model to perform the prediction task. It must be one of the following: "normal_tumor", "normal_tumor_reduced", "normal_tumor_types", "normal_tumor_types_reduced", "types" and "types_reduced".
`d`	a string indicating the RNA-Seq measurement type; either 'RSEM', 'RPKM' or 'TPM'. 'RSEM' by default.
`output`	a character value indicating the form of prediction output of the model. It must be either "raw" or "probs".

ogcc classifies cancer samples according to one of a set of predefined classification models using O-glycan-forming genes (OGFGs).

The minimum set of OGFGs required in any model can be displayed using getMSF function. The required feature for a specific model may differ according to the feature selection step of that model.

To display the output labels the model was trained on and can predict, use the function getLabels. These models are not generic models. They were developed and trained on specific set of cancer classes. Going beyond the classes specified for each model may result in misclassification problems.

ogcc can perform the following classification tasks based on OGFGTs expression profile:

model normal_tumor: predicts whether a sample is normal or cancer based on the expression profile of the OGFGs.
model types: predicts the cancer type.

a character vector in case of output = "raw" or a dataframe of class probalibilities in case of output = "probs"

Ayman Abuelela; ayman.elkhodiery@kaust.edu.sa

getMSF and getLabels

aymanabuelela/ogcc documentation built on May 11, 2019, 4:15 p.m.