dot-OriginalModel_Retraining: TB gene signatures that require retraining.
In dfjenkins3/TBSignatureProfiler: Profile RNA-Seq Data Using TB Pathway Signatures

.OriginalModel_Retraining

R Documentation

TB gene signatures that require retraining.

Description

A function to obtain predicted score for TB gene signatures that need retraining of original models.

Usage

.OriginalModel_Retraining(input, useAssay, geneSignaturesName, adj, BPPARAM)

Arguments

`input`	A SummarizedExperiment object with gene symbols as the assay row names.
`useAssay`	A character string or an integer specifying the assay in the `input`. Default is the first assay in the assay list. Used for the test SummarizedExperiment object. Default is `1`, indicating the first assay in the `input`.
`geneSignaturesName`	A character string/vector specifying the signature of interest. If `any(geneSignaturesName == "") == TRUE`, it will run all available gene signatures' original models.
`adj`	A small positive real number used in `ComBat` to solve for genes with 0 counts(rare cases). Default is `1e-3`.
`BPPARAM`	An instance inherited from `bplapply`.

Details

Maertzdorf_4 and Maertzdorf_15 were trained using a random forest to distinguish patients with active TB from healthy controls.

Verhagen_10 was also trained using a random forest to distinguish samples with active TB from either latent infection or healthy controls. The random forest model was build using randomForest.

Jacobsen_3 were trained using linear discriminant analysis (LDA) to distinguish samples with active TB from latent infection status.

Sambarey_HIV_10 were also trained using LDA to distinguish samples with active TB from either latent infection, healthy control, or other disease (HIV). The LDA model was built using lda.

Berry_OD_86 and Berry_393 were trained using K-nearest neighbors (KNN) model to differentiate samples with active TB from latent infection status. The KNN model was built using knn.

Suliman_RISK_4 and Zak_RISK_16 were trained using support vector machines (SVM) to distinguish TB progressor from non-progressors. The input gene expression features for Suliman_RISK_4 used the paired ratio of GAS6/CD1C, SEPTIN4/BLK, SEPTIN4/CD1C, GAS6/BLK. The SVM model was built using svm.