.OriginalModel_Retraining {TBSignatureProfiler} | R Documentation |
A function to obtain predicted score for TB gene signatures that need retraining of original models.
.OriginalModel_Retraining(input, useAssay, geneSignaturesName, adj, BPPARAM)
input |
A SummarizedExperiment object with gene symbols as the assay row names. |
useAssay |
A character string or an integer specifying the assay in the
|
geneSignaturesName |
A character string/vector specifying the signature
of interest. If |
adj |
A small positive real number used in |
BPPARAM |
An instance inherited from |
Maertzdorf_4 and Maertzdorf_15 were trained using random forest to distinguish
patients with active TB from healthy controls.
Verhagen_10 was also trained using 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 discriminatory 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 build 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 build 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 build using svm
.
A SummarizedExperiment object with predicted scores for each sample obtained from the signature's original model.