Models used for predicting sample label ("POS": robustly maps R-loops; "NEG": poorly maps R-loops )
based on R-loop-forming sequences analysis (RLSeq::analyzeRLFS()).
These models are used with RLSeq::predictCondition().
fft_model(quiet = FALSE) prep_features(quiet = FALSE)
| quiet | If TRUE, messages are suppressed. Default: FALSE. |
|---|
A model object from the caret package.
The models were developed as part of a semi-automated online learning scheme
found in the RLBase-data protocol here.
Briefly, R-loop-forming sequences (RLFS) analysis was performed using RLSeq::analyzeRLFS()
for every sample peakset in RLBase (see rlfs_res for full results). The
samples were then manually inspected and any which starkly differed from
their label were removed. Out of 693 possible samples, 135 were excluded
due to a mismatch with their label. The remaining steps were
performed automatically.
First,The non-discarded samples were partitioned 50:25:25 (train:test:discovery).
Feature transformation was performed on the full data-set using the "YeoJohnson"
transform along with typical standardization via caret::preProcess().
Then, feature selection was performed in the discovery set using Boruta::Boruta().
Then, the training set was then trained using a stacked ensemble model:
The ensemble model is a Random Forest and the 5 base models in the stack are:
Latent Dirichlet allocation
Recursive partitioning
Generalized linear model (logit)
K-nearest neighbors
Support vector machine (radial)
10-fold 5-repeated cross-validation was implemented during training.
Finally, The model was then evaluated in the testing set. It demonstrates an accuracy of 0.9043. For more details, see the HTML report here.
prep_features()
A feature-transform model which prepares the data for classification.
It is an object of class preProcess from the caret::preProcess() function call.
fft_model()
A binary classifier which returns "POS" or "NEG".
It is an object of class caretStack from the caretEnsemble::caretList() function call.
These models are used internally by RLSeq::predictCondition().
fftModel <- fft_model() pfModel <- prep_features()