R: A Method to Identify Single CpG Sites for Classification and Deconvolution

# A Method to Identify Single CpG Sites for Classification and Deconvolution

## Documentation for package ‘CimpleG’ version 1.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

CimpleG | Find simple CpG (CimpleG) signatures.
cimpleg | Find simple CpG (CimpleG) signatures.
compute_ax | Feature selection function used in the sigma delta space
compute_diffmeans_sumvar | Compute diff mean sum var dataframe
cpg | Find simple CpG (CimpleG) signatures.
darken | Helper function to darken down a given color.
deconvolution_barplot | Stacked barplot of deconvolution results
deconvolution_epidish | EpiDISH deconvolution
deconvolution_nmf | NMF deconvolution
deconvolution_nnls | NNLS deconvolution
deconv_pred_obs_plot | Scatter plots of observed (true) vs predicted values for deconvolution.
deconv_ranking_plot | Boxplot and rankings of deconvolution metrics for deconvolution results.
diffmeans_sumvariance_plot | Creates the old version of the difference in means by sum of variances plot
dmsv_plot | Creates the old version of the difference in means by sum of variances plot
eval_test_data | Evaluation of produced models on test data
get_cpg_annotation | Get CpG annotation from Illumina
lighten | Helper function to lighten up a given color.
load_object | Load an R object saved with CimpleG or an RDS file.
make_color_palette | Make color palette data frame
make_deconv_pred_obs_data | Make tidy data for use in deconvolution plots
make_deconv_ref_matrix | Build deconvolution reference matrix
predict.CimpleG | Predict outcome from a CimpleG signatures on new data
run_deconvolution | Perform deconvolution on a new set of samples, based on the CimpleG models trained
save_object | Save an R object to disk with fast and efficient compression algorithms.
select_features | Feature selection function used in the diffmeans, sumvariance space
signature_plot | CpG signature plot
test_data | Cell line test data
test_targets | Cell line test data targets
train_data | Cell line train data
train_targets | Cell line train data targets