R/splitTypeR.R
splitTypeR-package.RdThe splitTypeR package provides an automated statistical
framework for classifying heterogeneous biological samples based on
gene signature lists, effectively isolating signature-positive
samples. This classification method is designed for bulk
transcriptomic datasets.
The workflow begins by calculating a Gene Set Variation Analysis (GSVA) score for each sample to quantify the relative pathway activity. A subsequent permutation step extracts sample-specific standard deviations to capture the variance. By combining these metrics, the package establishes a unique normal distribution for each sample. Through an up-scaling step, values are randomly drawn from these individual distributions and pooled to construct a comprehensive mixture of normal distributions that represent the entire sample population.
Within this mixture model, the sub-distribution with the lower mean value is designated as the alternative distribution. The framework applies a hypothesis-testing approach in which the null hypothesis posits that a sample belongs to this alternative distribution. Samples that successfully reject the null hypothesis are assigned to the signature classification. Conversely, samples that fail to reject the null hypothesis remain unclassified.
By automating the classification workflow, the splitTypeR package
provides an objective method to identify patient or sample subgroups in
heterogeneous transcriptomic datasets.
runSubtypingBimodal for the classification of heterogeneous biological samples based on gene signature lists using a mixture of two normal distributions
getGeneSignaturesNames for the names of the available and ready-to-use gene signature lists
getGeneSignatures for the available and ready-to-use gene signature lists