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Change Log

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[Unreleased] - 2023-10-27

Added

  • New functions: doseRiderLMM and doseRiderGAMM.

    • Description: Introducing support for Linear Mixed Models (LMM) and Generalized Additive Mixed Models (GAMM) through doseRiderLMM and doseRiderGAMM functions respectively.

  • New feature in lmm.R:

    • Functions: fit_lmm and create_lmm_formula.
      • Description: The fit_lmm function allows fitting of linear mixed models while create_lmm_formula assists in formula creation for the LMM.
    • New Parameter: omic.
      • Description: The parameter lets the user choose between a Gaussian distribution and a negative binomial model, enhancing the flexibility of the model.

  • New function: smooth_pathway_trend.

    • Description: Utilizes the predict() function with re.form = NA to eliminate the fixed effect, providing a smoothed trend for pathway analysis.

  • Initial creation of bmd.R:

    • Purpose: Development of functionalities to calculate the Benchmark Dose (BMD). Note: Still in development phase.

[Unreleased] - 2023-08-03

Added

  • New option: center_values in plot_smooth.

    • Description: This option allows for centering and scaling the expression values for each gene.
    • Usage: When center_values is enabled T, the expression values of each gene are centered around their mean and scaled by dividing by their standard deviation.

  • New function: DoseRiderParallel.

    • Description: This function allows parallel processing of gene sets using multiple cores. It takes the same arguments as the original DoseRider function but runs the gene set analysis in parallel to improve computation speed.
    • Usage: DoseRiderParallel(se, gmt, dose_col = "dose", sample_col = "sample", covariate = "", omic = "rnaseq", minGSsize = 5, maxGSsize = 300, method = "fdr", num_cores = 5)

Changed

  • Calculation of p_value_cubic in DoseRider for the comparison between linear and cubic splines.
    • Description: Previously, only the linear base vs cubic p.value was calculated during the comparison. Now, we also calculate the p.value for the cubic spline.
    • Reason: To obtain the most non-linear significant gene sets.