Our group developed and maintains freely-available, open-source software packages in R/Bioconductor/CRAN/GitHub to analyze biomedical data.

• TOAST Usage Stats
  (TOols for the Analysis of heterogeneouS Tissues): Devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge.. Available on Bioconductor.

• NeuCA Usage Stats
  (Neural-network based Cell type Annotation): R/Bioconductor package for single-cell RNA-seq data cell type annotation, using neural-network approaches. NeuCA is flexible and adjust the classification method it will adopt, depending on cell types’ correlation level. Available on Bioconductor and Shiny app.

• CondiS
  (Conditional survival distributionS for missing survival data imputation): R/Bioconductor package for imputing the survival times for censored observations based on their conditional survival distributions derived from the Kaplan-Meier estimator. ‘CondiS’ can replace the censored observations with the best approximations from the statistical model, allowing for direct application of machine learning-based methods. When covariates are available, ‘CondiS’ is extended by incorporating the covariate information through machine learning-based regression modeling (‘CondiS_X’), which can further improve the imputed survival time. Available on CRAN and Shiny app.

• EasyCellType Usage Stats
  R/Bioconductor package that can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher’s exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster. Available on Bioconductor and Shiny app.

• caROC
  (covariate adjusted Receiver Operating Characteristics for continuous biomarkers) R/CRAN package for computing covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Available on CRAN.

• CAMLU
  (Cell Annotation using Machine Learning-based method for the presence of Unknown cells) An R package that provides an autoencoder based method for annotating cell types from scRNA-seq data. The function can identify unknown cells with the input training data. It also can annotate the full lists of cell types with consideration of unknown cell types. This vignette introduces the CAMLU function and the things it can do for you. Available on GitHub.

• TransCox
  (Transfer learning with the Cox proportional hazards model) An R package that perform transfer learning in Cox model. It can effectively borrow risk estimation from a larger cohort to improve inference and analysis of the current cohort. Available on GitHub.

• RegionalST
  An R package for analyzing intrasample heterogeneity with spatial transcriptomics data. It can quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-regional cell type-specific differential analysis. Available on GitHub. See html tutorial at Tutorial.

• PartCNV
  An R package to improve the detection of locally aneuploid cells by incorporating cytogenetic information. It implements a statistical framework for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. Our method uses an EM algorithm with mixtures of Poisson distributions while incorporating cytogenetics information to guide the classification (partCNV). When applicable, we further improve the accuracy by integrating a Hidden Markov Model for feature selection (partCNVH). Available on GitHub.

• DistributedLearningPredictor
  A python package to perform distributed learning prediction models while protecting patients’ privacy. Available on GitHub.

• GBC
  The R package for generalized biclustering. Available on GitHub.