Softwares

aPCoA

Covariate Adjusted Principal Coordinates Analysis: aPCoA is an easy-to-use tool, available as both an R package and a Shiny app,
to improve data visualization in this context, enabling enhanced presentation of the effects of interest.

BAZE

Bayesian zero-constrained regression with compositional covariates: BAZE
The proposed Bayesian sparse regression model addressed the challenges of microbiome data,
including the compositional nature of the data, the high dimension, and the relatedness among the features.
This packages include two different ways of addressing the fixed-sum constraint: the constrast transformation and the generalized transformation.

CAMLU

Cell Annotation using Machine Learning-based method for the presence of Unknown cells: CAMLU is 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.

DINGO, iDINGO

Integrative Differential Network Analysis in Genomics: iDINGO is a pathway-based method
for estimating group-specific conditional dependencies and inferring differential networks between groups, based on genomic data.
This can be done in a single-platform framework (for example, RNA-Seq data) or an integrative multi-platform framework
(microRNA -> RNA -> Proteomics, where data from all three platforms are available for every sample).
We recommend filtering genomic data to fewer than 300 genes, generally filtered using a pathway/pathways of interest.
Single-platform analyses are run using DINGO with an nxp matrix, where n is the number of samples.

GENECLUST

GENECLUST is a piece of computer software which can be used as a tool for exploratory analysis of gene expression microarray data.
The development of GeneClust was motivated by surging interest to search for interpretable biological structure in gene expression microarray data.

looPA

Microbiome feature identification using leave-one-out with Permanova assessment: looPA is a permutation based method,
which can account for phylogenetic relatedness between taxonomic features and identify important features for further investigation.

mediateR

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes: mediateR

NExUS

Network Estimation across Unequal Sample sizes: NExUS is a Bayesian method that enables joint learning of multiple networks
while avoiding artefactual relationship between sample size and network sparsity.

PRECISE

Personalized Cancer-specific Integrated Network Estimation: PRECISE is a general Bayesian framework for integrating existing interaction databases,
data-driven de novo causal structures, and upstream molecular profiling data to estimate cancer-specific integrated networks,
infer patient-specific networks and elicit interpretable pathway-level signatures.

ProgPerm

Progressive permutation for a dynamic representation of the robustness of microbiome discoveries: ProgPerm
We have developed this into a user-friendly and efficient R-shiny tool with visualizations.
By default, we use the Wilcoxon rank sum test to compute the p-values, since it is a robust nonparametric test.
Our proposed method can also utilize p-values obtained from other testing methods, such as DESeq.
This demonstrates the potential of the progressive permutation method to be extended to new settings.

sparseMbClust

Sparse tree-based clustering of microbiome data: sparseMbClust
R and Matlab code to implement the methods in "Sparse tree-based clustering of microbiome data
to characterize microbiome heterogeneity in pancreatic cancer

SurvivalContour

SurvivalContour: Show Survival Prediction in Contour Plot: SurvivalContour
the predicted survival or cumulative incidence function (for competing risks data) over time for a single continuous covariate in the form of a contour plot.
The estimate for the survival probability is based on the Cox model, the spline model, random survival forest, or parametric survival models, including the generalized
Gamma AFT model, the stable generalized Gamma AFT model, the Weibull AFT model, the log-logistic AFT model, and the log-Normal AFT model.