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MDSINE / Microbial Dynamical Systems INference Engine
A suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. MDSINE performs all analysis steps from reading data files through to the generation of figures. MDSINE implements methods that not only outperform previous approaches but also provides novel functionality, including capabilities to estimate confidence in model parameters and predicted dynamics. Application of MDSINE to two new gnotobiotic experimental datasets demonstrates the capability to generate predictive hypotheses that standard microbiome analysis methods cannot and, moreover, suggests new strategies for rational design of bacteriotherapies.
SPIEC-EASI / SParse InversE Covariance Estimation for Ecological Association Inference
Infers ecological associations between microbial populations. SPIEC-EASI uses algorithms for sparse neighbourhood and inverse covariance selection in order to reconstruct networks. It is able to produce a synthetic benchmark in the absence of an experimentally validated gold-standard network. The tool was tested on a large-scale 16S rRNA gene sequencing dataset sampled from the human gut. The results show that it outperforms state-of-the-art methods to recover edges and network properties on synthetic data.
BAnOCC / Bayesian Analysis of Compositional Covariance
Finds correlations in compositional data. BAnOCC quantifies uncertainty through the associated posterior and estimates both the log-basis correlation and precision matrix by modeling the composition directly. It is a Bayesian method for inferring the log-basis correlation structure. This tool has been used to assess microbial relationships in the human microbiome, confirming established interactions and suggesting novel ones for future validation.
LOL / Lots Of Lasso
Optimizes various methods for Lasso inference with matrix wrapper. LOL offers functions for breast cancer data set of genome-wide copy number merged data and expression of some important genes, to get the lambda value that yield certain number of non-zero coefficients, or a function that contains various optimization methods for Lasso inference, such as cross-validation, randomised lasso, simultaneous lasso etc. It is specifically designed for multicollinear predictor variables.
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