Direct analysis of microbial communities in the environment and human body has become more convenient and reliable owing to the advancements of high-throughput sequencing techniques for 16S rRNA gene profiling. Inferring the correlation relationship among members of microbial communities is of fundamental importance for genomic survey study.
Detects significant non-random patterns of co-occurrence (copresence and mutual exclusion) in incidence and abundance data. CoNet serves to open new opportunities for future targeted mechanistic studies of the microbial ecology of the human microbiome. It has been designed with (microbial) ecological data in mind, but can be applied in general to infer relationships between objects observed in different samples (for example between genes present or absent across organisms).
An approach that is capable of estimating correlation values from compositional data. Additionally, SparCC contains a script for calculating the distance between samples using the JSD metric, its square-root, and many other distance measures.
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.
Analyzes distance correlations between genomic elements. GenomeInspector calculates distance correlations between elements from at least two inputs or between elements in one input file and annotated genomic elements. It allows the extraction of elements from large sets that fulfill distance requirements.
Identifies significant co-occurrence patterns by finding sparse solutions to a system with a deficient rank. To be specific, we construct the system using log ratios of count or proportion data and solve the system using the l1-norm shrinkage method. Our comprehensive simulation studies show that REBACCA (i) achieves higher accuracy in general than the existing methods when a sparse condition is satisfied; (ii) controls the false positives at a pre-specified level, while other methods fail in various cases and (iii) runs considerably faster than the existing comparable method.
A method based on least squares with L1 penalty after log ratio transformation for raw compositional data to infer the correlations among microbes through a latent variable model. The simulation results show that CCLasso outperforms existing methods, e.g. SparCC, in edge recovery for compositional data.
Allows to estimate the sparse structure of inverse covariance for latent normal variables. gCoda addresses the high dimensionality of the microbiome data by using a penalized maximum likelihood method. It permits to infer the sparse direct interaction network among microbes from the logistic normal distribution of observed compositional data. The tool outperforms existing methods in edge recovery of inverse covariance for compositional data under a variety of scenarios.