@inproceedings{SciPyProceedings_11,
address = {Pasadena, CA USA},
author = {Hagberg, Aric A and Schult, Daniel A and Swart, Pieter J},
booktitle = {Proceedings of the 7th Python in Science Conference},
editor = {Varoquaux, Ga{\"{e}}l and Vaught, Travis and Millman, Jarrod},
pages = {11--15},
title = {{Exploring Network Structure, Dynamics, and Function using NetworkX}},
year = {2008}
}
@article{Friedman2012,
abstract = {High-throughput sequencing based techniques, such as 16S rRNA gene profiling, have the potential to elucidate the complex inner workings of natural microbial communities - be they from the world's oceans or the human gut. A key step in exploring such data is the identification of dependencies between members of these communities, which is commonly achieved by correlation analysis. However, it has been known since the days of Karl Pearson that the analysis of the type of data generated by such techniques (referred to as compositional data) can produce unreliable results since the observed data take the form of relative fractions of genes or species, rather than their absolute abundances. Using simulated and real data from the Human Microbiome Project, we show that such compositional effects can be widespread and severe: in some real data sets many of the correlations among taxa can be artifactual, and true correlations may even appear with opposite sign. Additionally, we show that community diversity is the key factor that modulates the acuteness of such compositional effects, and develop a new approach, called SparCC (available at https://bitbucket.org/yonatanf/sparcc), which is capable of estimating correlation values from compositional data. To illustrate a potential application of SparCC, we infer a rich ecological network connecting hundreds of interacting species across 18 sites on the human body. Using the SparCC network as a reference, we estimated that the standard approach yields 3 spurious species-species interactions for each true interaction and misses 60{\%} of the true interactions in the human microbiome data, and, as predicted, most of the erroneous links are found in the samples with the lowest diversity.},
author = {Friedman, Jonathan and Alm, Eric J.},
doi = {10.1371/journal.pcbi.1002687},
editor = {von Mering, Christian},
issn = {1553-7358},
journal = {PLoS Computational Biology},
month = {sep},
number = {9},
pages = {e1002687},
publisher = {Public Library of Science},
title = {{Inferring Correlation Networks from Genomic Survey Data}},
url = {http://dx.plos.org/10.1371/journal.pcbi.1002687},
volume = {8},
year = {2012}
}
@article{Watts2018,
abstract = {A common goal of microbiome studies is the elucidation of community composition and member interactions using counts of taxonomic units extracted from sequence data. Inference of interaction networks from sparse and compositional data requires specialised statistical approaches. A popular solution is SparCC, however its performance limits the calculation of interaction networks for very high-dimensional datasets. Here we introduce FastSpar, an efficient and parallelisable implementation of the SparCC algorithm which rapidly infers correlation networks and calculates p -values using an unbiased estimator. We further demonstrate that FastSpar reduces network inference wall time by 2-3 orders of magnitude compared to SparCC. FastSpar source code, precompiled binaries, and platform packages are freely available on GitHub: github.com/scwatts/FastSpar},
author = {Watts, Stephen C and Ritchie, Scott C and Inouye, Michael and Holt, Kathryn E},
doi = {10.1101/272583},
journal = {bioRxiv},
month = {mar},
pages = {272583},
publisher = {Cold Spring Harbor Laboratory},
title = {{FastSpar: Rapid and scalable correlation estimation for compositional data}},
url = {https://www.biorxiv.org/content/early/2018/03/23/272583},
year = {2018}
}
