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SPIEC-EASI specifications

Information


Unique identifier OMICS_18242
Name SPIEC-EASI
Alternative name SParse InversE Covariance Estimation for Ecological Association Inference
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Version 0.1.2
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Zachary Kurtz

Publication for SParse InversE Covariance Estimation for Ecological Association Inference

SPIEC-EASI citations

 (15)
call_split

A comparison of methods used to unveil the genetic and metabolic pool in the built environment

2018
Microbiome
PMCID: 5902888
PMID: 29661230
DOI: 10.1186/s40168-018-0453-0
call_split See protocol

[…] is. Thus, a total of 569,372 reads were included for microbial community analyses. Community membership and composition were analyzed using unweighted and weighted UniFrac distances, respectively []. SParse InversE Covariance Estimation for Ecological Association Inference (SPIEC-EASI) was used to assess potential ecological associations between microbial taxa in the active and total populations, […]

call_split

A Lachnospiraceae dominated bacterial signature in the fecal microbiota of HIV infected individuals from Colombia, South America

2018
Sci Rep
PMCID: 5852036
PMID: 29540734
DOI: 10.1038/s41598-018-22629-7
call_split See protocol

[…] ion of the zCompositions package allowed us to replace zeros.We performed a Jennrich test to calculate the p-value associated to the correlation structures between HIV-infected patients control group.SPIEC-EASI was employed using the strategy of Meinshausen-Buhlmann for graph estimation of the network, which is a modified precision matrix built from β coefficients calculated from the average neare […]

call_split

Fragile skin microbiomes in megacities are assembled by a predominantly niche based process

2018
Sci Adv
PMCID: 5842045
PMID: 29532031
DOI: 10.1126/sciadv.1701581
call_split See protocol

[…] ities were excluded. In total, 121, 120, 222, 224, and 224 OTUs were used in the network analyses for Beijing, Guangzhou, Xi’an, Kunming, and Hohhot, respectively. We performed the statistical method SPIEC-EASI, which enables the inference of microbial ecological networks from OTU data sets. Our data sets contained hundreds to thousands of OTUs and tens to thousands of samples. Notably, there were […]

library_books

Linking Associations of Rare Low Abundance Species to Their Environments by Association Networks

2018
Front Microbiol
PMCID: 5850922
PMID: 29563898
DOI: 10.3389/fmicb.2018.00297

[…] lso helpful to explore rare species and to validate the approach. There are, however, some challenges in developing a realistic model of microbial communities. Available computational tools, such as “SPIEC-EASI” R package () generate a synthetic OTU data using a random selection of species. The randomness contradicts the major assumption of the Anets algorithm that the selection of species in the […]

call_split

Individual and household attributes influence the dynamics of the personal skin microbiota and its association network

2018
Microbiome
PMCID: 5797343
PMID: 29394957
DOI: 10.1186/s40168-018-0412-9
call_split See protocol

[…] SParse InversE Covariance Estimation for Ecological Association Inference (SPIEC-EASI) was used to assess potential ecological associations between microbial taxa. SPIEC-EASI has been recently applied […]

library_books

Fungi stabilize connectivity in the lung and skin microbial ecosystems

2018
Microbiome
PMCID: 5769346
PMID: 29335027
DOI: 10.1186/s40168-017-0393-0

[…] We adapted the SPIEC-EASI method to analyze microbiome networks across multiple microbial domains []. The tables of absolute bacteria and eukaryote OTU counts are stored in matrices = W∈ℕ0n×d,V∈ℕ0n×p, where wj=w1jw2 […]

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SPIEC-EASI institution(s)
Departments of Microbiology and Medicine, New York University School of Medicine, New York, NY, USA; Department of Biology, Center for Genomics and Systems Biology, New York University, New York, NY, USA; Courant Institute of Mathematical Sciences, New York University, New York, NY, USA; Simons Center for Data Analysis, Simons Foundation, New York, NY, USA
SPIEC-EASI funding source(s)
Supported by the National Institutes of Health grants T32AI007180-30, R01 DK103358-01 and RO1 GM63270 and the Simons Foundation.

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