Efficient statistical significance approximation for local similarity analysis of high-throughput time series data

Xia, Li Charlie; Ai, Dongmei; Cram, Jacob A.; Fuhrman, Jed A.; Sun, Fengzhu

doi:10.1093/bioinformatics/bts668

Cited by 127 publications

(202 citation statements)

References 28 publications

(46 reference statements)

Supporting

Mentioning

199

Contrasting

Unclassified

Order By: Relevance

“…We used Local Similarity Analysis (eLSA) to identify intervals of correlation between OTUs and environmental parameters: analysis settings included a minimum occurrence of 5 months, normalization of variables by 'percentileZ' method, use of p mix (determined theoretical P-value followed by permutation testing (n ¼ 2000) for any P o0.1 to decrease computation time while maintaining accuracy) and linear interpolation of missing values (Ruan et al, 2006a;Xia et al, 2011Xia et al, , 2013. eLSA correlations with P o0.05 and q o0.10 were visualized in Cytoscape v2.8.2 (Shannon 2003;Smoot et al, 2011); q-values were calculated to determine a false-discovery rate (Storey 2002).…”

Section: Discussionmentioning

confidence: 99%

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

et al. 2013

View full text Add to dashboard Cite

Time-series are critical to understanding long-term natural variability in the oceans. Bacterial communities in the euphotic zone were investigated for over a decade at the San Pedro Ocean Time-series station (SPOT) off southern California. Community composition was assessed by Automated Ribosomal Intergenic Spacer Analysis (ARISA) and coupled with measurements of oceanographic parameters for the surface ocean (0-5 m) and deep chlorophyll maximum (DCM, average depth B30 m). SAR11 and cyanobacterial ecotypes comprised typically more than one-third of the measured community; diversity within both was temporally variable, although a few operational taxonomic units (OTUs) were consistently more abundant. Persistent OTUs, mostly Alphaproteobacteria (SAR11 clade), Actinobacteria and Flavobacteria, tended to be abundant, in contrast to many rarer yet intermittent and ephemeral OTUs. Association networks revealed potential niches for key OTUs from SAR11, cyanobacteria, SAR86 and other common clades on the basis of robust correlations. Resilience was evident by the average communities drifting only slightly as years passed. Average Bray-Curtis similarity between any pair of dates was B40%, with a slight decrease over the decade and obvious near-surface seasonality; communities 8-10 years apart were slightly more different than those 1-4 years apart with the highest rate of change at 0-5 m between communities o4 years apart. The surface exhibited more pronounced seasonality than the DCM. Inter-depth Bray-Curtis similarities repeatedly decreased as the water column stratified each summer. Environmental factors were better predictors of shifts in community composition than months or elapsed time alone; yet, the best predictor was community composition at the other depth (that is, 0-5 m versus DCM).

show abstract

Section: Discussionmentioning

confidence: 99%

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

et al. 2013

View full text Add to dashboard Cite

show abstract

“…The eLSA analysis was run with the program's default parameters, that is, with no delay allowed (delayLimit = 0), P-value calculated by theoretical approximation (P-valueMethod = theo), required precision of P-value as 1/1000 (precision = 1000), and data rank-normalized and z-transformed (normMethod = robustZ) (Ruan et al, 2006;Xia et al, 2013). Multiple hypothesis correction was done using q-values (Storey, 2002).…”

Section: Toolsmentioning

confidence: 99%

“…For example, CoNet acknowledges that various techniques have different strengths and weaknesses and/or are designed to optimally detect different functional relationships, and thus uses an ensemble method with the ReBoot procedure for P-value computation to combine information from several different standard comparison metrics. Local Similarity Analysis (LSA) (Ruan et al, 2006;Beman et al, 2011;Steele et al, 2011;Xia et al, 2013) is optimized to detect non-linear, time-sensitive relationships and can be used to build correlation networks from time-series data. The Maximal Information Coefficient (MIC) (Reshef et al, 2011) is a non-parametric method designed to capture a wide range of associations without limitation to specific function types (such as linear or exponential) and to give similar scores to equally noisy relationships of different types.…”

Section: Introductionmentioning

confidence: 99%

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

et al. 2016

Self Cite

View full text Add to dashboard Cite

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

show abstract

“…dispersal barriers, colonisation history, past environmental conditions) or more contemporary species sorting processes. Other methods, such as extended local similarity analysis (eLSA) (Xia et al 2011(Xia et al , 2013 and similar network approaches (e.g. Faust et al 2015) can identify time-delayed associations between occurences of taxa and environmental variables.…”

Section: How To Measure and Study Legacy Effectsmentioning

confidence: 99%

The legacy of the past: effects of historical processes on microbial metacommunities

Vass¹,

Langenheder²

2017

Aquat. Microb. Ecol.

View full text Add to dashboard Cite

Distinguishing the importance of different community assembly mechanisms is an emerging topic in microbial ecology and much focus has been placed in recent years on investigating how contemporary environmental conditions, dispersal and stochastic processes influence the spatial turnover of communities. However, historical events, such as past environmental conditions or dispersal events, can be important as well. We provide a short summary of the processes that can lead to so-called legacy effects, where past biotic or abiotic factors influence the composition of present-day communities. Priority effects, which arise if early colonizers gain advantage over later-arriving species, can lead to persistent legacy effects. In contrast, time-lags in environmental selection can lead to transient legacy effects. Dispersal rates as well as factors that influence the adaptability of species to changing environmental conditions should be important factors that determine the relative importance of contemporary selection versus historical processes and whether legacy effects are likely to be permanent or temporary. Working with microbial communities offers the advantage of feasible time series studies and multi-generation experiments, and can therefore make important contributions to a novel systematic framework on how historical processes shape complex metacommunities in nature.

show abstract

Efficient statistical significance approximation for local similarity analysis of high-throughput time series data

Abstract: fsun@usc.edu.

Cited by 127 publications

References 28 publications

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

The legacy of the past: effects of historical processes on microbial metacommunities

Contact Info

Product

Resources

About