Alpha and Beta Diversity Explanations and Commands

tutorial
alpha
beta

(Stephanie Orchanian) #1

About the Alpha and Beta Diversity Analysis Tutorial

This Alpha and Beta Diversity Community Tutorial (run using QIIME 2017.12) walks you through analyzing the alpha and beta diversity of a sample dataset. Below you will find a link to a small test dataset to download and use in this tutorial.

Files used in tutorial

The following files, derived from the Moving Pictures tutorial, are used in this document.

Alpha Diversity Analysis

The alpha and alpha-phylogenetic methods compute a user-specified alpha diversity metric for all samples in a feature table.

Phylogenetic alpha diversity metrics (in this case, Faith’s Phylogenetic Diversity), can be run with the following command:

qiime diversity alpha-phylogenetic \
  --i-table table.qza \
  --i-phylogeny rooted-tree.qza \
  --p-metric faith_pd \
  --o-alpha-diversity faith_pd_vector.qza

Non-phylogenetic alpha diversity metrics (in this case, Observed OTUs), can be run with the following command:

qiime diversity alpha \
  --i-table table.qza \
  --p-metric observed_otus \
  --o-alpha-diversity observed_otus_vector.qza

The --i-table input provides the feature table containing the samples for which the alpha diversity metric will be computed. The --i-phylogeny input provides the phylogenetic tree containing the tip identifiers that correspond to the feature identifiers in the table, and is only used for the alpha-phylogenetic command (i.e., when computing phylogenetic diversity metrics. The --p-metric parameter specifies the alpha diversity metric to be run. The --o-alpha-diversity output specifies the output file.

To compute a different alpha diversity metric, change the ``–p-metric` parameter to the one that corresponds to the metric you want to compute. The following list provides information on the available alpha diversity metrics in QIIME 2.

  • Abundance-based Coverage Estimator (ACE) metric: Calculates the ACE metric
    • Estimates species richness using a correction factor
    • --p-metric: ace
    • Chao, A. and Lee, S.M
 (1992). “Estimating the number of classes via sample coverage”. Journal of the American Statistical Association. (87): 210-217.
  • Berger-Parker Dominance Index: Calculates Berger-Parker dominance index
    • Relative richness of the abundant species
    • --p-metric: berger_parker_d
    • Berger, W.H. and Parker, F.L. (1970). “Diversity of planktonic Foraminifera in deep sea sediments”. Science. (168): 1345-1347.
  • Brillouin’s index: Calculates Brillouin’s index
    • Measures the diversity of the species present
    • Use when randomness can’t be guaranteed
    • --p-metric: brillouin_d
    • Pielou, E. C. (1975). Ecological Diversity. New York, Wiley InterScience.
  • Chao1 confidence interval: Calculates chao1 confidence interval
    • Confidence interval for richness estimator, Chao1
    • --p-metric: chao1_ci
    • Colwell, R.K., Mao, C.X., Chang, J. (2004). “Interpolating, extrapolating, and comparing incidence-based species accumulation curves.” Ecology. (85), 2717-2727.
  • Chao1 index: Calculates Chao1 index
    • Estimates diversity from abundant data
    • Estimates number of rare taxa missed from undersampling
    • --p-metric: chao1
    • *Chao, A. (1984). “Non-parametric estimation of the number of classes in a population”.
  • Dominance measure: Calculates dominance measure**
    • How equally the taxa are presented
    • --p-metric: dominance
  • Effective Number of Species (ENS)/Probability of intra-or interspecific encounter (PIE) metric: Calculates Effective Number of Species (ENS)/Probability of intra-or interspecific encounter (PIE) metric
    • Shows how absolute amount of species, relative abundances of species, and their intraspecific clustering affect differences in biodiversity among communities
    • --p-metric: enspie
    • Chase, J.M., and Knight, R. (2013). “Scale-dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough”. Ecology Letters (16): 17-26.
  • Etsy confidence interval: Calculates Esty’s confidence interval
    • Confidence interval for how many singletons in total individuals
    • --p-metric: etsy_ci
    • Esty, W. W. (1983). “A normal limit law for a nonparametric estimator of the coverage of a random sample”. Ann Statist. (11): 905-912.
  • Faith’s phylogenetic diversity: Calculates faith’s phylogenetic diversity
    • Measures of biodiversity that incorporates phylogenetic difference between species
    • Sum of length of branches
    • --p-metric: faith_pd
    • Faith. D.P. (1992). “Conservation evaluation and phylogenetic diversity”. Biological Conservation. (61) 1-10.
  • Fisher’s index: Calculates Fisher’s index
    • Relationship between the number of species and the abundance of each species
    • --p-metric: fisher_alpha
    • Fisher, R.A., Corbet, A.S. and Williams, C.B. (1943). “The relation between the number of species and the number of individuals in a random sample of an animal population”. Journal of Animal Ecology. (12): 42-58.
  • Gini index: Calculates Gini index
    • Measures species abundance
    • Assumes that the sampling is accurate and that additional data would fall on linear gradients between the values of the given data
    • --p-metric: gini_index
    • Gini, C. (1912). “Variability and Mutability”. C. Cuppini, Bologna. 156.
  • Good’s coverage of counts: Calculates Good’s coverage of counts.
    • Estimates the percent of an entire species that is represented in a sample
    • --p-metric: goods_coverage
    • Good. I.J (1953) “The populations frequency of Species and the Estimation of Populations Parameters”. Biometrika. 40(3/4):237-264
  • Heip’s evenness measure: Calculates Heip’s evenness measure.
    • Removes dependency on species number
    • --p-metric: heip_e
    • Heip, C. (1974). “A new index measuring evenness”. J. Mar. Biol. Ass. UK. (54): 555-557.
  • Kempton-Taylor Q index: Calculates Kempton-Taylor Q index
    • Measured diversity based off the distributions of species
    • Makes abundance curve based off all species and IQR is used to measure diversity
    • --p-metric: kempton_taylor_q
    • Kempton, R.A. and Taylor, L.R. (1976). “Models and statistics for species diversity”. Nature (262): 818-820.
  • Lladser’s confidence interval: Calculates Lladser’s confidence interval
    • Single confidence interval of the conditional uncovered probability
    • --p-metric: lladser_ci
    • Lladser, M.E., Gouet, R., Reeder, R. (2011). “Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown”. PLoS.
  • Lladser’s point estimate: Calculates Lladser’ point estimate
    • Estimates how much of the environment contains unsampled taxa
    • Best estimate on a complete sample
    • --p-metric: lladser_pe
    • Lladser, M.E., Gouet, R., Reeder, J. (2011). “Extrapolation of Urn Models via Poissonization: Accurate Measurements of the Microbial Unknown”. PLoS.
  • Margalef’s richness index: Calculates Margalef’s richness index
    • Measures species richness in a given area or community
    • --p-metric: margalef
    • Magurran, A.E. (2004). “Measuring biological diversity”. Blackwell. 76-77.
  • Mcintosh dominance index D: Calculates McIntosh dominance index D
    • Affected by the variation in dominant taxa and less affected by the variation in less abundant or rare taxa
    • --p-metric: msintosh_d
    • McIntosh, R.P. (1967). “An index of diversity and the relation of certain concepts to diversity”. Ecology (48): 392-404.
  • Mcintosh evenness index E: Calculates McIntosh’s evenness measure E
    • How evenly abundant taxa are
    • --p-metric: mcintosh_e
    • Heip, C. (1974). “A new index measuring evenness”. J. Mar. Biol. Ass. UK. (54) 555-557.
  • Menhinick’s richness index: Calculates Menhinick’s richness index
    • The ratio of the number of taxa to the square root of the sample size
    • --p-metric: menhinick
    • Magurran, A.E. (2004). “Measuring biological diversity”. Blackwell. 76-77.
  • Michaelis-Menten fit to rarefaction curve of observed OTUs: Calculates Michaelis-Menten fit to rarefaction curve of observed OTUs.
    • Estimated richness of species pools
    • --p-metric: michaelis_mentin_fit
    • Raaijmakers, J.G.W. (1987). “Statistical analysis of the Michaelis-Menten equation”. Biometrics. (43): 793-803.
  • Number of distinct features: Calculates number of distinct OTUs
    • --p-metric: observed_otus
    • DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K. Huber, T., Davis, D., Hu, P., Andersen, G.L. (2006). “Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB”. Applied and Environmental Microbiology (72): 5069–5072.
  • Number of double occurrences: Calculates number of double occurrence OTUs (doubletons)
    • OTUs that only occur twice
    • --p-metric: doubles
  • Number of observed features, including singles and doubles: Calculates number of observed OTUs, singles, and doubles.
    • --p-metric: osd
    • DeSantis, T.Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E.L., Keller, K. Huber, T., Davis, D., Hu, P., Andersen, G.L. (2006). “Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB”. Applied and Environmental Microbiology. 72 (7): 5069–5072.
  • Singles: Calculates number of single occurrence OTUs (singletons)
    • OTUs that appear only once in a given sample
    • --p-metric: singles
  • Pielou’s evenness: Calculates Pielou’s eveness
    • Measure of relative evenness of species richness
    • --p-metric: pielou_e
    • Pielou, E. (1966). “The measurement of diversity in different types of biological collections”. J. Theor. Biol. (13): 131-144.
  • Robbins’ estimator: Calculates Robbins’ estimator
    • Probability of unobserved outcomes
    • --p-metric: robbins
    • Robbins, H.E. (1968). “Estimating the Total Probability of the unobserved outcomes of an experiment”. Ann Math. Statist. 39(1): 256-257.
  • Shannon’s index: Calculates Shannon’s index
    • Calculates richness and diversity using a natural logarithm
    • Accounts for both abundance and evenness of the taxa present
    • --p-metric: shannon
    • Shannon, C.E. and Weaver, W. (1949). “The mathematical theory of communication”. University of Illonois Press, Champaign, Illonois.
  • Simpson evenness measure E: Calculates Simpson’s evenness measure E.
    • Diversity that account for the number of organisms and number of species
    • --p-metric: simpson_e
    • Simpson, E.H. (1949). “Measurement of Diversity”. Nature. (163): 688
  • Simpson’s index: Calculates Simpson’s index
    • Measures the relative abundance of the different species making up the sample richness
    • --p-metric: simpson
    • Simpson, E.H. (1949). “Measurement of diversity". Nature. (163): 688.
  • Strong’s dominance index (Dw): Calculates Strong’s dominance index
    • Measures species abundance unevenness
    • --p-metric: strong
    • Strong, W.L. (2002). “Assessing species abundance uneveness within and between plant communities”. Community Ecology (3): 237-246.

Beta Diversity Analysis

The beta and beta-phylogenetic methods compute a user-specified beta diversity metric for all samples in a feature table.

Phylogenetic beta diversity metrics (in this case, Unweighted UniFrac), can be run with the following command:

qiime diversity beta-phylogenetic \
  --i-table table.qza \
  --i-phylogeny rooted-tree.qza \
 --p-metric unweighted_unifrac \
 --o-distance-matrix unweighted_unifrac_distance_matrix.qza

Non-phylogenetic beta diversity metrics (in this case, Bray-Curtis), can be run with the following command:

qiime diversity beta \
  --i-table table.qza \
 --p-metric braycurtis \
 --o-distance-matrix unweighted_unifrac_distance_matrix.qza

The --i-table input provides the feature table containing the samples for which the beta diversity metric will be computed. The --i-phylogeny input provides the phylogenetic tree containing the tip identifiers that correspond to the feature identifiers in the table, and is only used for the beta-phylogenetic command (i.e., when computing phylogenetic diversity metrics. The --p-metric parameter specifies the beta diversity metric to be run. The --o-distance-matrix output specifies the output file.

To compute a different beta diversity metric, change the ``–p-metric` parameter to the one that corresponds to the metric you want to compute. The following list provides information on the available beta diversity metrics in QIIME 2.

  • Bray-Curtis dissimilarity: Calculates Bray–Curtis dissimilarity
    • Fraction of overabundant counts
    • --p-metric: braycurtis
    • Sorenson, T. (1948) “A method of establishing groups of equal amplitude in plant sociology based on similarity of species content.” Kongelige Danske Videnskabernes Selskab 5.1-34: 4-7.
  • Canberra distance: Calculates Canberra distance
    • Overabundance on a feature by feature basis
    • --p-metric: canberra
    • Lance, Godfrey L.N. and Williams, W.T. (1967). “A general theory of classificatory sorting strategies II. Clustering systems.” The computer journal 10 (3):271-277.
  • Chebyshev distance: Calculates Chebyshev distance
    • Maximum distance between two samples
    • --p-metric: chebyshev
    • Cyrus. D. Cantrell (2000). “Modern Mathematical Methods for Physicists and Engineers”. Cambridge University Press.
  • City-block distance: Calculates City-block distance
    • Similar to the Euclidean distance but the effect of a large difference in a single dimension is reduced
    • --p-metric: cityblock
    • Paul, E.B. (2006). “Manhattan distance". Dictionary of Algorithms and Data Structures
  • Correlation coefficient: Measures Correlation coefficient
    • Measure of strength and direction of linear relationship between samples
    • --p-metric: correlation
    • Galton, F. (1877). “Typical laws of heredity”. Nature. 15 (388): 492–495.
  • Cosine Similarity: Measures Cosine similarity
    • Ratio of the amount of common species in a sample to the mean of the two samples
    • --p-metric: cosine
    • Ochiai, A. (1957). “Zoogeographical Studies on the Soleoid Fishes Found in Japan and its Neighhouring Regions-II”. Nippon Suisan Gakkaishi. 22(9): 526-530.
  • Dice measures: Calculates Dice measure
    • Statistic used for comparing the similarity of two samples
    • Only counts true positives once
    • --p-metric: dice
    • Dice, Lee R. (1945). “Measures of the Amount of Ecologic Association Between Species”. Ecology. 26 (3): 297–302.
  • Euclidean distance: Measures Euclidean distance
    • Species-by-species distance matrix
    • --p-metric: euclidean
    • Legendre, P. and Caceres, M. (2013). “Beta diversity as the variance of community data: dissimilarity coefficients and partitioning.” Ecology Letters. 16(8): 951-963.
  • Generalized Unifrac: Measures Generalized UniFrac
    • Detects a wider range of biological changes compared to unweighted and weighted UniFrac
    • --p-metric: generalized_unifrac
    • Chen, F., Bittinger, K., Charlson, E.S., Hoffmann, C., Lewis, J., Wu, G. D., Collman, R.G., Bushman, R.D., Li,H. (2012). “Associating microbiome composition with environmental covariates using generalized UniFrac distances.” Bioinformatics. 28 (16): 2106-2113.
  • Hamming distance: Measures Hamming distance
    • Minimum number of substitutions required to change one group to the other
    • --p-metric: hamming
    • Hamming, R.W. (1950) “Error Detecting and Error Connecting Codes”. The Bell System Technical Journal. (29): 147-160.
  • Jaccard similarity index: Calculates Jaccard similarity index
    • Fraction of unique features, regardless of abundance
    • --p-metric: jaccard
    • Jaccard, P. (1908). “Nouvellesrecherches sur la distribution florale.” Bull. Soc. V and. Sci. Nat., (44):223-270.
  • Kulczynski dissimilarity index: Measures Kulczynski dissimilarity index
    • Describes the dissimilarity between two samples
    • --p-metric: kulsinski
    • Kulcynski, S. (1927). “Die Pflanzenassoziationen der Pieninen. Bulletin International de l’Academie Polonaise des Sciences et des Lettres”. Classe des Sciences Mathematiques et Naturelles. 57-203.
  • Mahalanobis distance: Calculates Mahalanobis distance
    • How many standard deviations one sample is away from the mean
    • Unitless and scale-invariant
    • Takes into account the correlations of the data set
    • --p-metric: mahalanobis
    • Citation: Mahalanobis, Chandra, P. (1936). “On the generalised distance in statistics”. Proceedings of the National Institute of Sciences of India. 2 (1): 49–55.
  • Matching components: Measures Matching components
    • Compares indices under all possible situations
    • --p-metric: matching
    • Janson, S., and Vegelius, J. (1981). “Measures of ecological association”. Oecologia. (49): 371–376.
  • Rogers-tanimoto distance: Measures Rogers-Tanimoto distance
    • Allows the possibility of two samples, which are quite different from each other, to both be similar to a third
    • --p-metric: rogerstanimoto
    • Tanimoto, T. (1958). “An Elementary Mathematical theory of Classification and Prediction”. New York: Internal IBM Technical Report.
  • Russel-Rao coefficient: Calculates Russell-Rao coefficients
    • Equal weight is given to matches and non-matches
    • --p-metric: russelrao
    • Russell, P.F. and Rao, T.R. (1940). “On habitat and association of species of anopheline larvae in south-eastern Madras”. J. Malaria Inst. India. (3): 153-178.
  • Sokal-Michener coefficient: Measures Sokal-Michener coefficient
    • Proportion of matches between samples
    • --p-metric: sokalmichener
    • Sokal, R.R. and Michener, C.D. (1958). “A statistical method for evaluating systematic relationships”. Univ. Kans. Sci. Bull. (38) 1409-1438.
  • Sokal-Sneath Index: Calculates Sokal-Sneath index
    • Measure of species turnover
    • --p-metric: sokalsneath
    • Sokal, R.R. and Sneath, P.H.A. (1963). “Principles of Numerical Taxonomy”. W. H. Freeman, San Francisco, California.
  • Species-by-species Euclidean: Measures Species-by-species Euclidean
    • Standardized Euclidean distance between two groups
    • Each coordinate difference between observations is scaled by dividing by the corresponding element of the standard deviation
    • --p-metric: seuclidean
    • Legendre, P. and Caceres, M. (2013). “Beta diversity as the variance of community data: dissimilarity coefficients and partitioning.” Ecology Letters. 16(8): 951-963.
  • Squared Euclidean: Measures squared Euclidean distance
    • Place progressively greater weight on samples that are farther apart
    • --p-metric: sqeuclidean
    • Legendre, P. and Caceres, M. (2013). “Beta diversity as the variance of community data: dissimilarity coefficients and partitioning.” Ecology Letters. 16(8): 951-963.
  • Unweighted unifrac: Measures unweighted UniFrac
    • Measures the fraction of unique branch length
    • --p-metric: unweighted_unifrac
    • Lozupone, C. and Knight, R. (2005). “UniFrac: a new phylogenetic method for comparing microbial communities.” Applied and environmental microbiology 71 (12): 8228-8235.
  • Weighted Minkowski metric: Measures Weighted Minkowski metric
    • Allows the use of the k-means-type paradigm to cluster large data sets
    • --p-metric: wminkowski
    • Chan, Y., Ching, W.K., Ng, M.K., Huang, J.Z. (2004). “An optimization algorithm for clustering using weighted dissimilarity measures”. Pattern Recognition. 37(5): 943-952.
  • Weighted normalized UniFrac: Measures Weighted normalized UniFrac
    • Takes into account abundance
    • Normalization adjusts for varying root-to-tip distances.
    • --p-metric: weighted_normalized_unifrac
    • Lozupone, C. A., Hamady, M., Kelley, S. T., Knight, R. (2007). “Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities”. Applied and Environmental Microbiology. 73(5): 1576–85.
  • Weighted unnormalized UniFrac: Measures Weighted unnormalized UniFrac
    • Takes into account abundance
    • Doesn’t correct for unequal sampling effort or different evolutionary rates between taxa
    • --p-metric: weighted_unifrac
    • Lozupone, C. A., Hamady, M., Kelley, S. T., Knight, R. (2007). “Quantitative and qualitative beta diversity measures lead to different insights into factors that structure microbial communities”. Applied and Environmental Microbiology. 73(5): 1576–85.
  • Yule index: Measures Yule index
    • Measures biodiversity
    • Determined by the diversity of species and the proportions between the abundance of those species.
    • --p-metric: yule
    • Fisher, R.A., Corbert, A.S., Williams, C.B. (1943). “The Relationship Between the Number of Species and the Number of Individuals in a Random Sample of an Animal Population”. J. Animal Ecol. (12): 42-58.

To further analyze the results of your beta and alpha diversities, return to the QIIME 2 “Moving Pictures Tutorial” tutorial and continue at the “alpha-group-significance” command.


Selecting and visualizing alpha/beta diversity analyses
Suggestion: Beginner flow chart tutorial
--p-metric for beta diversity
Statistical analysis - looking for core microbiom
Weighted_unifrac_normalized metric
Presence-absence distance method
Presence-absence distance method
Beta diversity analysis
Weighted_unifrac_normalized metric
Interpreting results from pairwise Kruskal-Wallis
(Greg Caporaso) #3

Thanks so much for this contribution @Stephanieorch - this is really great!

I have a couple of minor suggestions (mostly related to formatting). Would you prefer that I make those edits to your post myself, or direct message you with them? Either works for me.

I think we should ultimately plan to transition this into the main QIIME 2 documentation (ie., https://docs.qiime2.org). I want to talk with some of the other developers about where it would make sense to put this. We’ve talked about having a section for plugin-specific documentation, in which case this would fit really well in a section on the q2-diversity plugin, but we don’t have that in place yet. We’re tied up with the 2017.12 release, and then the holidays, so we can follow up with you about this in early 2018.

Thanks again!


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(Danny W) #5

This is great @Stephanieorch!!!

@gregcaporaso I think it could be useful to discuss how the diversity outputs (distance matrix) could also be implemented into R programs like Vegan or phyloseq for further analysis. Unless Qiime2 is planning on incorporating these downstream tools in the future!!!


#6

Thanks for this comprehensive resource @Stephanieorch!! :slight_smile:


(Stephanie Orchanian) #7

Hi @gregcaporaso, I have no preference on how you make the edits so do the one that is easiest for you. That would be great to have this in the main QIIME 2 documentation. I look forward to speaking with you all in 2018.


(Greg Caporaso) #9

@Stephanieorch, I made a few edits directly. If you’d like to see my changes, you can view edits by click the little pencil icon to the right of your name toward the top of the post:

Thanks again for this great resource!


(Matthew Ryan Dillon) #11

An off-topic reply has been split into a new topic: How to specify multiple metrics when running alpha-rarefaction?

Please keep replies on-topic in the future.


(Nicholas Bokulich) #12

An off-topic reply has been split into a new topic: Weighted_unifrac_normalized metric

Please keep replies on-topic in the future.


(Nayeli Luis Vargas) #13

Thanks so much for this contribution @Stephanieorch.
It was so useful!!