#There are a number of books or guides on compositional data analysis. The vast majority of these discuss CoDa from a gelolgical point of view:
##The basics:
Aitchison, J. The Statistical Analysis of Compositional Data (1986)
Aitchison, J. A Concise Guide to Compositional Data Analysis. http://ima.udg.edu/Activitats/CoDaWork05/A_concise_guide_to_compositional_data_analysis.pdf
Pawlowsky-Glahn, Vera and Egozcue, Juan José and Tolosana-Delgado, Raimon. Modeling and Analysis of Compositional Data (2015)
Gregory B. Gloor and Jean M. Macklaim and Andrew D. Fernandes Displaying Variation in Large Datasets: a Visual Summary of Effect Sizes (2016) Journal of Computational and Graphical Statistics, in press (http://dx.doi.org/10.1080/10618600.2015.1131161) (A new way to visualize high-dimensional data as an effect plot)
Colquhoun, D. An investigation of the fales discovery rate and the misinterpretation of p-values. (2014) R. Soc. Open Sci. 1:140216 doi: 10.1098/rsos.140216
##Functional guides:
Van den Boogaart, K Gerald and Tolosana-Delgado, Raimon. Analyzing compositional data with R (2013)
Aitchison, John and Greenacre, Michael. Biplots of compositional data (2002). Journal of the Royal Statistical Society: Series C, 51:375
##Recent papers:
Gloor, Gregory B. and Macklaim, Jean M. and Pawlowsky-Glahn, Vera and Egozcue, Juan J. Microbiome Datasets Are Compositional: And This Is Not Optional. 2017. Frontiers in Microbiology 8:2224 (The supplement contains worked examples)
Bian, Gaorui and Gloor, Gregory B et.al. The Gut Microbiota of Healthy Aged Chinese Is Similar to That of the Healthy Young 2017. mSphere 2:e00327-17 (an example on a very large dataset. Includes supplement of all code)
Gloor, Gregory B and Wu, Jia Rong and Pawlowsky-Glahn, Vera and Egozcue, Juan José It's all relative: analyzing microbiome data as compositions (2016) Annals. Epidemiology: May;26(5):322-9 (http://dx.doi.org/10.1016/j.annepidem.2016.03.003) (A full worked example and code)
Gloor, G. B. and Reid ,Gregor. Compositional analysis: a valid approach to analyze microbiome high throughput sequencing data (2016) Can J Microbiol, 62(8):692–703 (http://dx.doi.org/10.1139/cjm-2015-0821) (A full worked example and code)
Gloor, Gregory B and Macklaim, Jean M and Vu, Michael and Fernandes, Andrew D Compositional uncertainty should not be ignored in high-throughput sequencing data analysis (2016) Austrian Journal of Statistics, 45:73–87, September 2016. (A more formal treatment of the uncertainty problem and one way around it)
Lovell, David and Pawlowsky-Glahn, Vera and Egozcue, Juan José and Marguerat, Samuel and Bähler, Jürg Proportionality: a valid alternative to correlation for relative data (2015) PLoS Comput Biol, 11:e1004075 (Nicely illustrates the problems and a general solution to the correlation problem)
Fernandes, Andrew D and Reid, Jennifer Ns and Macklaim, Jean M and McMurrough, Thomas A and Edgell, David R and Gloor, Gregory B Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis (2014) Microbiome, 2:15.1 (The first paper showing that essentially all high throughput data share similar compositional problems)
Fernandes, A. D. and Macklaim, J. M. and Linn, T.G and Reid, G. and Gloor, G. B. ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq (2013). PLoS ONE: 8:e67019 (One of the first papers to point out compositional problems in RNA-seq data)
Friedman, Jonathan and Alm, Eric J. Inferring correlation networks from genomic survey data (2012) PLoS Comput Biol. 8:e1002687 (The first paper to point out compositional problems in microbiome data)
##A few applications
Macklaim, M Jean and Fernandes, D Andrew and Di Bella, M Julia and Hammond, Jo-Anne and Reid ,Gregor and Gloor, Gregory B. Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis (2013) Microbiome 1:15
McMurrough, Thomas A and Dickson, Russell J and Thibert, Stephanie M F and Gloor, Gregory B and Edgell, David R. Control of catalytic efficiency by a coevolving network of catalytic and noncatalytic residues (2014) Proc Natl Acad Sci U S A. 111:e2376
Macklaim, Jean M and Clemente, Jose C and Knight, Rob and Gloor, Gregory B and Reid, Gregor. Changes in vaginal microbiota following antimicrobial and probiotic therapy (2015) Microb Ecol Health Dis. 26:27799
Goneau, Lee W and Hannan, Thomas J and MacPhee, Roderick A and Schwartz, Drew J and Macklaim, Jean M and Gloor, Gregory B and Razvi, Hassan and Reid, Gregor and Hultgren, Scott J and Burton, Jeremy P Subinhibitory antibiotic therapy alters recurrent urinary tract infection pathogenesis through modulation of bacterial virulence and host immunity (2015) Mbio, 6:
McMillan, Amy and Rulisa, Stephen and Sumarah, Mark and Macklaim, Jean M. and Renaud, Justin and Bisanz, Jordan E. and Gloor, Gregory B. and Reid, Gregor A multi-platform metabolomics approach identifies highly specific biomarkers of bacterial diversity in the vagina of pregnant and non-pregnant women (2015) Scientific Reports, 5:14174ep