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We are developing a taxonomic analysis pipeline for multivariate analysis of microbial community structure in an environmental context. Microbial diversity is measured by sequencing homologous genes, typically the 16S rRNA, through the next-generation sequencing platforms. We extract the abundances of the observed taxa by classifying the sequences and then investigate the correlations between diversity patterns and environmental parameters.
Abstract
R Scripts
Some results
generateCCAAdonis.R
1University of Glasgow, 2University of Amsterdam
Umer Zeeshan Ijaz1, Rob Van Son2, and Christopher Quince1
Microbial 16S rRNA Taxonomic Analysis Pipeline
ninput SPE.csv ENV.csv noutput <results_generated_here> nscripts _generateColors_.R generateCLUSPlot.R generateNMDSPlot.R generateBivariatePlot.R generateDissimilarityPlot.R generateOrdistep.R generateCCAAdonis.R generateDiversityIndices.R generateReadsBarPlot.R generateCCABIOENV.R generateEnvHeapMaps.R generateRichnessPlot.R generateCCAPlot.R generateNMDSEnvPlot.R transposeCSV.sh
Directory Structure
Code: http://userweb.eng.gla.ac.uk/umer.ijaz/Taxonomic_Scripts.tar.gz!User Manual: http://userweb.eng.gla.ac.uk/umer.ijaz/taxonomic_scripts_manual.pdf!
This script uses analysis of variance using distance matrices to find the best set of environmental parameters that describe the community structure. We have used adonis() func.on from the vegan library which fits linear models to distance matrices and uses a permuta.on test with pseudo F-‐ra.os. It also draws a CCA plot with only those environmental variables that are below a cut off P-‐value. Addi.onally, most abundant taxa are drawn on top of the CCA plot.
generateCCABIOENV.R This script is an extension of vegan library's bioenv() func.on and finds the best set of environmental parameters with maximum (rank) correla.on with the community dissimilari.es and plots them on CCA. It also finds the best subset of species and along with environmental parameters, plot them on on NMDS plots.
generateNMDSEnvPlot.R This script generates the NMDS plot with environmental parameters drawn on top as contours.
_generateColors_.R This is a general purpose parser for coloring sites. If the samplenames have underscores in them, then the names are separated on these underscores and the colors are then assigned automa.cally based on the uniqueness of string literals in a par.cular column before and ager the underscore. For example, given the following sample names NW_1_1 NW_1_2 NW_2_1 if one chooses colorColumn<-‐2 in the parameter sec.ons of the respec.ve scripts, then the color indices will be (1,1,2), if one choose colorColumn<-‐3 then the color indices will be (1,2,1).
generateReadsBarPlot.R This script generates a bar plot of reads for each sample in the species abundance file.
generateRichnessPlot.R This script generates mul.ple subplots for all the environmental parameters against species richness in a single plot. The species richness is rarefied to the minimum sample numbers and a correla.on test is performed between the rarefied richness and the environmental parameters. The resul.ng correla.on and their significance is drawn on top of each subplot. Currently, it has support for three correla.on measures: Pearson; Spearman; and Kendall. Furthermore this script also generates a *_RICHNESS_LOG.txt file that contains the summary stats of regression of rarefied richness against environmental parameters. The last column contains the P-‐values and if significant, it indicates that the richness is affected by this par.cular environmental parameter
generateBivariatePlot.R This script generates bivariate plots with histograms on the diagonals, scaler plots with smooth curves below the diagonals and correla.ons with significance levels above diagonals. Moreover, the variables are reordered in the plots with any two consecu.ve variables on the diagonal being most similar.
generateDissimilarityPlot.R
This script generates plots of a given dissimilarity measure between samples. Magenta is high similarity, and cyan is high dissimilarity. In the current version of the program, you can use the following dissimilarity measures: Bray-‐Cur.s dissimilarity matrix on raw species data; Bray-‐Cur.s dissimilarity matrix on log-‐transformed abundances; chord distance matrix; Hellinger distance matrix; and Chi-‐square pre-‐transforma.on followed by Euclidean distance.
generateNMDSPlot.R
This script generates the non-‐metric distance scaling (NMDS) plot for the species abundance file. It finds a non-‐parameteric monotonic rela.onship between the dissimilari.es in the samples matrix, and the loca.on of each item in a low-‐dimensional space.
generateCCAPlot.R This script performs canonical correspondence analysis (CCA) to find the rela.onship between species and their environment. The method extracts environmental gradients and then use them for describing and visualizing the preference of taxa/sample on an ordina.on diagram.
generateCLUSPlot.R This script generates the hierarchical clustering plot by using Bray-‐Cur.s as a dissimilarity index between samples.
generateDiversityIndices.R This script generates the ecological diversity indices and rarefac.on species richness. The following indices are supported: Shanon index; Simpson index; inverse Simpson’s index; Fisher’s logarithmic series’ alpha parameters; and Pielou’s evenness. Furthermore, it generates a csv file *_div.csv for these indices.
generateEnvHeapMaps.R This script generates the heap maps for the species abundance file with OTU/Taxa names on the x-‐axis and samples on the y-‐axis. Furthermore, each generated image is ordered by an environmental parameter (increases in value if you go down). As you move from leg to right the abundance of taxa decreases. The script automa.cally splits the images on 100 most abundant taxa.
generateOrdiStep.R This script is useful for iden.fying the environmental parameters that describe the community composi.on. It produces three text files: -‐*_STEP_automa.c_permua.on_LOG.txt: Automa.c model building based on Akaike informa.on criteria but based on permuta.on test using step func.on -‐*_ORDISTEP_automa.c_pvalue_LOG.txt: Automa.c model building based on Akaike informa.on criteria but based on permuta.on of P-‐values -‐*_ORDISTEP_manual_LOG.txt: Manual modeling
Figure 1: An example of species abundance file for mul.ple samples that is generated by denoising 16S rRNA sequences using AmpliconNoise (Quince et al. 2011) and classified using RDP classifier
Figure 2: An example of environmental parameters for mul.ple samples
Figure 3: Richness plot Figure 4: Dissimilarity plot
Figure 5: Bivariate plot Figure 6: Hierarchical clustering plot
Figure 7: Heap map
Figure 8: Diversity indices
Figure 9: NMDS plot with best subset of taxa and environmental parameters
Figure 10: CCA plot with all environmental parameters
Figure 11: NMDS plot with environmental parameter contours
Input to the pipeline
Further Developments The final aim of this work is to provide a web-‐based front-‐end by pre-‐packaging the scripts with a perl-‐CGI servlet that has the ability to run the scripts in the background and to display the results in a web brower. A preliminary version of the pipeline is hosted at hlp://quince-‐srv1.eng.gla.ac.uk:8080 with the interface as follows:
Acknowledgments This work is supported by a Technology Strategy Board (TSB) and Unilever funded research grant “Development of instrumental and bioinforma.c pipelines to accelerate commercial applica.ons of metagenomics approaches”.
Figure 12: Microbial Taxonomic Analysis Pipeline v0.2
