Basic report for blinded Beer total, ITS v.5

Report summary

Perform preprocessing of user data and analyze essential taxonomic and functional composition (without analysis of factors = meta-data about the samples).

Created	23/03/2020
Updated	30/04/2020
Type	Basic report
Project	blinded Beer total, ITS v.5
Uploaded samples	19

Data quality

Assessment of raw data quality.

Number of reads

Read quantity distribution

Number of the reads per sample before and after the quality filtering. Quality filtering (using split_libraries_fastq.py QIIME script) included: trimming of low-quality read ends (quality threshold = 20) and discarding of trimmed reads shorter than 75% of the initial length. Vertical line denotes minimal number of reads (3000 reads).

Download read_quant_distr.svg

Samples with low coverage

List of samples that had insufficient number of high-quality reads after the quality filtering (< 3000 reads) and were excluded from further analysis.

All samples passed the filter.

Read classification statistics

The reads were denoised using Deblur. Taxonomy was assigned using a scikit-learn naive Bayes machine-learning classifier from QIIME2. The classifier was trained on UNITE database version 7.2. No subsequent filtering by minimum fraction of mapped reads is performed.

Proportion of classified reads

Distribution of the successfully classified reads for each sample.

Download classif_reads_frac.svg

Samples with insufficient proportion of classified reads

Warning: there are samples with low proportion of classified reads (<70%). It is recommended to repeat the analysis by creating an additional project without including these samples.

All samples passed the filter.

Taxonomic composition

Heatmap of taxonomic composition

The interactive heatmap represents relative abundance of major microbial taxa (columns) in the samples (rows). Using the drop-down list “Heatmap settings” on the right of the heatmap, users can select taxonomic rank of interest. For convenience of comparison between close values, clicking on a cell “freezes” the displayed value of cell on the Legend and additionally the displayed abundance of top 10 taxa of corresponding sample (click again or on the cross near sample name to “unfreeze”). Use the Top control to change the way of major composition display between the top features in the selected sample and the top features across all samples on the average.

Major taxa

The boxplots represent distribution of relative abundance for 25 most abundant taxa across all samples (for each taxonomic rank). For proper display on log scale, zero values were replaced with a pseudocount not higher than minimum value of relative abundance of major taxa.

Complete taxonomic composition

The table contains relative abundance of all microbial taxa for each taxonomic rank.

Raw counts

Download raw_counts.xlsx

Percent

Download full_taxonomic_composition.xlsx

Taxonomic core

The plot represents the proportion of OTUs shared across the varying proportion of samples.

Download taxa_core.svg

Analysis of outliers

Automatic filtering of the user samples with extreme taxonomic composition (based on the combined analysis of user and external data). Analysis of outliers: samples in upper 1% tail of distribution of median distance between each sample and closest 50% of neighbours approximated by normal distribution. List of outliers:

No outliers detected.

PCoA visualization based on taxonomic composition

Distribution of the samples by their taxonomic composition in reduced dimensionality. The closer the samples (points) on the plot, the more similar their composition. Vectors show the directions in which the levels of the respective major taxa increase. Method of dimension reduction: PCoA (Principal Coordinate Analysis); dissimilarity metric: Bray-Curtis (at OTU level). Clicking on a dot “freezes” the detailed information about the sample on the right of the plot (click again or on the cross near sample name to “unfreeze”). Switch between the display modes with or without outliers and with or without vectors showing major microbial “drivers” using the respective controls.

Enterotypes

Enterotyping (cluster analysis of samples by their composition) was performed according to the original protocol (Arumugam et al, 2011). The optimal number of clusters was determined according to the highest Calinski-Harabasz index. Silhouette width is a measure of the clustering quality. For each of the enterotypes, there is a list of its drivers – microbial taxa distinguishing the samples belonging to the cluster from the other samples.

Number of enterotypes

2

Calinski-Harabasz index

36.185

Average silhouette width of the clusters

0.518

Microbial drivers

Enterotype name: Enterotype 1

Table

taxon	score
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetaceae;g__Saccharomyces	0.99
k__Fungi;p__Ascomycota;c__Dothideomycetes;o__Dothideales;f__Aureobasidiaceae;g__	0.53
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetaceae;g__Issatchenkia	0.39
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Debaryomycetaceae;g__Schwanniomyces	0.32
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetales_fam_Incertae_sedis;g__Starmerella	0.32
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycodaceae;g__Hanseniaspora	0.28
k__Fungi;p__Ascomycota;c__;o__;f__;g__	0.20
k__Fungi;p__Basidiomycota;c__Microbotryomycetes;o__Sporidiobolales;f__Sporidiobolaceae;g__Rhodotorula	0.08
k__Fungi;p__Basidiomycota;c__Tremellomycetes;o__Tremellales;f__Bulleribasidiaceae;g__Vishniacozyma	-0.03
k__Fungi;p__;c__;o__;f__;g__	-0.17

Enterotype name: Enterotype 2

Table

taxon	score
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Pichiaceae;g__Dekkera	0.79
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetales_fam_Incertae_sedis;g__Candida	0.30
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Pichiaceae;g__Pichia	0.27
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Dipodascaceae;g__Dipodascus	0.24
k__Fungi;p__Ascomycota;c__Dothideomycetes;o__Capnodiales;f__Mycosphaerellaceae;g__Mycosphaerella	0.24
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__;g__	0.21
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Metschnikowiaceae;g__Metschnikowia	0.20
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycodaceae;g__	0.18
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Saccharomycetales_fam_Incertae_sedis;g__	0.17
k__Fungi;p__Ascomycota;c__Saccharomycetes;o__Saccharomycetales;f__Dipodascaceae;g__	0.17

Hierarchical clustering

The tree shows clustering of the samples by similarity of their taxonomic composition at varying levels of detail. Dissimilarity measure: Bray-Curtis (at OTU level); linkage: Ward’s method.

Alpha-diversity

Static plots

Shannon index

Download shannon.svg

Chao1 index

Download chao.svg

Interactive plot

The measure describes the conditional number of taxa in each sample. Metric: Shannon index. Clicking on a dot “freezes” the displayed value on Y axis and additionally the abundance of top 10 taxa (click on it or on the cross near the sample name to “unfreeze”). In addition, the mean and confidence interval value appear when the mouse is over the boxplot. Controls at the top and bottom-right allow to change the displayed data.

Taxa co-occurence analysis

Co-occurence graph

Co-occurrence of microbial genera was analyzed basing on correlation analysis of their relative abundance using SPIEC-EASI software. In the graph, vertices show genera; pairs of highly co-occurring genera are connected with blue lines. The graph shows the members of the cooperatives - groups of highly co-occurring genera corresponding to isolated components (singleton vertices are omitted). Parameters of SPIEC-EASI algorithm: Meinshausen and Bühlmann neighbourhood selection method (MB), minimum lambda ratio= 0.1, number of lambda iterations = 20, model selection using StARS algorithm (number of StARS subsamples = 50).

No cooperatives detected. Possible reasons: too few or too many co-occurring taxa or insufficient number of samples to perform the analysis.

Members of the cooperatives

Cooperative content.

No cooperatives detected. Possible reasons: too few or too many co-occurring taxa or insufficient number of samples to perform the analysis.

Abundance of the cooperatives

Relative abundance of each cooperative in the samples.

No cooperatives detected. Possible reasons: too few or too many co-occurring taxa or insufficient number of samples to perform the analysis.

All features tables

All calculated features can be downloaded here.