This example shows several ways of visualizing the results of functional metagenomic analyses. The discussion is based on two studies focusing on the metagenomic analysis of the human distal gut microbiome.

The human distal gut is the highest density, natural bacterial ecosystem known to date. Its size - up to 100 trillions cells - far exceeds the size of all the human body's other microbial communities. Recent studies have shown that the gut microbiota helps regulate energy balance, both by extracting calories from otherwise indigestible components, and by controlling the storage of energy in adipocytes. Furthermore, the gut microbiota is involved in a myriad of bioprocesses ranging from the synthesis of essential vitamins to the metabolism of carbohydrates, lipids and other xenobiotics that we ingest.

For this example, we will use two data sets. The first data set consists of data resulting from the analysis of the distal gut microbiome of two adult American subjects [1]. It comprises a phylogenetic survey of the microbial communities and a functional analysis of the metabolic functions represented by the identified gene pool. The variables included in `dataset1`

are described below. Note that the taxonomic assignments are represented as a nominal categorical array.

load gutmicrobiomedata.mat % === first data set variables rank1 = dataset1.rank1; % superkingdom assignments of each hit rank2 = dataset1.rank2; % phylum assignments of each hit rank3 = dataset1.rank3; % class assignments of each hit subjF = dataset1.subjF; % number of hits in female subject subjM = dataset1.subjM; % number of hits in male subject

We perform a taxonomic profiling of the first data set by considering the taxonomic assignment of the contigs according to the best BLASTX hit.

We start by computing the number of assigned hits that belong to each superkingdom for each subject.

l1 = getlabels(rank1); % superkingdom labels n1 = numel(l1); count1 = zeros(n1,1); % number of hits for each superkingdom and subject for i = 1:n1 obs = rank1 == l1{i}; count1(i,1) = sum(subjF(obs)); count1(i,2) = sum(subjM(obs)); end % === plot figure() barh(count1); colormap(summer); ax = gca; ax.YTickLabel = l1; xlabel('Number of hits'); title('Superkingdom assignment of best-BLASTX-hits'); legend('SubjF', 'SubjM');

As you can see from the bar plot, the microbial community living in the distal gut microbiome is prevalently of bacterial nature. The differences observed between the male and female subjects cannot be addressed due to the limited subject sample size and the possibility that these differences might be related to host genotype or lifestyle.

We now repeat the analysis at the phylum level.

l2 = getlabels(rank2); % phylum labels n2 = numel(l2); count2 = zeros(n2,1); % number of hits for each phylum and subject for i = 1:n2 obs = rank2 == l2{i}; count2(i,1) = sum(subjF(obs)); count2(i,2) = sum(subjM(obs)); end % === plot figure() barh(count2); colormap(summer); ax = gca; ax.YTick = 1:n2; ax.YTickLabel = l2; xlabel('Number of hits'); title('Phylum assignment of best-BLASTX-hits'); legend('SubjF', 'SubjM');

The bacterial phylotypes are assigned mostly to two divisions, the Firmicutes and the Actinobacteria. The relative paucity of Bacteroidetes assignments conflicts with data from other studies, but the discrepancy might be caused by the known biases of fecal lysis and DNA extraction methods used.

Finally, we perform the same steps on the assignments at the class level.

l3 = getlabels(rank3); % class labels n3 = numel(l3); count3 = zeros(n3,1); % number of hits for each class and subject for i = 1:n3 obs = rank3 == l3{i}; count3(i,1) = sum(subjF(obs)); count3(i,2) = sum(subjM(obs)); end % === plot figure(); barh(count3); colormap(summer); ax = gca; ax.YTick = 1:n3; ax.YTickLabel = l3; xlabel('Number of hits'); title('Class assignment of best-BLASTX-hits'); legend('SubjF', 'SubjM');

The taxonomic distribution at the class level reveals an abundance of bacterial phylotypes in the Clostridia and Bacilli groups, and also Actinobacteria and Methanobacteria.

You can combine the taxonomic distribution and the underlying taxonomic classification into a single representation by using a graph where each leaf node represents a class, and each internal node represents a phylum or a superkingdom.

To construct such a graph, we need to determine the connectivity matrix `CM`

representing the parent-child relationships among the nodes. We identify the phyla (children) belonging to each superkingdom (parent), and in turn the classes (children) belonging to each phylum (parent).

L = nominal([l1 l2 l3]); N = n1 + n2 + n3; CM = zeros(N, N); % connectivity matrix % === populate CM with relationships between superkingdoms and phyla for i = 1:n1 obs = rank1 == l1{i}; % entries classified in a given superkingdom from = find(L == l1{i}); % parent node subobs = unique(rank2(obs)); % phyla in a given superkigdom for j = 1:numel(subobs) to = find(L == subobs(j)); % child node CM(from, to) = 1; end end % === populate CM with relationships between phyla and classes for i = 1:n2 obs = rank2 == l2{i}; % entries classified in a given phylum from = find(L == l2{i}); from = from(end); subobs = unique(rank3(obs)); % classes in a given phylum for j = 1:numel(subobs) to = find(L == subobs(j)); to = to(end); CM(from, to) = 1; end end % === create biograph object bg = biograph(CM-diag(diag(CM)),[],'NodeAutoSize','off','ShowTextInNodes','Label');

The resulting graph has 60 nodes and 58 edges. Each level in the graph is associated with a given taxonomic rank, and the edges represent the underlying taxonomic classification. We can now label each node with the corresponding taxonomic assignment and rotate the entire graph counterclockwise by 90 degrees.

% === label each node set(bg.Nodes,'Size',[10 100]); for i = 1:numel(bg.Nodes) bg.Nodes(i).Label = char(L(i)); end dolayout(bg); % === rotate counterclockwise by 90 degrees for i = 1:numel(bg.Nodes) bg.Nodes(i).Position = fliplr(bg.Nodes(i).Position).*[-1 1]; bg.Nodes(i).Size = [100 15]; end % === redraw edges without changing node positions bg.LayoutType = 'equilibrium'; dolayout(bg,'PathsOnly',true); view(bg)

To include the distribution data in the graph, for each assignment we consider the average number of hits between the two subjects and the corresponding percentage. We then customize the color and size of each node. In particular, leaf nodes are represented with boxes, while internal nodes are represented with circles, and the size of each node is proportional to the number of hits (percentage) that fall within a given taxonomic assignment.

% === compute distribution among ranks count = [count1; count2; count3]; count = sum(count,2)/2; % avg between subjects pct = (count + 1)/sum(count + 1) * 100; % add pseudocounts % === determine color schema t = accumarray(round(pct+1),1); t(t>0) = 1:nnz(t); colors = flipud(summer(nnz(t))); cindex = t(round(pct+1)); % === customize color of nodes according to distribution for i = 1:numel(bg.Nodes) mynode = bg.Nodes(i); if (numel(getdescendants(mynode))~= 1) % leaf mynode.Shape = 'circle'; end mynode.Color = colors(cindex(i),:); end view(bg)

From this representation, you can immediately see how the majority of the microbial communities are composed of Bacteria, in particular Firmicutes, including Clostridia and Bacilli.

Phylogenetic assessments of microbial communities provide a starting point for interpreting the functional predictions from metagenomic data. The metabolic potential of the microbiota is studied to understand how the human distal gut microbiome provides us with physiological properties that we have not had to evolve on our own.

Here we consider the metabolic functions associated with the human distal gut microbiome through KEGG pathways assignments. We use odds ratios to rank the enrichment or depletion of KEGG categories with respect to reference genomic data sets, namely the *Homo sapiens* genome, a collection of sequenced bacterial genomes, and a collection of the sequenced archaeal genomes.

genome = dataset1.genome; % reference genomes considered keggCat = dataset1.keggCat; % KEGG category assignment keggData = dataset1.keggData; % odds ratio for each KEGG category relative to reference genomes

An odds ratio of one (corresponding to a log of zero) indicates that the microbial community had the same proportion of hits to a given category as the reference data set. An odds ratio greater than one (corresponding to a log greater than zero) indicates enrichment, whereas an odds ratio less than one (corresponding to a log less than zero) indicates under-representation with respect to the reference data set.

figure() hi = imagesc(log(keggData)); colormap(redbluecmap); colorbar; ha = gca; ha.XTick = 1:numel(genome); ha.XTickLabel = genome; ha.YTick = 1:numel(keggCat); ha.YTickLabel = keggCat;

From the heat map above, we notice that the human gut microbiome is highly enriched relative to the human genome, similar to the sequenced bacteria, and moderately enriched relative to the sequenced archaea.

COG categories, which use evolutionary relationships to group functionally related genes, can be used to perform functional analysis instead of KEGG categories, which map enzymes onto known metabolic pathways. The DataMatrix object `dm2`

consists of data resulting from a comparative metagenomic analysis of the human distal gut microbiome of several Japanese subjects, including infants, children and adults [2]. For reference, the data of American subjects considered above as well as other metagenomic data sets are reported. The rows represent the various COG observations, whereas the columns represent the various subject groups. The numeric data consists of normalized percentages of hits in a given COG category for a given subject group.

get(dm2)

Name: '' RowNames: {3868×1 cell} ColNames: {1×12 cell} NRows: 3868 NCols: 12 NDims: 2 ElementClass: 'double'

For each main COG category, we compute a cumulative normalized percentage and store the results in a new DataMatrix object named `dm2Count`

.

codes = {'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', ... 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V'}; % COG code to consider n = numel(codes); N = size(dm2,2); count = zeros(n,N); for i = 1:n try count(i,:) = sum(dm2.(codes{i})); catch sprintf('COG code %s is not found in the data set.',codes{i}); end end dm2Count = bioma.data.DataMatrix(count, codes, dm2.ColNames);

To investigate whether the COG enrichment patterns are different among the three age-related groups, we first consider the data associated with the adult, children and infant subjects.

group1 = {'Adult', 'Child', 'Infant'}; figure() plot(dm2Count.(':')(group1), '.-', 'LineWidth', 2); haxis = gca; haxis.XTick = 1:n; haxis.XTickLabel = codes; legend(group1, 'location', 'northwest') xlabel('COG categories'); ylabel('Normalized percentage of assigned genes');

We observe from this plot that adult subjects and children appear to have a similar pattern of enrichment in terms of COG categories. The infant subjects, on the other hand, display some singularities for categories G, K and L, corresponding to carbohydrate transport and metabolism, transcription, and replication respectively.

In light of this affinity between adult and child microbiome functional patterns, we consider a combination of the two samples (Adult+Child) when performing a comparison against other environmental sample microbiomes.

group2 = {'Adult+Child', 'Soil', 'Whale Fall Ave.', 'Sargasso'}; figure() plot(dm2Count.(':')(group2), '.-', 'LineWidth', 2); haxis = gca; haxis.XTick = 1:n; haxis.XTickLabel = codes; legend(group2, 'location', 'north'); xlabel('COG categories'); ylabel('Normalized percentage of assigned genes');

The most striking differences between the human microbiome enrichment pattern and those of other environmental microbial communities is related to COG category G (carbohydrate metabolism). This is perhaps related to the notion that the colonic microbiota utilizes otherwise indigestible polysaccharides and peptides as major resource for energy production and biosynthesis of cellular components. The enrichment of several enzymes for DNA repair is also noteworthy (COG category L).

A more effective way of visualizing the distribution of patterns of COG-assigned genes between each type of microbiome consists of plotting the enrichment values for each COG category along a circumference. For each data point, the distance from the center of the circle is proportional to the enrichment value.

r = dm2Count.(':')(group2); % colors = hsv(numel(group2)); colors = {'b', 'g', 'r', 'k'}; theta = (linspace(0,2*pi,n+1))'; figure(); hold on; for i = 1:numel(group2) rho = [r(:,i); r(1,i)]; plot(rho .* cos(theta), rho .* sin(theta), '-', 'Color', colors{i}, 'LineWidth', 2); end legend(group2, 'location', 'NorthEastOutside') % === plot outside circle and labels m = max(max(r)); for i = 1:n text( (m + .5) * cos(theta(i)), (m + .5) * sin(theta(i)), codes{i}, ... 'HorizontalAlignment', 'center'); end theta = (linspace(0,2*pi,100))'; plot(m * cos(theta), m * sin(theta), 'k-'); axis equal axis([-1 1 -1 1] * (m+1)) axis off

We can examine the relationship between the human gut microbiomes and other environmental microbiomes using the enrichment values for each COG. We create a hierarchical cluster tree using the complete linkage algorithm and the distance matrix generated by considering the correlation between data points. The samples considered include: Adult, Child, Infant, American, Soil, Whale fall (1, 2, and 3) and Sargasso.

group3 = {'Adult', 'Child', 'Infant', 'American (SubjF)', 'American (SubjM)', ... 'Soil', 'Whale Fall 1', 'Whale Fall 2', 'Whale Fall 3', 'Sargasso'}; z = linkage((dm2Count.(':')(group3))', 'complete', 'correlation'); dendrogram(z, 'orientation', 'left', 'labels', group3, 'colorthreshold', 'default')

The clustering analysis further shows that, while the adult and child microbiomes present similar profiles, those of infants have a distinct profile. Furthermore, some differences can be observed between the Japanese individuals and the American subjects. Finally, as expected, the human gut microbiome appears to be specific of the human species, and not related to the other environmental microbial communities.

[1] Gill, S., et al., "Metagenomic Analysis of the Human Distal Gut Microbiome", Science, 312(5778):1355-9, 2006.

[2] Kurokawa, K., et al., "Comparative Metagenomics Revealed Commonly Enriched Gene Sets in Human Gut Microbiomes", DNA Research, 14(4):169-81, 2007.

Was this topic helpful?