Visualization and Reporting - iffatAGheyas/bioinformatics-tutorial-wiki GitHub Wiki

6.2.10 Visualization & Reporting

Effective visualization and reporting turn your analyses into compelling stories. In this section we cover three complementary approaches: Krona for interactive taxonomic sunbursts, phyloseq in R for publication-quality heat trees and barplots, and MicrobiomeAnalyst for rich web-based dashboards.

A. Krona Interactive Plots

Installation

conda install -c bioconda krona
# Or download from https://github.com/marbl/Krona

Prepare a Krona‐compatible table

Krona expects a tab-delimited file with counts by taxonomic path, e.g.:

SampleA    Bacteria;Proteobacteria;Gammaproteobacteria;Enterobacterales;Enterobacteriaceae;Escherichia    12345
SampleA    Bacteria;Firmicutes;Bacilli;Lactobacillales;Streptococcaceae;Streptococcus                  6789

Generate HTML

# Convert Kraken2 report to Krona input
cut -f2,3 kraken2_out/sampleA.bracken.tsv \
  | sed '1d' \
  | ktImportTaxonomy -o kraken2_out/sampleA.krona.html -

  • -o ...krona.html creates an interactive HTML you can open in any browser.

  • Zoom, search, and export SVG directly from the plot.

B. Phyloseq Heat Trees & Barplots (R)

Installation

# In R
install.packages("phyloseq")
BiocManager::install("metacoder")
install.packages("ggplot2")

Import Data

library(phyloseq)
# ASV/OTU table and taxonomy from your pipeline
ps <- phyloseq(
  import_biom("feature-table.biom"),
  import_qiime_sample_data("sample-metadata.tsv")
)

Stacked Barplots

library(ggplot2)
# Genus‐level relative abundance
ps_genus <- tax_glom(ps, taxrank = "Genus")
ps_rel   <- transform_sample_counts(ps_genus, function(x) x / sum(x))
plot_bar(ps_rel, fill="Genus") +
  facet_wrap(~Condition, scales="free_x") +
  theme_bw() +
  labs(y="Relative abundance")

Heat Trees

library(metacoder)
# Convert phyloseq to metacoder object
obj <- phyloseq_to_metacoder(ps)
# Calculate taxon counts
obj$data$otu_table$count <- calc_taxon_abund(obj,
    "otu_table", cols = colnames(otu_table(ps)))
# Plot heat tree
heat_tree(obj,
  node_size = n_obs,            # number of observations
  node_color = log10(n_obs + 1),# log10 of counts
  layout = "davidson-harel",
  title = "Taxonomic Heat Tree"
)
  • Save to file with ggsave("heat_tree.png", width=6, height=6, dpi=300).

C. MicrobiomeAnalyst Dashboards

Overview

MicrobiomeAnalyst is a web platform for multi-omics data that provides interactive plots, statistics, and functional profiling.

Workflow

  1. Upload your feature table (OTU/ASV counts) and metadata as CSV or BIOM.

  2. Select analyses: diversity, differential abundance, biomarker discovery, correlation networks.

  3. Customize plots: heatmaps, volcano plots, correlation networks.

  4. Export figures (PNG, PDF, SVG) and full reports.

  5. Tip: MicrobiomeAnalyst integrates popular R pack

Tip: MicrobiomeAnalyst integrates popular R packages under the hood (e.g. phyloseq, DESeq2), making it easy to generate publication-ready figures without coding.

Summary of Outputs

kraken2_out/sampleA.krona.html           # interactive sunburst
phyloseq_plots/barplot_genus.png         # stacked barplot
phyloseq_plots/heat_tree.png             # taxonomic heat tree
microbiomeanalyst_report.zip             # full HTML/PDF dashboard

With these tools, you can deliver both interactive web reports and static figures for publications or presentations.