Shiny-phyloseq: Web Application for Interactive Microbiome Analysis - - PowerPoint PPT Presentation

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Shiny-phyloseq: Web Application for Interactive Microbiome Analysis - - PowerPoint PPT Presentation

Oct 28, 2023 131 likes •331 views

Shiny-phyloseq: Web Application for Interactive Microbiome Analysis with Provenance Tracking Paul J. McMurdie ! Research Associate ! Prof Susan Holmes Group ! Statistics Department ! Stanford University Overview Intro to Microbiome Research

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SLIDE 1

Paul J. McMurdie!

Research Associate! Prof Susan Holmes Group! Statistics Department!

Stanford University

Shiny-phyloseq: Web Application for Interactive Microbiome Analysis with Provenance Tracking

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SLIDE 2
  • Intro to Microbiome Research!
  • phyloseq - a microbiome BioC package!
  • (RNA-Seq methods solve a microbiome problem)!
  • Shiny-phyloseq: a shiny interface to phyloseq

Overview

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SLIDE 3

What are microbes?

Cell structure (they don’t all look like this)

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SLIDE 4

What are microbes?

http://en.wikipedia.org/wiki/Tree_of_life_(biology)

Bacteria Archaea Eukaryota

Ancestry of Life

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SLIDE 5
  • A population of a single species/strain is a culture,

extremely rare outside of lab, some infections!

  • A microbiome is a mixed population of different

microbial species (microbial ecosystem)

What is a microbiome?

The totality of microbes in a defined environment, especially their genomes and interactions with each

  • ther and surrounding environment.
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SLIDE 6

Cow Rumen Human Microbiomes Oceans, soils, waterways Wastewater Treatment

Why study microbiomes?

Deep-Sea Hydrothermal Vent Earth Microbiome Project:

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SLIDE 7

Human Body Sites, HMP

>10 times more microbial cells than human cells

!

Entire human microbiome weighs less than 2 kg, at most

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SLIDE 8

Fecal Transplants

Borody, et al (2011)! Nature Rev Gastroenterology &! Hepatology

(Clostridium diffjcile infection)

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SLIDE 9
  • Culture-based methods fail to detect most microbes!
  • Microbes are easy to miss (except pathogens)!
  • Most microbes are NOT pathogens (even the human-associated)

Bias for cultivable microbes, especially pathogens

  • PCR, fast & cheap DNA sequencing, microarrays, etc!
  • Discovery of culture-independent techniques - 16S-rRNA

Availability of tools limited to last 3 decades

Why is microbiome research new?

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SLIDE 10

ribosome 16S rRNA ribosome! in action

How do we query microbiomes??

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SLIDE 11
  • Universal (e.g. 16S rRNA) Gene census!
  • Shotgun Metagenome Sequencing!
  • Transcriptomics (shotgun mRNA)!
  • Proteomics (protein fragments)!
  • Metabolomics (excreted chemicals)

Number of Microbial Species Counted

How do we query microbiomes??

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SLIDE 12

Paul J. McMurdie!

!

Statistics Department! & CEHG! Stanford University!

!

with contributions from! Prof Susan Holmes

Microbiome data ! heterogeneity and processing

microbiome! samples amplify 16S rRNA! (barcoded) demultiplex and ! species clustering

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SLIDE 13

ape package

OTU Abundance

  • tu_table

Sample Variables sample_data Taxonomy Table taxonomyTable Phylogenetic Tree phylo

  • tu_table

sample_data tax_table phy_tree

  • tu_table

sample_data tax_table read.tree read.nexus read_tree as as as import phyloseq

constructor: Biostrings package

Reference Seq. XStringSet

DNAStringSet RNAStringSet AAStringSet

phyloseq Experiment Data

  • tu_table,

sam_data, tax_table, phy_tree refseq

Accessors:

get_taxa get_samples get_variable nsamples ntaxa rank_names sample_names sample_sums sample_variables taxa_names taxa_sums

Processors:

filter_taxa merge_phyloseq merge_samples merge_taxa prune_samples prune_taxa subset_taxa subset_samples tip_glom tax_glom

matrix matrix data.frame

  • ptional

refseq

data

data structure & API

phyloseq

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SLIDE 14

phyloseq

Preprocessing Import Direct Plots

plot_network plot_heatmap plot_ordination distance

  • rdinate

Summary / Exploratory Graphics

filter_taxa filterfun_sample genefilter_sample prune_taxa prune_samples subset_taxa subset_samples transform_sample_counts import_biom import_mothur import_pyrotagger import_qiime import_RDP

plot_tree plot_richness plot_bar

bootstrap permutation tests regression discriminant analysis multiple testing gap statistic clustering procrustes

Inference, Testing

sample data OTU cluster output

Input

raw

phyloseq

processed

work flow

phyloseq

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SLIDE 15

graphics

  • −0.3
−0.2 −0.1 0.0 0.1 0.2 0.3 −0.4 −0.2 0.0 0.2 0.4 NMDS1 NMDS2 SampleType
  • Feces
Freshwater Freshwater (creek) Mock Ocean Sediment (estuary) Skin Soil Tongue plot_ordination, NMDS, wUF Freshwater Freshwater (creek) Freshwater Freshwater (creek) Freshwater (creek) Soil Soil Soil Skin Skin Skin Mock Mock Mock Feces Feces Feces Feces Sediment (estuary) Tongue Tongue Ocean Ocean Ocean Sediment (estuary) Sediment (estuary) SampleType OTU 1 100 10000 Abundance plot_heatmap; bray−curtis, NMDS
  • ●
  • ●
  • ●
  • SeqTech
  • Illumina
Pyro454 Sanger Enterotype
  • 1
2 3 plot_network; Enterotype data, bray−curtis, max.dist=0.25
  • ●
  • ●
  • Cytophaga
Emticicia Sphingobacterium Segetibacter Haliscomenobacter Pedobacter Bacteroides Alistipes Bacteroides Cytophaga Porphyromonas Prevotella Parabacteroides Algoriphagus Odoribacter CandidatusAquirestis Capnocytophaga Porphyromonas Spirosoma Prevotella Balneola Prevotella Hymenobacter Prevotella
  • 76
73 75 75 79 67 81 84 84 82 75 Abundance
  • 1
25 625 15625 SampleType
  • Feces
Freshwater Freshwater (creek) Mock Ocean Sediment (estuary) Skin Soil Tongue Order
  • Bacteroidales
Flavobacteriales Sphingobacteriales plot_tree; Bacteroidetes−only. Merged samples, tip_glom=0.1 0e+00 2e+05 4e+05 6e+05 Feces Freshwater Freshwater (creek) Mock Ocean Sediment (estuary) Skin Soil Tongue SampleType Abundance Family Bacteroidaceae Balneolaceae Cryomorphaceae Cyclobacteriaceae Flavobacteriaceae Flexibacteraceae Porphyromonadaceae Prevotellaceae Rikenellaceae Saprospiraceae Sphingobacteriaceae plot_bar; Bacteroidetes−only
  • S.obs
S.chao1 S.ACE 2000 4000 6000 8000 FALSE TRUE FALSE TRUE FALSE TRUE Human Associated Samples Number of OTUs SampleType Feces Freshwater Freshwater (creek) Mock Ocean Sediment (estuary) Skin Soil Tongue

plot_ordination() plot_network() plot_bar() plot_heatmap() plot_tree() plot_richness()

phyloseq

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SLIDE 16

http://joey711.github.io/waste-not-supplemental/ edgeR, DESeq(2), metagenomeSeq! perform better than popular alternatives! in differential abundance detection:!

!

McMurdie and Holmes (2014) PLoS Comp Biol!

DOI: 10.1371/journal.pcbi.1003531

Side Note: BioC tools for microbiome

genes samples species samples

species counts gene counts

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SLIDE 17

Acknowledgements

Susan Holmes Wolfgang Huber BioC and CRAN

Helpful advice and feedback re: DESeq(2) Postdoc Advisor, Mentor, Co-author Support, Feedback, Distribution of phyloseq and biom

RStudio

Shiny, RStudio IDE

Hadley Wickham

ggplot2, reshape2, plyr R packages

Holmes Group

Helpful advice and feedback

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SLIDE 18

Shiny-phyloseq

Live Demo

install.packages(“shiny”) shiny::runGitHub(“shiny-phyloseq”, “joey711”)

How to Run: http://joey711.github.io/shiny-phyloseq/

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SLIDE 19

End.! Questions?