Frans’s Analysis

Install needed packages

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("bugsigdbr")
devtools::install_github("waldronlab/bugSigSimple")
devtools::install_github("waldronlab/bugSigdbstats")
install.packages(c("tidyverse", "kableExtra"))
BiocManager::install("ComplexHeatmap")
BiocManager::install("InteractiveComplexHeatmap")

Setup and initial packages

library(bugsigdbr)
library(dplyr)
library(bugSigSimple)
library(tidyverse)
library(kableExtra)
library(ComplexHeatmap)
library(BugSigDBStats)
library(InteractiveComplexHeatmap)

Import BSDB data

bsdb <- bugsigdbr::importBugSigDB(version="10.5281/zenodo.5819260")

## Using cached version from 2022-01-24 21:34:32

Figure out number of unique antibiotics studies in BSDB

There are currently 21 antimicrobial studies

bsdb %>% filter(Condition=="parkinson's disease") %>% group_by(PMID) %>% summarize(PMID=first(PMID))

Modify CreateStudyTable to be more informative

createStudyTable <- function(dat) 
{
  studies <- data.frame(Study = paste0(str_extract(dat$Authors, 
                                                   "[A-Za-z]+[:space:]"), dat$Year), Condition = dat$Condition, 
                        Cases = dat$`Group 1 sample size`, Controls = dat$`Group 0 sample size`, 
                        `Study Design` = dat$`Study design`, Location = dat$`Location of subjects`)
  studies %>% group_by(Study) %>% summarize(Condition = first(Condition), 
                                            Cases = max(Cases), Controls = max(Controls), `Study Design` = first(Study.Design),
                                            Location = first(Location)) %>% 
    kbl() %>% kable_styling()
}

Create table of studies

park <- subsetByCondition(bsdb, "parkinson's disease")
createStudyTable(park)

Study	Condition	Cases	Controls	Study Design	Location
Barichella 2019	parkinson’s disease	193	113	case-control	Italy
Bedarf 2017	parkinson’s disease	30	28	case-control	Germany
Li 2019	parkinson’s disease	51	48	case-control	China
Mihaila 2019	parkinson’s disease	48	36	case-control	United States of America
Petrov 2017	parkinson’s disease	89	66	cross-sectional observational, not case-control	Russian Federation

Examine body sites

park$`Body site` %>% unique()

## [1] "feces"                "Major salivary gland"

Recode body sites to gut

park <- park %>% mutate(site = recode(`Body site`, "feces" = "Gut", "meconium" = "Gut"))
park$site %>% unique()

## [1] "Gut"                  "Major salivary gland"

Create binomial test taxonomic table

kableExtra::kbl(createTaxonTable(park, 10))

Taxon Name	Taxonomic Level	total_signatures	increased_signatures	decreased_signatures	Binomial Test pval	kingdom	phylum	class	order	family	genus	species	n_signatures	metaphlan_name
Lachnospiraceae	family	8	1	7	0.0700	Bacteria	Firmicutes	Clostridia	Eubacteriales	Lachnospiraceae	NA	NA	9	k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae
Verrucomicrobia	phylum	8	8	0	0.0078	Bacteria	Verrucomicrobia	NA	NA	NA	NA	NA	12	k__Bacteria|p__Verrucomicrobia
Akkermansia	genus	8	8	0	0.0078	Bacteria	Verrucomicrobia	Verrucomicrobiae	Verrucomicrobiales	Akkermansiaceae	Akkermansia	NA	8	k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Akkermansiaceae|g__Akkermansia
Verrucomicrobiaceae	family	8	8	0	0.0078	Bacteria	Verrucomicrobia	Verrucomicrobiae	Verrucomicrobiales	Verrucomicrobiaceae	NA	NA	8	k__Bacteria|p__Verrucomicrobia|c__Verrucomicrobiae|o__Verrucomicrobiales|f__Verrucomicrobiaceae
Lactobacillaceae	family	5	3	2	1.0000	Bacteria	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	NA	NA	6	k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
Parabacteroides	genus	4	4	0	0.1200	Bacteria	Bacteroidetes	Bacteroidia	Bacteroidales	Tannerellaceae	Parabacteroides	NA	4	k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Parabacteroides
Christensenellaceae	family	4	4	0	0.1200	Bacteria	Firmicutes	Clostridia	Eubacteriales	Christensenellaceae	NA	NA	5	k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Christensenellaceae
Enterobacteriaceae	family	4	4	0	0.1200	Bacteria	Proteobacteria	Gammaproteobacteria	Enterobacterales	Enterobacteriaceae	NA	NA	4	k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae
Lactobacillus	genus	3	2	1	1.0000	Bacteria	Firmicutes	Bacilli	Lactobacillales	Lactobacillaceae	Lactobacillus	NA	3	k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
Roseburia	genus	3	1	2	1.0000	Bacteria	Firmicutes	Clostridia	Eubacteriales	Lachnospiraceae	Roseburia	NA	3	k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae|g__Roseburia

Writing a scientific paper

Introduction

1. General issue (Antibiotics affect gut microbiome, disruptions to gut microbiome can affect health)
2. Specific issue (Taxonomic effects of abx on gut microbiome are not well understood)
3. What is missing (We need to know if abx change specific taxa in the gut microbiome)
4. What you did to fill in the gap? (Review of recent lit. examining role of abx on gut microbiome taxa)

Methods

• Describe steps of the study (finding papers, reading papers, curation, analysis)

Results

• Present findings and summarize them
• Study table and taxon table go here
• In text briefly summarize important aspects of these tables
• Future analyses: cluster analysis and Monte Carlo analysis

Discussion

• What was gained by this study
• What are the applications of these findings
• What are the limitations to this study (limited by published research)
• What can future studies do to enhance these findings

Conclusion

One paragraph summary of results and discussion