An analysis of Most frequent taxa in Major Depression and Bipolar Disorder
Fatima Zohra
30 January, 2025
capstoneanalysis_fatima.rmdSetup phase: Install and load relevant packages
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install()
BiocManager::version()
BiocManager::install("remotes", dependencies = TRUE)
BiocManager::install("waldronlab/bugSigSimple")Load bugSigSimple and bugSigDB
#library(bugSigDB)
library (bugSigSimple)Read curation file
dat <- bugsigdbr::importBugSigDB()## Using cached version from 2025-01-30 21:57:36
dim(dat)## [1] 5520 50Subset all signatures by curator and condition
my.dat <- subsetByCurator(dat, curator="Fatima Zohra")
table(my.dat[,"Condition"])##
## Alcohol drinking
## 4
## Alzheimer's disease
## 2
## Anorexia nervosa
## 10
## Anxiety disorder
## 5
## Attention deficit hyperactivity disorder
## 6
## Autism
## 5
## Bipolar disorder
## 18
## Cervical cancer
## 4
## Chronic hepatitis C virus infection
## 4
## Chronic kidney disease
## 8
## Chronic obstructive pulmonary disease
## 2
## Colorectal cancer
## 7
## Crohn's disease
## 3
## Endometrial cancer
## 2
## Epilepsy
## 2
## Gastric adenocarcinoma
## 4
## Gastric cancer
## 13
## Genital neoplasm, female
## 2
## Gestational diabetes
## 2
## Hepatitis, Alcoholic
## 8
## Hepatocellular carcinoma
## 4
## HIV infection
## 17
## Human papilloma virus infection
## 4
## Human papilloma virus infection,Age
## 2
## Inflammatory bowel disease
## 2
## Leukemia
## 4
## Lung cancer
## 16
## Multiple sclerosis
## 4
## Necrotizing enterocolitis
## 2
## Non-alcoholic fatty liver disease
## 2
## Obsessive-compulsive disorder
## 1
## Ovarian cancer
## 4
## Pancreatic carcinoma
## 8
## Parkinson's disease
## 2
## Pneumonia
## 2
## Polycystic ovary syndrome
## 6
## Postmenopausal
## 1
## Psychosis
## 4
## Rheumatoid arthritis
## 2
## Sampling site
## 1
## Schizophrenia
## 19
## Stimulus or stress design
## 3
## Substance-related disorder
## 4
## Ulcerative colitis
## 2
## Unipolar depression
## 17
## Vulvovaginitis
## 3
condsnew <-c("bipolar disorder","major depressive disorder")
condsnew <-c("bipolar disorder")
efo <- bugsigdbr::getOntology("efo")## Loading required namespace: ontologyIndex## Using cached version from 2025-01-30 21:55:38
dat.bpd <- bugsigdbr::subsetByOntology(dat, column = "Condition", "bipolar disorder", efo)
dat.upd <- bugsigdbr::subsetByOntology(dat, column = "Condition", "major depressive disorder", efo)
my.dat.cond <- rbind(dat.bpd, dat.upd)
table(my.dat.cond[,"Condition"])##
## Bipolar disorder Major depressive disorder
## 21 13Overall frequencies of taxa increased and decreased in cases
getMostFrequentTaxa(my.dat.cond,n=30)##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae
## 3
## k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae
## 3
## k__Bacteria|p__Actinomycetota
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Eggerthella
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium sp.
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Anaerostipes
## 2
## k__Bacteria|p__Bacillota|c__Erysipelotrichia|o__Erysipelotrichales|f__Turicibacteraceae|g__Turicibacter
## 2
## k__Bacteria|p__Bacillota|c__Negativicutes|o__Acidaminococcales|f__Acidaminococcaceae
## 2
## k__Bacteria|p__Bacillota|c__Negativicutes|o__Acidaminococcales|f__Acidaminococcaceae|g__Phascolarctobacterium
## 2
## k__Bacteria|p__Bacillota|c__Negativicutes|o__Selenomonadales|f__Selenomonadaceae|g__Megamonas
## 2
## k__Bacteria|p__Bacillota|c__Negativicutes|o__Veillonellales|f__Veillonellaceae|g__Veillonella
## 2
## k__Bacteria|p__Bacteroidota
## 2
## k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 2
## k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 2
## k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Actinomycetales|f__Actinomycetaceae
## 1
getMostFrequentTaxa(my.dat.cond,, direction="increased")##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3
getMostFrequentTaxa(my.dat.cond,, direction="decreased")##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3Binomial test “Increased in Cases”
binom.test(x=4, n=6)##
## Exact binomial test
##
## data: 4 and 6
## number of successes = 4, number of trials = 6, p-value = 0.6875
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2227781 0.9567281
## sample estimates:
## probability of success
## 0.6666667
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=3, n=4)##
## Exact binomial test
##
## data: 3 and 4
## number of successes = 3, number of trials = 4, p-value = 0.625
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1941204 0.9936905
## sample estimates:
## probability of success
## 0.75
binom.test(x=3, n=5)##
## Exact binomial test
##
## data: 3 and 5
## number of successes = 3, number of trials = 5, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1466328 0.9472550
## sample estimates:
## probability of success
## 0.6
binom.test(x=3, n=3)##
## Exact binomial test
##
## data: 3 and 3
## number of successes = 3, number of trials = 3, p-value = 0.25
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2924018 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=3, n=3)##
## Exact binomial test
##
## data: 3 and 3
## number of successes = 3, number of trials = 3, p-value = 0.25
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2924018 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=3, n=4)##
## Exact binomial test
##
## data: 3 and 4
## number of successes = 3, number of trials = 4, p-value = 0.625
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1941204 0.9936905
## sample estimates:
## probability of success
## 0.75Binomial test “Decreased in Cases”
binom.test(x=7, n=8)##
## Exact binomial test
##
## data: 7 and 8
## number of successes = 7, number of trials = 8, p-value = 0.07031
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.4734903 0.9968403
## sample estimates:
## probability of success
## 0.875
binom.test(x=6, n=6)##
## Exact binomial test
##
## data: 6 and 6
## number of successes = 6, number of trials = 6, p-value = 0.03125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.5407419 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=3, n=5)##
## Exact binomial test
##
## data: 3 and 5
## number of successes = 3, number of trials = 5, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1466328 0.9472550
## sample estimates:
## probability of success
## 0.6
binom.test(x=3, n=5)##
## Exact binomial test
##
## data: 3 and 5
## number of successes = 3, number of trials = 5, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1466328 0.9472550
## sample estimates:
## probability of success
## 0.6
binom.test(x=2, n=6)##
## Exact binomial test
##
## data: 2 and 6
## number of successes = 2, number of trials = 6, p-value = 0.6875
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.04327187 0.77722190
## sample estimates:
## probability of success
## 0.3333333
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=3)##
## Exact binomial test
##
## data: 2 and 3
## number of successes = 2, number of trials = 3, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.09429932 0.99159624
## sample estimates:
## probability of success
## 0.6666667
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1Adjusting p value by Bonferroni correction
pvals <- c(0.6875, 0.125, 0.375, 0.625, 1, 0.25, 0.07031, 0.03125, 0.6875, 0.5)
adj.pvals <- p.adjust(pvals, method="bonferroni")
adj.pvals## [1] 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7031 0.3125 1.0000 1.0000Subset only on bipolar disorder
ind <- my.dat.cond[,"Condition"] %in% c("bipolar disorder")
my.dat.mdd <- my.dat.cond[!ind,]
dim(my.dat.mdd)## [1] 34 50
table(my.dat.mdd[,"Condition"])##
## Bipolar disorder Major depressive disorder
## 21 13
getMostFrequentTaxa(my.dat.mdd, n= 20)##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae
## 3
## k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae
## 3
## k__Bacteria|p__Actinomycetota
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Eggerthella
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium sp.
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 2
getMostFrequentTaxa(my.dat.mdd,, "increased")##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Actinomycetota
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae
## 2
## k__Bacteria|p__Actinomycetota|c__Actinomycetes|o__Actinomycetales|f__Actinomycetaceae|g__Actinomyces|s__Actinomyces bouchesdurhonensis
## 1
getMostFrequentTaxa(my.dat.mdd,, "decreased")##
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 3
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 2
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 2
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium sp.
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 2Binomial test “Increased in bipolar”
binom.test(x=3, n=3)##
## Exact binomial test
##
## data: 3 and 3
## number of successes = 3, number of trials = 3, p-value = 0.25
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2924018 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=3, n=3)##
## Exact binomial test
##
## data: 3 and 3
## number of successes = 3, number of trials = 3, p-value = 0.25
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2924018 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=3, n=4)##
## Exact binomial test
##
## data: 3 and 4
## number of successes = 3, number of trials = 4, p-value = 0.625
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1941204 0.9936905
## sample estimates:
## probability of success
## 0.75
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=3)##
## Exact binomial test
##
## data: 2 and 3
## number of successes = 2, number of trials = 3, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.09429932 0.99159624
## sample estimates:
## probability of success
## 0.6666667
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=3)##
## Exact binomial test
##
## data: 2 and 3
## number of successes = 2, number of trials = 3, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.09429932 0.99159624
## sample estimates:
## probability of success
## 0.6666667
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1Binomial test “decreased in bipolar”
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=3, n=5)##
## Exact binomial test
##
## data: 3 and 5
## number of successes = 3, number of trials = 5, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1466328 0.9472550
## sample estimates:
## probability of success
## 0.6
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1Adjusting p value by FDR correction for BD
pvals <- c(0.6875, 0.125, 0.375, 0.625, 1, 0.25, 0.07031, 0.03125, 0.6875, 0.5)
adj.pvals <- p.adjust(pvals, method="fdr")
adj.pvals## [1] 0.7638889 0.4166667 0.7500000 0.7638889 1.0000000 0.6250000 0.3515500
## [8] 0.3125000 0.7638889 0.7638889Subset only on MDD
ind <- my.dat.cond[,"Condition"] %in% c("unipolar depression")
my.dat.bd <- my.dat.cond[!ind,]
dim(my.dat.bd)## [1] 34 50
table(my.dat.bd[,"Condition"])##
## Bipolar disorder Major depressive disorder
## 21 13
getMostFrequentTaxa(my.dat.bd, n=20)##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae
## 3
## k__Bacteria|p__Bacteroidota|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae
## 3
## k__Bacteria|p__Actinomycetota
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Eggerthella
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium|s__Clostridium sp.
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
## 2
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 2
getMostFrequentTaxa(my.dat.bd,, direction="increased")##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3
getMostFrequentTaxa(my.dat.bd,, direction="decreased")##
## k__Bacteria|p__Actinomycetota|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 6
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 5
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Lachnospirales|f__Lachnospiraceae|g__Roseburia
## 4
## k__Bacteria|p__Bacillota|c__Clostridia|o__Peptostreptococcales|f__Peptostreptococcaceae
## 4
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 3
## k__Bacteria|p__Bacillota|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Bacillota|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 3Binomial test increased in MDD w/out MHT
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=3)##
## Exact binomial test
##
## data: 2 and 3
## number of successes = 2, number of trials = 3, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.09429932 0.99159624
## sample estimates:
## probability of success
## 0.6666667
binom.test(x=1, n=2)##
## Exact binomial test
##
## data: 1 and 2
## number of successes = 1, number of trials = 2, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.01257912 0.98742088
## sample estimates:
## probability of success
## 0.5Binomial test decreased in MDD w/out MHT
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=4, n=4)##
## Exact binomial test
##
## data: 4 and 4
## number of successes = 4, number of trials = 4, p-value = 0.125
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.3976354 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=4, n=5)##
## Exact binomial test
##
## data: 4 and 5
## number of successes = 4, number of trials = 5, p-value = 0.375
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.2835821 0.9949492
## sample estimates:
## probability of success
## 0.8
binom.test(x=3, n=5)##
## Exact binomial test
##
## data: 3 and 5
## number of successes = 3, number of trials = 5, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1466328 0.9472550
## sample estimates:
## probability of success
## 0.6
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=4)##
## Exact binomial test
##
## data: 2 and 4
## number of successes = 2, number of trials = 4, p-value = 1
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.06758599 0.93241401
## sample estimates:
## probability of success
## 0.5
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1
binom.test(x=2, n=2)##
## Exact binomial test
##
## data: 2 and 2
## number of successes = 2, number of trials = 2, p-value = 0.5
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
## 0.1581139 1.0000000
## sample estimates:
## probability of success
## 1Faecalibacterium plot
taxa.mdd <- c(up=1/7, down=4/7)
taxa.bd <- c(up=0/4, down=3/4)
barplot(rbind(taxa.mdd, taxa.bd), beside=TRUE, col=c("red","blue"), legend=TRUE, args.legend=list(x="topleft", legend=c("major depressive disorder", "bipolar disorder")), ylab="relative frequency", main="Frequency of Faecalibacterium")
Actinobacteria plot
taxa.mdd <- c(up=2/7, down=2/7)
taxa.bd <- c(up=2/4, down=0/4)
barplot(rbind(taxa.mdd, taxa.bd), beside=TRUE, col=c("red","blue"), legend=TRUE, args.legend=list(x="topright", cex = 0.70, legend=c("major depressive disorder", "bipolar disorder")), ylab="relative frequency", main="Frequency of Actinobacteria")
Bacteroides plot
taxa.mdd <- c(up=2/7, down=2/7)
taxa.bd <- c(up=0/4, down=2/4)
barplot(rbind(taxa.mdd, taxa.bd), beside=TRUE, col=c("red","blue"), legend=TRUE, args.legend=list(x="topleft", cex = 0.80, legend=c("major depressive disorder", "bipolar disorder")), ylab="relative frequency", main="Frequency of Bacteroides")
Ruminococcaceae plot
taxa.mdd <- c(up=0/7, down=4/7)
taxa.bd <- c(up=0/4, down=2/4)
barplot(rbind(taxa.mdd, taxa.bd), beside=TRUE, col=c("red","blue"), legend=TRUE, args.legend=list(x="topleft", cex = 0.75, legend=c("major depressive disorder", "bipolar disorder")), ylab="relative frequency", main="Frequency of Ruminococcaceae")