An analysis of Most frequent taxa in Major Depression and Bipolar Disorder
Fatima Zohra
31 March, 2022
capstoneanalysis_fatima.rmdSetup phase: Install and load relevant packages
install.packages("devtools")
devtools::install_github("waldronlab/bugSigSimple")
devtools::install_github("waldronlab/bugSigDB")Load bugSigSimple and bugSigDB
#library(bugSigDB)
library (bugSigSimple)Read curation file
dat <- bugsigdbr::importBugSigDB()## Using cached version from 2022-03-31 12:35:22
dim(dat)## [1] 2098 48Subset 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
## 17
## cervical cancer
## 4
## chronic hepatitis C virus infection
## 4
## chronic kidney disease
## 9
## chronic obstructive pulmonary disease
## 2
## colorectal cancer
## 7
## Crohn's disease
## 3
## endometrial cancer
## 4
## 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
## 6
## inflammatory bowel disease
## 2
## leukemia
## 4
## lung cancer
## 19
## multiple sclerosis
## 4
## necrotizing enterocolitis
## 3
## 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
## prostate cancer
## 2
## psychosis
## 4
## rheumatoid arthritis
## 2
## schizophrenia
## 20
## stimulus or stress design
## 3
## substance-related disorder
## 4
## ulcerative colitis
## 2
## unipolar depression
## 19
## vulvovaginitis
## 3
condsnew <-c("bipolar disorder","unipolar depression")
condsnew <-c("bipolar disorder")
efo <- bugsigdbr::getOntology("efo")## Loading required namespace: ontologyIndex## Using cached version from 2022-03-31 12:34:11
dat.bpd <- bugsigdbr::subsetByOntology(dat, column = "Condition", "bipolar disorder", efo)
dat.upd <- bugsigdbr::subsetByOntology(dat, column = "Condition", "unipolar depression", efo)
my.dat.cond <- rbind(dat.bpd, dat.upd)
table(my.dat.cond[,"Condition"])##
## bipolar disorder unipolar depression
## 18 23Overall frequencies of taxa increased and decreased in cases
getMostFrequentTaxa(my.dat.cond,n=30)##
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 7
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 6
## k__Bacteria|p__Actinobacteria
## 5
## k__Bacteria|p__Bacteroidetes
## 5
## k__Bacteria|p__Firmicutes
## 5
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 5
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 4
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 4
## k__Bacteria|p__Actinobacteria|c__Actinomycetia|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium
## 3
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales
## 3
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales|f__Pasteurellaceae
## 3
## k__Bacteria|p__Actinobacteria|c__Actinomycetia|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium adolescentis
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Atopobiaceae|g__Atopobium
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Gordonibacter
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Butyricimonas
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Porphyromonadaceae
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Tannerella
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Eubacteriaceae|g__Eubacterium
## 2
getMostFrequentTaxa(my.dat.cond,sig.type="increased")##
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 5
## k__Bacteria|p__Actinobacteria
## 4
## k__Bacteria|p__Firmicutes
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 4
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales
## 3
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae
## 3
getMostFrequentTaxa(my.dat.cond,sig.type="decreased")##
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 6
## k__Bacteria|p__Bacteroidetes
## 5
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 4
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 3
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Tannerella
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales|f__Pasteurellaceae
## 2Binomial 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] 23 48
table(my.dat.mdd[,"Condition"])##
## unipolar depression
## 23
getMostFrequentTaxa(my.dat.mdd, n=20)##
## k__Bacteria|p__Firmicutes
## 5
## k__Bacteria|p__Bacteroidetes
## 4
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 4
## k__Bacteria|p__Actinobacteria
## 3
## k__Bacteria|p__Actinobacteria|c__Actinomycetia|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium
## 3
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 3
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales
## 3
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales|f__Pasteurellaceae
## 3
## k__Bacteria|p__Actinobacteria|c__Actinomycetia|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium|s__Bifidobacterium adolescentis
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Atopobiaceae|g__Atopobium
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Eggerthellales|f__Eggerthellaceae|g__Gordonibacter
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Butyricimonas
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Porphyromonadaceae
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae|g__Clostridium
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Lachnospiraceae
## 2
getMostFrequentTaxa(my.dat.mdd,sig.type= "increased")##
## k__Bacteria|p__Firmicutes
## 4
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 3
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 3
## k__Bacteria|p__Actinobacteria
## 2
## k__Bacteria|p__Actinobacteria|c__Actinomycetia|o__Bifidobacteriales|f__Bifidobacteriaceae|g__Bifidobacterium
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Atopobiaceae|g__Atopobium
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Butyricimonas
## 2
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Porphyromonadaceae
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Flavonifractor
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Enterobacterales|f__Enterobacteriaceae
## 2
getMostFrequentTaxa(my.dat.mdd,sig.type= "decreased")##
## k__Bacteria|p__Bacteroidetes
## 4
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Ruminococcus
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales|f__Pasteurellaceae
## 2
## k__Bacteria|p__Proteobacteria|c__Gammaproteobacteria|o__Pasteurellales|f__Pasteurellaceae|g__Haemophilus
## 2
## k__Bacteria|p__Synergistetes|c__Synergistia|o__Synergistales|f__Synergistaceae|g__Pyramidobacter
## 2
## k__Bacteria|p__Actinobacteria
## 1Binomial 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] 18 48
table(my.dat.bd[,"Condition"])##
## bipolar disorder
## 18
getMostFrequentTaxa(my.dat.bd, n=20)##
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 3
## k__Bacteria|p__Actinobacteria
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 2
## k__Bacteria|p__Bacteroidetes
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides helcogenes
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Tannerella
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae|g__Streptococcus
## 1
getMostFrequentTaxa(my.dat.bd,sig.type= "increased")##
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales|f__Coriobacteriaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae
## 4
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Lactobacillaceae|g__Lactobacillus
## 4
## k__Bacteria|p__Actinobacteria
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia
## 2
## k__Bacteria|p__Actinobacteria|c__Coriobacteriia|o__Coriobacteriales
## 2
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 2
## k__Bacteria|p__Firmicutes|c__Bacilli
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales
## 1
## k__Bacteria|p__Firmicutes|c__Bacilli|o__Lactobacillales|f__Streptococcaceae
## 1
getMostFrequentTaxa(my.dat.bd,sig.type= "decreased")##
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae|g__Faecalibacterium
## 3
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Oscillospiraceae
## 2
## k__Bacteria|p__Bacteroidetes
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Bacteroidaceae|g__Bacteroides|s__Bacteroides helcogenes
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Odoribacteraceae|g__Odoribacter
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Prevotellaceae|g__Prevotella
## 1
## k__Bacteria|p__Bacteroidetes|c__Bacteroidia|o__Bacteroidales|f__Tannerellaceae|g__Tannerella
## 1
## k__Bacteria|p__Firmicutes|c__Clostridia|o__Eubacteriales|f__Clostridiaceae
## 1Binomial 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")