Reading data
Get bulk export from bugsigdb.org:
full.dat <- bugsigdbr::importBugSigDB(version = "devel", cache = FALSE)
dim(full.dat)## [1] 13573 51
colnames(full.dat)## [1] "BSDB ID" "Study"
## [3] "Study design" "PMID"
## [5] "DOI" "URL"
## [7] "Authors list" "Title"
## [9] "Journal" "Year"
## [11] "Keywords" "Experiment"
## [13] "Location of subjects" "Host species"
## [15] "Body site" "UBERON ID"
## [17] "Condition" "EFO ID"
## [19] "Group 0 name" "Group 1 name"
## [21] "Group 1 definition" "Group 0 sample size"
## [23] "Group 1 sample size" "Antibiotics exclusion"
## [25] "Sequencing type" "16S variable region"
## [27] "Sequencing platform" "Data transformation"
## [29] "Statistical test" "Significance threshold"
## [31] "MHT correction" "LDA Score above"
## [33] "Matched on" "Confounders controlled for"
## [35] "Pielou" "Shannon"
## [37] "Chao1" "Simpson"
## [39] "Inverse Simpson" "Richness"
## [41] "Signature page name" "Source"
## [43] "Curated date" "Curator"
## [45] "Revision editor" "Description"
## [47] "Abundance in Group 1" "MetaPhlAn taxon names"
## [49] "NCBI Taxonomy IDs" "State"
## [51] "Reviewer"Stripping illformed entries:
Curation output
Number of papers and signatures curated:
## [1] 1998
nrow(full.dat)## [1] 13573Publication date of the curated papers:
pmids <- pmids[!is.na(pmids)]
pubyear <- pmid2pubyear(pmids)
head(cbind(pmids, pubyear))
tab <- table(pubyear)
tab <- tab[order(as.integer(names(tab)))]
df <- data.frame(year = names(tab), papers = as.integer(tab))
ggbarplot(df, x = "year", y = "papers",
label = TRUE, fill = "steelblue",
ggtheme = theme_bw())Stripping empty signatures:
ind1 <- lengths(full.dat[["MetaPhlAn taxon names"]]) > 0
ind2 <- lengths(full.dat[["NCBI Taxonomy IDs"]]) > 0
dat <- full.dat[ind1 & ind2,]
nrow(dat)## [1] 13573Papers containing only empty UP and DOWN signatures (under curation?):
## character(0)Progress over time:
dat[,"Curated date"] <- as.character(lubridate::dmy(dat[,"Curated date"]))
plotProgressOverTime(dat)
plotProgressOverTime(dat, diff = TRUE)
Stratified by curator:
npc <- stratifyByCurator(dat)
plotCuratorStats(dat, npc)
Number of complete and revised signatures: Turned off because it’s way too long these days
Study stats
Study design
spl <- split(dat[["Study"]], dat[["Study design"]])
sds <- lapply(spl, unique)
sort(lengths(sds), decreasing = FALSE)## cross-sectional observational, not case-control,randomized controlled trial
## 1
## laboratory experiment,prospective cohort
## 1
## laboratory experiment,randomized controlled trial
## 1
## meta-analysis,prospective cohort
## 1
## prospective cohort,laboratory experiment
## 1
## randomized controlled trial,case-control
## 1
## time series / longitudinal observational,cross-sectional observational, not case-control
## 1
## case-control,prospective cohort
## 2
## cross-sectional observational, not case-control,laboratory experiment
## 2
## laboratory experiment,cross-sectional observational, not case-control
## 2
## meta-analysis,laboratory experiment
## 2
## prospective cohort,cross-sectional observational, not case-control
## 2
## randomized controlled trial,time series / longitudinal observational
## 2
## time series / longitudinal observational,case-control
## 2
## randomized controlled trial,laboratory experiment
## 3
## time series / longitudinal observational,prospective cohort
## 3
## laboratory experiment,time series / longitudinal observational
## 4
## meta-analysis,case-control
## 4
## time series / longitudinal observational,laboratory experiment
## 4
## case-control,meta-analysis
## 5
## laboratory experiment,case-control
## 6
## case-control,laboratory experiment
## 7
## case-control,time series / longitudinal observational
## 8
## meta-analysis
## 31
## randomized controlled trial
## 90
## time series / longitudinal observational
## 151
## prospective cohort
## 157
## laboratory experiment
## 222
## cross-sectional observational, not case-control
## 508
## case-control
## 781Experiment stats
Columns of the full dataset that describe experiments:
# Experiment ID
exp.cols <- c("Study", "Experiment")
# Subjects
sub.cols <- c("Host species",
"Location of subjects",
"Body site",
"Condition",
"Antibiotics exclusion",
"Group 0 sample size",
"Group 1 sample size")
# Lab analysis
lab.cols <- c("Sequencing type",
"16S variable region",
"Sequencing platform")
# Statistical analysis
stat.cols <- c("Statistical test",
"MHT correction",
"Significance threshold")
# Alpha diversity
div.cols <- c("Pielou",
"Shannon",
"Chao1",
"Simpson",
"Inverse Simpson",
"Richness")Restrict dataset to experiment information:
Subjects
Number of experiments for the top 10 categories for each subjects column:
## $`Host species`
##
## Homo sapiens Mus musculus Rattus norvegicus
## 6434 926 217
## Sus scrofa domesticus Canis lupus familiaris Not specified
## 140 134 42
## Macaca mulatta Ovis aries Bos taurus
## 33 28 24
## Macaca fascicularis
## 24
##
## $`Location of subjects`
##
## China United States of America Japan
## 3066 1163 309
## Germany Italy Denmark
## 200 190 183
## Spain South Korea Australia
## 178 159 150
## United Kingdom
## 137
##
## $`Body site`
##
## Feces Saliva Vagina
## 5187 443 173
## Subgingival dental plaque Caecum Oral cavity
## 135 91 83
## Nasopharynx Mouth Colon
## 80 67 65
## Rectum
## 56
##
## $Condition
##
## Diet Parkinson's disease Colorectal cancer
## 286 233 207
## Obesity COVID-19 Response to transplant
## 163 127 110
## Response to diet Polycystic ovary syndrome Type II diabetes mellitus
## 103 99 93
## Constipation
## 91
##
## $`Antibiotics exclusion`
##
## 3 months 1 month
## 1194 959
## 2 months 6 months
## 340 324
## 2 weeks 1 week
## 228 36
## Recent use of antibiotics currently on antibiotics
## 34 24
## 1 year more than 5 days during 6 months
## 22 19Proportions instead:
sub.tab <- lapply(sub.cols[1:5], tabCol, df = exps, n = 10, perc = TRUE)
names(sub.tab) <- sub.cols[1:5]
sub.tab## $`Host species`
##
## Homo sapiens Mus musculus Rattus norvegicus
## 0.77400 0.11100 0.02610
## Sus scrofa domesticus Canis lupus familiaris Not specified
## 0.01680 0.01610 0.00505
## Macaca mulatta Ovis aries Bos taurus
## 0.00397 0.00337 0.00289
## Macaca fascicularis
## 0.00289
##
## $`Location of subjects`
##
## China United States of America Japan
## 0.3690 0.1400 0.0372
## Germany Italy Denmark
## 0.0241 0.0229 0.0220
## Spain South Korea Australia
## 0.0214 0.0191 0.0181
## United Kingdom
## 0.0165
##
## $`Body site`
##
## Feces Saliva Vagina
## 0.62700 0.05350 0.02090
## Subgingival dental plaque Caecum Oral cavity
## 0.01630 0.01100 0.01000
## Nasopharynx Mouth Colon
## 0.00967 0.00810 0.00785
## Rectum
## 0.00677
##
## $Condition
##
## Diet Parkinson's disease Colorectal cancer
## 0.0353 0.0287 0.0255
## Obesity COVID-19 Response to transplant
## 0.0201 0.0157 0.0136
## Response to diet Polycystic ovary syndrome Type II diabetes mellitus
## 0.0127 0.0122 0.0115
## Constipation
## 0.0112
##
## $`Antibiotics exclusion`
##
## 3 months 1 month
## 0.34500 0.27700
## 2 months 6 months
## 0.09830 0.09370
## 2 weeks 1 week
## 0.06590 0.01040
## Recent use of antibiotics currently on antibiotics
## 0.00983 0.00694
## 1 year more than 5 days during 6 months
## 0.00636 0.00549Sample size:
ssize <- apply(exps[,sub.cols[6:7]], 2, summary)
ssize## Group 0 sample size Group 1 sample size
## Min. 0.0000 1.00000
## 1st Qu. 11.0000 10.00000
## Median 24.0000 21.00000
## Mean 401.2697 61.64559
## 3rd Qu. 49.0000 41.00000
## Max. 308633.0000 10413.00000
## NA's 1413.0000 1406.00000Lab analysis
Number of experiments for the top 10 categories for each lab analysis column:
## $`Sequencing type`
##
## 16S WMS PCR ITS / ITS2 18S
## 6585 1300 79 76 5
##
## $`16S variable region`
##
## 34 4 12 123 45 345 123456789 3
## 3111 1611 355 244 214 149 116 77
## 56 23456789
## 52 37
##
## $`Sequencing platform`
##
## Illumina Roche454
## 6826 339
## Ion Torrent RT-qPCR
## 279 156
## Nanopore PacBio Vega (VS)/Revio (RS)/Sequel II
## 70 45
## MGISEQ-2000 Human Intestinal Tract Chip
## 38 31
## BGISEQ-500 Sequencing Mass spectrometry
## 21 18Proportions instead:
lab.tab <- lapply(lab.cols, tabCol, df = exps, n = 10, perc = TRUE)
names(lab.tab) <- lab.cols
lab.tab## $`Sequencing type`
##
## 16S WMS PCR ITS / ITS2 18S
## 0.819000 0.162000 0.009820 0.009450 0.000622
##
## $`16S variable region`
##
## 34 4 12 123 45 345 123456789 3
## 0.50900 0.26300 0.05800 0.03990 0.03500 0.02440 0.01900 0.01260
## 56 23456789
## 0.00850 0.00605
##
## $`Sequencing platform`
##
## Illumina Roche454
## 0.86300 0.04290
## Ion Torrent RT-qPCR
## 0.03530 0.01970
## Nanopore PacBio Vega (VS)/Revio (RS)/Sequel II
## 0.00885 0.00569
## MGISEQ-2000 Human Intestinal Tract Chip
## 0.00481 0.00392
## BGISEQ-500 Sequencing Mass spectrometry
## 0.00266 0.00228Statistical analysis
Number of experiments for the top 10 categories for each statistical analysis column:
# Define the columns to analyze
stat.cols <- c("Statistical test", "MHT correction", "Significance threshold")
# Get top 10 counts
stat.tab.count <- lapply(stat.cols, tabCol, df = exps, n = 10)
names(stat.tab.count) <- stat.cols
# Get top 10 proportions
stat.tab.prop <- lapply(stat.cols, tabCol, df = exps, n = 10, perc = TRUE)
names(stat.tab.prop) <- stat.colsTop 10 Counts
count_df <- data.frame(
Name = names(stat.tab.count[["Statistical test"]]),
Count = as.vector(stat.tab.count[["Statistical test"]])
)
DT::datatable(
count_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top categories for: Statistical test"
)
count_df <- data.frame(
Name = names(stat.tab.count[["MHT correction"]]),
Count = as.vector(stat.tab.count[["MHT correction"]])
)
DT::datatable(
count_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top categories for: MHT correction"
)
count_df <- data.frame(
Value = names(stat.tab.count[["Significance threshold"]]),
Count = as.vector(stat.tab.count[["Significance threshold"]])
)
DT::datatable(
count_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top categories for: Significance threshold"
)Top 10 Proportions
prop_df <- data.frame(
Name = names(stat.tab.prop[["Statistical test"]]),
Proportion = as.vector(stat.tab.prop[["Statistical test"]])
)
DT::datatable(
prop_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top proportions for: Statistical test"
)
prop_df <- data.frame(
Name = names(stat.tab.prop[["MHT correction"]]),
Proportion = as.vector(stat.tab.prop[["MHT correction"]])
)
DT::datatable(
prop_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top proportions for: MHT correction"
)
prop_df <- data.frame(
Value = names(stat.tab.prop[["Significance threshold"]]),
Proportion = as.vector(stat.tab.prop[["Significance threshold"]])
)
DT::datatable(
prop_df,
rownames = FALSE,
options = list(pageLength = 10),
caption = "Top proportions for: Significance threshold"
)Alpha diversity
Overall distribution:
apply(exps[,div.cols], 2, table)## Pielou Shannon Chao1 Simpson Inverse Simpson Richness
## decreased 93 924 589 333 79 598
## increased 64 726 432 197 61 435
## unchanged 322 2691 1294 1034 277 1397Correspondence of Shannon diversity and Richness:
table(exps$Shannon, exps$Richness)##
## decreased increased unchanged
## decreased 328 15 73
## increased 16 216 68
## unchanged 126 115 1123Conditions with consistently increased or decreased alpha diversity:
tabDiv(exps, "Shannon", "Condition")## increased decreased
## Pulmonary tuberculosis 4 25
## Polycystic ovary syndrome 4 19
## Gastric cancer 6 20
## COVID-19 11 24
## Periodontitis 16 3
## Diet 19 31
## HIV infection 1 12
## Ulcerative colitis 1 12
## Clostridium difficile infection 10 0
## Crohn's disease 0 10
## Multiple sclerosis 2 12
## Obesity 7 17
## Systemic inflammatory response syndrome 5 15
## Alzheimer's disease 2 11
## Chronic constipation 9 0
## Human papilloma virus infection 10 1
## Gingivitis 1 9
## Pancreatic carcinoma 9 1
## Smoking behaviour measurement 8 0
## Age 6 13
## Dry eye syndrome 1 8
## Heart failure 0 7
## Lung cancer 2 9
## Mycobacterium tuberculosis 7 0
## Atopic eczema 5 11
## Autism spectrum disorder 7 1
## Cesarean section 6 0
## Graves disease 1 7
## Parkinson's disease 20 14
## Response to allogeneic hematopoietic stem cell transplant 0 6
## Spontaneous preterm birth 13 7
## Acute pancreatitis 0 5
## Aging 2 7
## Bronchiectasis 0 5
## Cervical cancer 5 0
## Constipation 8 3
## Epilepsy 5 0
## Helminthiasis 5 0
## Oxygen 5 0
## Response to antibiotic 2 7
## Species design 10 15
## Urinary tract infection 1 6
## Acute lymphoblastic leukemia 0 4
## Body mass index 0 4
## Chronic kidney disease 2 6
## Colitis 4 0
## Colorectal cancer 18 14
## Cystic fibrosis 0 4
## Ethnic group 3 7
## Food allergy 6 2
## Gestational diabetes 4 0
## Hepatocellular carcinoma 4 8
## Human immunodeficiency virus 0 4
## Inflammatory bowel disease 0 4
## Pregnancy 4 0
## Alcohol drinking 3 0
## Antimicrobial agent 7 10
## Atopic asthma 4 1
## Birth measurement 3 0
## Chronic periodontitis 3 0
## Cognitive impairment 1 4
## Environmental exposure measurement 3 0
## Extraction protocol 23 26
## Hematopoietic stem cell 0 3
## High fat diet 4 1
## Hypertension 7 4
## Hypothyroidism 0 3
## Male homosexuality 3 0
## Peri-Implantitis 3 0
## Response to antiviral drug 2 5
## SARS-CoV-2-related disease 0 3
## Schizophrenia 1 4
## Smoking behavior 10 13
## Traditional Chinese medicine type 2 5
## Tuberculosis 2 5
## Acne 0 2
## Age at assessment 3 1
## Breed 0 2
## Cervical glandular intraepithelial neoplasia 2 0
## Chronic obstructive pulmonary disease 4 2
## Compound based treatment 0 2
## Dental caries 2 0
## Eczema 0 2
## Endometrial cancer 4 2
## Environmental factor 7 5
## Esophageal adenocarcinoma 0 2
## Iron biomarker measurement 1 3
## Irritable bowel syndrome 5 7
## Lifestyle measurement 4 2
## Milk allergic reaction 2 0
## Non-alcoholic fatty liver disease 3 1
## Papillary thyroid carcinoma 2 0
## Phenotype 2 0
## Phenylketonuria 1 3
## Population 5 7
## Reproductive behaviour measurement 2 0
## Response to anti-tuberculosis drug 8 10
## Response to immunochemotherapy 3 1
## Response to opioid 3 1
## Response to transplant 10 8
## Sampling site 3 1
## Simian immunodeficiency virus infection 0 2
## Sleep duration 3 1
## Small for gestational age 0 2
## Smoking status measurement 2 0
## Squamous cell carcinoma 2 0
## Streptococcus pneumoniae 0 2
## Stroke 2 0
## Acute respiratory failure 6 5
## Age-related macular degeneration 0 1
## Air pollution 7 6
## Anxiety disorder 0 1
## Attention deficit-hyperactivity disorder 2 1
## Breast cancer 4 5
## Breastfeeding duration 2 3
## Chronic fatigue syndrome 0 1
## Chronic hepatitis B virus infection 0 1
## Clinical treatment 1 2
## Coccidiosis 0 1
## Delivery method 3 4
## Depressive disorder 1 2
## Diarrhea 3 4
## Endometriosis 2 3
## Esophageal cancer 1 2
## Esophageal squamous cell carcinoma 1 0
## Estradiol measurement 1 2
## Fasting 0 1
## Female infertility 2 1
## Fungal infectious disease 2 1
## Glaucoma 4 5
## Hypertrophy 1 0
## Infant 1 0
## Major depressive disorder 0 1
## Metastatic colorectal cancer 1 2
## Myocardial infarction 1 2
## Non-alcoholic steatohepatitis 1 2
## Obstructive sleep apnea 0 1
## Oral cavity carcinoma 0 1
## Oral squamous cell carcinoma 3 2
## Ovarian cancer 4 3
## Pneumonia 1 2
## Prediabetes syndrome 0 1
## Premature birth 1 0
## Prostate cancer 0 1
## Psoriasis 1 0
## Respiratory Syncytial Virus Infection 0 1
## Respiratory tract infectious disease 0 1
## Response to stress 1 0
## Response to vaccine 1 2
## Sample treatment protocol 1 0
## Sampling time 4 3
## Social interaction measurement 2 1
## Socioeconomic status 3 4
## Stimulus or stress design 0 1
## Timepoint 1 0
## Treatment 4 3
## Type I diabetes mellitus 0 1
## Vesicle membrane 3 2
## Vitiligo 0 1
## Abnormal stool composition 0 0
## Acute myeloid leukemia 1 1
## Alcohol use disorder measurement 2 2
## Arthritis 0 0
## Asthma 3 3
## Biological sex 1 1
## Bipolar disorder 0 0
## Celiac disease 0 0
## Chlamydia trachomatis 2 2
## Colon carcinoma 0 0
## Colorectal adenoma 2 2
## Contraception 0 0
## COVID-19 symptoms measurement 0 0
## Diabetes mellitus 1 1
## Diet measurement 0 0
## Disease progression measurement 0 0
## Exposure 1 1
## Gastric adenocarcinoma 0 0
## Glioma 0 0
## Head and neck squamous cell carcinoma 0 0
## Health study participation 3 3
## HIV mother to child transmission 0 0
## HIV-1 infection 1 1
## Hydroxyproline measurement 1 1
## Hypertension, pregnancy-induced 0 0
## Hypoxia 4 4
## Ischemic stroke 0 0
## Lactose intolerance 0 0
## Lung transplantation 2 2
## Metabolic process 0 0
## Neurodevelopmental delay 0 0
## Obsessive-compulsive disorder 0 0
## Oral lichen planus 3 3
## Oral mucositis 2 2
## Oxalate measurement 1 1
## Postpartum 0 0
## Psoriasis vulgaris 0 0
## Response to diet 5 5
## Response to dietary antigen 0 0
## Response to ketogenic diet 2 2
## Rheumatoid arthritis 5 5
## Sample collection protocol 0 0
## SARS coronavirus 0 0
## Social deprivation,Psychological measurement 0 0
## Transplant outcome measurement 0 0
## Type II diabetes mellitus 7 7
## Vaginal microbiome measurement 0 0
## Viral load 0 0
## Waist circumference 0 0
## unchanged
## Pulmonary tuberculosis 20
## Polycystic ovary syndrome 30
## Gastric cancer 27
## COVID-19 50
## Periodontitis 16
## Diet 85
## HIV infection 27
## Ulcerative colitis 5
## Clostridium difficile infection 1
## Crohn's disease 11
## Multiple sclerosis 32
## Obesity 68
## Systemic inflammatory response syndrome 4
## Alzheimer's disease 30
## Chronic constipation 12
## Human papilloma virus infection 28
## Gingivitis 12
## Pancreatic carcinoma 6
## Smoking behaviour measurement 9
## Age 12
## Dry eye syndrome 14
## Heart failure 2
## Lung cancer 8
## Mycobacterium tuberculosis 5
## Atopic eczema 72
## Autism spectrum disorder 12
## Cesarean section 16
## Graves disease 7
## Parkinson's disease 95
## Response to allogeneic hematopoietic stem cell transplant 0
## Spontaneous preterm birth 6
## Acute pancreatitis 3
## Aging 2
## Bronchiectasis 0
## Cervical cancer 6
## Constipation 13
## Epilepsy 5
## Helminthiasis 8
## Oxygen 0
## Response to antibiotic 23
## Species design 16
## Urinary tract infection 11
## Acute lymphoblastic leukemia 10
## Body mass index 3
## Chronic kidney disease 11
## Colitis 2
## Colorectal cancer 72
## Cystic fibrosis 3
## Ethnic group 6
## Food allergy 19
## Gestational diabetes 36
## Hepatocellular carcinoma 7
## Human immunodeficiency virus 6
## Inflammatory bowel disease 1
## Pregnancy 7
## Alcohol drinking 2
## Antimicrobial agent 25
## Atopic asthma 7
## Birth measurement 4
## Chronic periodontitis 8
## Cognitive impairment 15
## Environmental exposure measurement 4
## Extraction protocol 21
## Hematopoietic stem cell 3
## High fat diet 2
## Hypertension 8
## Hypothyroidism 4
## Male homosexuality 6
## Peri-Implantitis 2
## Response to antiviral drug 17
## SARS-CoV-2-related disease 5
## Schizophrenia 24
## Smoking behavior 21
## Traditional Chinese medicine type 8
## Tuberculosis 0
## Acne 3
## Age at assessment 1
## Breed 7
## Cervical glandular intraepithelial neoplasia 9
## Chronic obstructive pulmonary disease 5
## Compound based treatment 3
## Dental caries 4
## Eczema 10
## Endometrial cancer 3
## Environmental factor 18
## Esophageal adenocarcinoma 4
## Iron biomarker measurement 2
## Irritable bowel syndrome 28
## Lifestyle measurement 14
## Milk allergic reaction 5
## Non-alcoholic fatty liver disease 9
## Papillary thyroid carcinoma 10
## Phenotype 19
## Phenylketonuria 4
## Population 28
## Reproductive behaviour measurement 3
## Response to anti-tuberculosis drug 13
## Response to immunochemotherapy 3
## Response to opioid 2
## Response to transplant 34
## Sampling site 7
## Simian immunodeficiency virus infection 9
## Sleep duration 2
## Small for gestational age 6
## Smoking status measurement 4
## Squamous cell carcinoma 4
## Streptococcus pneumoniae 4
## Stroke 16
## Acute respiratory failure 0
## Age-related macular degeneration 4
## Air pollution 3
## Anxiety disorder 7
## Attention deficit-hyperactivity disorder 6
## Breast cancer 31
## Breastfeeding duration 5
## Chronic fatigue syndrome 4
## Chronic hepatitis B virus infection 5
## Clinical treatment 5
## Coccidiosis 4
## Delivery method 13
## Depressive disorder 8
## Diarrhea 7
## Endometriosis 14
## Esophageal cancer 2
## Esophageal squamous cell carcinoma 5
## Estradiol measurement 2
## Fasting 4
## Female infertility 3
## Fungal infectious disease 4
## Glaucoma 6
## Hypertrophy 4
## Infant 4
## Major depressive disorder 6
## Metastatic colorectal cancer 3
## Myocardial infarction 21
## Non-alcoholic steatohepatitis 8
## Obstructive sleep apnea 11
## Oral cavity carcinoma 7
## Oral squamous cell carcinoma 3
## Ovarian cancer 35
## Pneumonia 3
## Prediabetes syndrome 4
## Premature birth 6
## Prostate cancer 9
## Psoriasis 14
## Respiratory Syncytial Virus Infection 5
## Respiratory tract infectious disease 5
## Response to stress 9
## Response to vaccine 5
## Sample treatment protocol 4
## Sampling time 7
## Social interaction measurement 6
## Socioeconomic status 8
## Stimulus or stress design 4
## Timepoint 5
## Treatment 7
## Type I diabetes mellitus 10
## Vesicle membrane 1
## Vitiligo 4
## Abnormal stool composition 6
## Acute myeloid leukemia 4
## Alcohol use disorder measurement 4
## Arthritis 6
## Asthma 17
## Biological sex 6
## Bipolar disorder 5
## Celiac disease 6
## Chlamydia trachomatis 2
## Colon carcinoma 8
## Colorectal adenoma 10
## Contraception 5
## COVID-19 symptoms measurement 5
## Diabetes mellitus 9
## Diet measurement 8
## Disease progression measurement 5
## Exposure 4
## Gastric adenocarcinoma 8
## Glioma 5
## Head and neck squamous cell carcinoma 8
## Health study participation 36
## HIV mother to child transmission 8
## HIV-1 infection 3
## Hydroxyproline measurement 3
## Hypertension, pregnancy-induced 6
## Hypoxia 0
## Ischemic stroke 5
## Lactose intolerance 5
## Lung transplantation 2
## Metabolic process 7
## Neurodevelopmental delay 6
## Obsessive-compulsive disorder 5
## Oral lichen planus 4
## Oral mucositis 3
## Oxalate measurement 8
## Postpartum 5
## Psoriasis vulgaris 14
## Response to diet 46
## Response to dietary antigen 6
## Response to ketogenic diet 3
## Rheumatoid arthritis 16
## Sample collection protocol 9
## SARS coronavirus 6
## Social deprivation,Psychological measurement 12
## Transplant outcome measurement 11
## Type II diabetes mellitus 42
## Vaginal microbiome measurement 5
## Viral load 6
## Waist circumference 5
tabDiv(exps, "Shannon", "Condition", perc = TRUE)## increased decreased
## Pulmonary tuberculosis 0.082 0.510
## Polycystic ovary syndrome 0.075 0.360
## Gastric cancer 0.110 0.380
## COVID-19 0.130 0.280
## Periodontitis 0.460 0.086
## Diet 0.140 0.230
## HIV infection 0.025 0.300
## Ulcerative colitis 0.056 0.670
## Clostridium difficile infection 0.910 0.000
## Crohn's disease 0.000 0.480
## Multiple sclerosis 0.043 0.260
## Obesity 0.076 0.180
## Systemic inflammatory response syndrome 0.210 0.620
## Alzheimer's disease 0.047 0.260
## Chronic constipation 0.430 0.000
## Human papilloma virus infection 0.260 0.026
## Gingivitis 0.045 0.410
## Pancreatic carcinoma 0.560 0.062
## Smoking behaviour measurement 0.470 0.000
## Age 0.190 0.420
## Dry eye syndrome 0.043 0.350
## Heart failure 0.000 0.780
## Lung cancer 0.110 0.470
## Mycobacterium tuberculosis 0.580 0.000
## Atopic eczema 0.057 0.120
## Autism spectrum disorder 0.350 0.050
## Cesarean section 0.270 0.000
## Graves disease 0.067 0.470
## Parkinson's disease 0.160 0.110
## Response to allogeneic hematopoietic stem cell transplant 0.000 1.000
## Spontaneous preterm birth 0.500 0.270
## Acute pancreatitis 0.000 0.620
## Aging 0.180 0.640
## Bronchiectasis 0.000 1.000
## Cervical cancer 0.450 0.000
## Constipation 0.330 0.120
## Epilepsy 0.500 0.000
## Helminthiasis 0.380 0.000
## Oxygen 1.000 0.000
## Response to antibiotic 0.062 0.220
## Species design 0.240 0.370
## Urinary tract infection 0.056 0.330
## Acute lymphoblastic leukemia 0.000 0.290
## Body mass index 0.000 0.570
## Chronic kidney disease 0.110 0.320
## Colitis 0.670 0.000
## Colorectal cancer 0.170 0.130
## Cystic fibrosis 0.000 0.570
## Ethnic group 0.190 0.440
## Food allergy 0.220 0.074
## Gestational diabetes 0.100 0.000
## Hepatocellular carcinoma 0.210 0.420
## Human immunodeficiency virus 0.000 0.400
## Inflammatory bowel disease 0.000 0.800
## Pregnancy 0.360 0.000
## Alcohol drinking 0.600 0.000
## Antimicrobial agent 0.170 0.240
## Atopic asthma 0.330 0.083
## Birth measurement 0.430 0.000
## Chronic periodontitis 0.270 0.000
## Cognitive impairment 0.050 0.200
## Environmental exposure measurement 0.430 0.000
## Extraction protocol 0.330 0.370
## Hematopoietic stem cell 0.000 0.500
## High fat diet 0.570 0.140
## Hypertension 0.370 0.210
## Hypothyroidism 0.000 0.430
## Male homosexuality 0.330 0.000
## Peri-Implantitis 0.600 0.000
## Response to antiviral drug 0.083 0.210
## SARS-CoV-2-related disease 0.000 0.380
## Schizophrenia 0.034 0.140
## Smoking behavior 0.230 0.300
## Traditional Chinese medicine type 0.130 0.330
## Tuberculosis 0.290 0.710
## Acne 0.000 0.400
## Age at assessment 0.600 0.200
## Breed 0.000 0.220
## Cervical glandular intraepithelial neoplasia 0.180 0.000
## Chronic obstructive pulmonary disease 0.360 0.180
## Compound based treatment 0.000 0.400
## Dental caries 0.330 0.000
## Eczema 0.000 0.170
## Endometrial cancer 0.440 0.220
## Environmental factor 0.230 0.170
## Esophageal adenocarcinoma 0.000 0.330
## Iron biomarker measurement 0.170 0.500
## Irritable bowel syndrome 0.120 0.180
## Lifestyle measurement 0.200 0.100
## Milk allergic reaction 0.290 0.000
## Non-alcoholic fatty liver disease 0.230 0.077
## Papillary thyroid carcinoma 0.170 0.000
## Phenotype 0.095 0.000
## Phenylketonuria 0.120 0.380
## Population 0.120 0.180
## Reproductive behaviour measurement 0.400 0.000
## Response to anti-tuberculosis drug 0.260 0.320
## Response to immunochemotherapy 0.430 0.140
## Response to opioid 0.500 0.170
## Response to transplant 0.190 0.150
## Sampling site 0.270 0.091
## Simian immunodeficiency virus infection 0.000 0.180
## Sleep duration 0.500 0.170
## Small for gestational age 0.000 0.250
## Smoking status measurement 0.330 0.000
## Squamous cell carcinoma 0.330 0.000
## Streptococcus pneumoniae 0.000 0.330
## Stroke 0.110 0.000
## Acute respiratory failure 0.550 0.450
## Age-related macular degeneration 0.000 0.200
## Air pollution 0.440 0.380
## Anxiety disorder 0.000 0.120
## Attention deficit-hyperactivity disorder 0.220 0.110
## Breast cancer 0.100 0.120
## Breastfeeding duration 0.200 0.300
## Chronic fatigue syndrome 0.000 0.200
## Chronic hepatitis B virus infection 0.000 0.170
## Clinical treatment 0.120 0.250
## Coccidiosis 0.000 0.200
## Delivery method 0.150 0.200
## Depressive disorder 0.091 0.180
## Diarrhea 0.210 0.290
## Endometriosis 0.110 0.160
## Esophageal cancer 0.200 0.400
## Esophageal squamous cell carcinoma 0.170 0.000
## Estradiol measurement 0.200 0.400
## Fasting 0.000 0.200
## Female infertility 0.330 0.170
## Fungal infectious disease 0.290 0.140
## Glaucoma 0.270 0.330
## Hypertrophy 0.200 0.000
## Infant 0.200 0.000
## Major depressive disorder 0.000 0.140
## Metastatic colorectal cancer 0.170 0.330
## Myocardial infarction 0.042 0.083
## Non-alcoholic steatohepatitis 0.091 0.180
## Obstructive sleep apnea 0.000 0.083
## Oral cavity carcinoma 0.000 0.120
## Oral squamous cell carcinoma 0.380 0.250
## Ovarian cancer 0.095 0.071
## Pneumonia 0.170 0.330
## Prediabetes syndrome 0.000 0.200
## Premature birth 0.140 0.000
## Prostate cancer 0.000 0.100
## Psoriasis 0.067 0.000
## Respiratory Syncytial Virus Infection 0.000 0.170
## Respiratory tract infectious disease 0.000 0.170
## Response to stress 0.100 0.000
## Response to vaccine 0.120 0.250
## Sample treatment protocol 0.200 0.000
## Sampling time 0.290 0.210
## Social interaction measurement 0.220 0.110
## Socioeconomic status 0.200 0.270
## Stimulus or stress design 0.000 0.200
## Timepoint 0.170 0.000
## Treatment 0.290 0.210
## Type I diabetes mellitus 0.000 0.091
## Vesicle membrane 0.500 0.330
## Vitiligo 0.000 0.200
## Abnormal stool composition 0.000 0.000
## Acute myeloid leukemia 0.170 0.170
## Alcohol use disorder measurement 0.250 0.250
## Arthritis 0.000 0.000
## Asthma 0.130 0.130
## Biological sex 0.120 0.120
## Bipolar disorder 0.000 0.000
## Celiac disease 0.000 0.000
## Chlamydia trachomatis 0.330 0.330
## Colon carcinoma 0.000 0.000
## Colorectal adenoma 0.140 0.140
## Contraception 0.000 0.000
## COVID-19 symptoms measurement 0.000 0.000
## Diabetes mellitus 0.091 0.091
## Diet measurement 0.000 0.000
## Disease progression measurement 0.000 0.000
## Exposure 0.170 0.170
## Gastric adenocarcinoma 0.000 0.000
## Glioma 0.000 0.000
## Head and neck squamous cell carcinoma 0.000 0.000
## Health study participation 0.071 0.071
## HIV mother to child transmission 0.000 0.000
## HIV-1 infection 0.200 0.200
## Hydroxyproline measurement 0.200 0.200
## Hypertension, pregnancy-induced 0.000 0.000
## Hypoxia 0.500 0.500
## Ischemic stroke 0.000 0.000
## Lactose intolerance 0.000 0.000
## Lung transplantation 0.330 0.330
## Metabolic process 0.000 0.000
## Neurodevelopmental delay 0.000 0.000
## Obsessive-compulsive disorder 0.000 0.000
## Oral lichen planus 0.300 0.300
## Oral mucositis 0.290 0.290
## Oxalate measurement 0.100 0.100
## Postpartum 0.000 0.000
## Psoriasis vulgaris 0.000 0.000
## Response to diet 0.089 0.089
## Response to dietary antigen 0.000 0.000
## Response to ketogenic diet 0.290 0.290
## Rheumatoid arthritis 0.190 0.190
## Sample collection protocol 0.000 0.000
## SARS coronavirus 0.000 0.000
## Social deprivation,Psychological measurement 0.000 0.000
## Transplant outcome measurement 0.000 0.000
## Type II diabetes mellitus 0.120 0.120
## Vaginal microbiome measurement 0.000 0.000
## Viral load 0.000 0.000
## Waist circumference 0.000 0.000
## unchanged
## Pulmonary tuberculosis 0.410
## Polycystic ovary syndrome 0.570
## Gastric cancer 0.510
## COVID-19 0.590
## Periodontitis 0.460
## Diet 0.630
## HIV infection 0.680
## Ulcerative colitis 0.280
## Clostridium difficile infection 0.091
## Crohn's disease 0.520
## Multiple sclerosis 0.700
## Obesity 0.740
## Systemic inflammatory response syndrome 0.170
## Alzheimer's disease 0.700
## Chronic constipation 0.570
## Human papilloma virus infection 0.720
## Gingivitis 0.550
## Pancreatic carcinoma 0.380
## Smoking behaviour measurement 0.530
## Age 0.390
## Dry eye syndrome 0.610
## Heart failure 0.220
## Lung cancer 0.420
## Mycobacterium tuberculosis 0.420
## Atopic eczema 0.820
## Autism spectrum disorder 0.600
## Cesarean section 0.730
## Graves disease 0.470
## Parkinson's disease 0.740
## Response to allogeneic hematopoietic stem cell transplant 0.000
## Spontaneous preterm birth 0.230
## Acute pancreatitis 0.380
## Aging 0.180
## Bronchiectasis 0.000
## Cervical cancer 0.550
## Constipation 0.540
## Epilepsy 0.500
## Helminthiasis 0.620
## Oxygen 0.000
## Response to antibiotic 0.720
## Species design 0.390
## Urinary tract infection 0.610
## Acute lymphoblastic leukemia 0.710
## Body mass index 0.430
## Chronic kidney disease 0.580
## Colitis 0.330
## Colorectal cancer 0.690
## Cystic fibrosis 0.430
## Ethnic group 0.380
## Food allergy 0.700
## Gestational diabetes 0.900
## Hepatocellular carcinoma 0.370
## Human immunodeficiency virus 0.600
## Inflammatory bowel disease 0.200
## Pregnancy 0.640
## Alcohol drinking 0.400
## Antimicrobial agent 0.600
## Atopic asthma 0.580
## Birth measurement 0.570
## Chronic periodontitis 0.730
## Cognitive impairment 0.750
## Environmental exposure measurement 0.570
## Extraction protocol 0.300
## Hematopoietic stem cell 0.500
## High fat diet 0.290
## Hypertension 0.420
## Hypothyroidism 0.570
## Male homosexuality 0.670
## Peri-Implantitis 0.400
## Response to antiviral drug 0.710
## SARS-CoV-2-related disease 0.620
## Schizophrenia 0.830
## Smoking behavior 0.480
## Traditional Chinese medicine type 0.530
## Tuberculosis 0.000
## Acne 0.600
## Age at assessment 0.200
## Breed 0.780
## Cervical glandular intraepithelial neoplasia 0.820
## Chronic obstructive pulmonary disease 0.450
## Compound based treatment 0.600
## Dental caries 0.670
## Eczema 0.830
## Endometrial cancer 0.330
## Environmental factor 0.600
## Esophageal adenocarcinoma 0.670
## Iron biomarker measurement 0.330
## Irritable bowel syndrome 0.700
## Lifestyle measurement 0.700
## Milk allergic reaction 0.710
## Non-alcoholic fatty liver disease 0.690
## Papillary thyroid carcinoma 0.830
## Phenotype 0.900
## Phenylketonuria 0.500
## Population 0.700
## Reproductive behaviour measurement 0.600
## Response to anti-tuberculosis drug 0.420
## Response to immunochemotherapy 0.430
## Response to opioid 0.330
## Response to transplant 0.650
## Sampling site 0.640
## Simian immunodeficiency virus infection 0.820
## Sleep duration 0.330
## Small for gestational age 0.750
## Smoking status measurement 0.670
## Squamous cell carcinoma 0.670
## Streptococcus pneumoniae 0.670
## Stroke 0.890
## Acute respiratory failure 0.000
## Age-related macular degeneration 0.800
## Air pollution 0.190
## Anxiety disorder 0.880
## Attention deficit-hyperactivity disorder 0.670
## Breast cancer 0.780
## Breastfeeding duration 0.500
## Chronic fatigue syndrome 0.800
## Chronic hepatitis B virus infection 0.830
## Clinical treatment 0.620
## Coccidiosis 0.800
## Delivery method 0.650
## Depressive disorder 0.730
## Diarrhea 0.500
## Endometriosis 0.740
## Esophageal cancer 0.400
## Esophageal squamous cell carcinoma 0.830
## Estradiol measurement 0.400
## Fasting 0.800
## Female infertility 0.500
## Fungal infectious disease 0.570
## Glaucoma 0.400
## Hypertrophy 0.800
## Infant 0.800
## Major depressive disorder 0.860
## Metastatic colorectal cancer 0.500
## Myocardial infarction 0.880
## Non-alcoholic steatohepatitis 0.730
## Obstructive sleep apnea 0.920
## Oral cavity carcinoma 0.880
## Oral squamous cell carcinoma 0.380
## Ovarian cancer 0.830
## Pneumonia 0.500
## Prediabetes syndrome 0.800
## Premature birth 0.860
## Prostate cancer 0.900
## Psoriasis 0.930
## Respiratory Syncytial Virus Infection 0.830
## Respiratory tract infectious disease 0.830
## Response to stress 0.900
## Response to vaccine 0.620
## Sample treatment protocol 0.800
## Sampling time 0.500
## Social interaction measurement 0.670
## Socioeconomic status 0.530
## Stimulus or stress design 0.800
## Timepoint 0.830
## Treatment 0.500
## Type I diabetes mellitus 0.910
## Vesicle membrane 0.170
## Vitiligo 0.800
## Abnormal stool composition 1.000
## Acute myeloid leukemia 0.670
## Alcohol use disorder measurement 0.500
## Arthritis 1.000
## Asthma 0.740
## Biological sex 0.750
## Bipolar disorder 1.000
## Celiac disease 1.000
## Chlamydia trachomatis 0.330
## Colon carcinoma 1.000
## Colorectal adenoma 0.710
## Contraception 1.000
## COVID-19 symptoms measurement 1.000
## Diabetes mellitus 0.820
## Diet measurement 1.000
## Disease progression measurement 1.000
## Exposure 0.670
## Gastric adenocarcinoma 1.000
## Glioma 1.000
## Head and neck squamous cell carcinoma 1.000
## Health study participation 0.860
## HIV mother to child transmission 1.000
## HIV-1 infection 0.600
## Hydroxyproline measurement 0.600
## Hypertension, pregnancy-induced 1.000
## Hypoxia 0.000
## Ischemic stroke 1.000
## Lactose intolerance 1.000
## Lung transplantation 0.330
## Metabolic process 1.000
## Neurodevelopmental delay 1.000
## Obsessive-compulsive disorder 1.000
## Oral lichen planus 0.400
## Oral mucositis 0.430
## Oxalate measurement 0.800
## Postpartum 1.000
## Psoriasis vulgaris 1.000
## Response to diet 0.820
## Response to dietary antigen 1.000
## Response to ketogenic diet 0.430
## Rheumatoid arthritis 0.620
## Sample collection protocol 1.000
## SARS coronavirus 1.000
## Social deprivation,Psychological measurement 1.000
## Transplant outcome measurement 1.000
## Type II diabetes mellitus 0.750
## Vaginal microbiome measurement 1.000
## Viral load 1.000
## Waist circumference 1.000
tabDiv(exps, "Richness", "Condition")## increased decreased
## Diet 6 20
## Helminthiasis 13 0
## HIV infection 3 15
## Multiple sclerosis 2 14
## Periodontitis 15 3
## Polycystic ovary syndrome 5 16
## Pulmonary tuberculosis 6 16
## COVID-19 11 20
## Parkinson's disease 19 27
## Phenotype 9 1
## Small for gestational age 0 8
## Aging 0 7
## Chronic constipation 7 0
## Gastric cancer 5 12
## Gingivitis 1 8
## Species design 10 17
## Chronic kidney disease 0 6
## Increased intestinal transit time 6 0
## Response to allogeneic hematopoietic stem cell transplant 0 6
## Schizophrenia 1 7
## Alcohol drinking 5 0
## Antimicrobial agent 2 7
## Environmental factor 0 5
## Human immunodeficiency virus 1 6
## Human papilloma virus infection 7 2
## Acute lymphoblastic leukemia 5 1
## Air pollution 9 5
## Cervical glandular intraepithelial neoplasia 4 0
## Crohn's disease 2 6
## Diarrhea 5 1
## Dry eye syndrome 0 4
## Epilepsy 4 0
## Fungal infectious disease 0 4
## Major depressive disorder 4 0
## Stress-related disorder 0 4
## Ulcerative colitis 0 4
## Vesicle membrane 5 1
## Alzheimer's disease 8 5
## Atopic asthma 4 1
## Delivery method 4 1
## Endometriosis 4 1
## Food allergy 0 3
## Graves disease 2 5
## Hematopoietic stem cell 0 3
## Hypertension 1 4
## Hypertrophy 3 0
## Iron biomarker measurement 1 4
## Irritable bowel syndrome 4 7
## Oral squamous cell carcinoma 1 4
## Treatment 4 7
## Treatment outcome measurement 1 4
## Tuberculosis 2 5
## Alcohol use disorder measurement 2 0
## Bone mineral content measurement 0 2
## Breast cancer 2 0
## Cognitive impairment 0 2
## Colorectal cancer 16 18
## Esophageal adenocarcinoma 0 2
## Gestational diabetes 4 6
## Health study participation 2 0
## Hypothyroidism 0 2
## Inflammatory bowel disease 2 4
## Lung cancer 0 2
## Metabolic process 0 2
## Obesity 10 8
## Phenylketonuria 1 3
## Population 2 0
## Response to antibiotic 0 2
## Response to antiviral drug 0 2
## Response to diet 3 5
## Response to transplant 9 7
## Simian immunodeficiency virus infection 0 2
## Smoking behavior 6 8
## Smoking behaviour measurement 2 0
## Smoking status measurement 2 0
## Streptococcus pneumoniae 0 2
## Traditional Chinese medicine type 1 3
## Transplant outcome measurement 0 2
## Age 4 5
## Asthma 2 3
## Atopic eczema 2 1
## Autism spectrum disorder 5 6
## Bone density 0 1
## Breastfeeding duration 1 0
## Cesarean section 3 2
## Coccidiosis 0 1
## Colon carcinoma 0 1
## Colorectal adenoma 1 2
## Endometrial cancer 1 2
## Heart failure 2 3
## Infant 1 0
## Ischemic stroke 2 1
## Lifestyle measurement 3 2
## Neurodevelopmental delay 1 0
## Obsessive-compulsive disorder 0 1
## Ovarian cancer 1 0
## Pregnancy 1 0
## Psoriasis 0 1
## Reproductive behaviour measurement 1 0
## Response to stress 1 0
## Rheumatoid arthritis 3 4
## Sampling site 1 2
## Socioeconomic status 2 1
## Transport 1 2
## Type I diabetes mellitus 0 1
## Abnormal stool composition 0 0
## Attention deficit-hyperactivity disorder 0 0
## Chlamydia trachomatis 1 1
## Chronic periodontitis 0 0
## Constipation 7 7
## Depressive disorder 1 1
## Diabetes mellitus 0 0
## Diet measurement 0 0
## Ethnic group 2 2
## Glioma 1 1
## Head and neck squamous cell carcinoma 0 0
## Hepatocellular carcinoma 3 3
## HIV mother to child transmission 0 0
## Male homosexuality 0 0
## Myocardial infarction 0 0
## Non-alcoholic fatty liver disease 0 0
## Papillary thyroid carcinoma 0 0
## Physical activity 2 2
## Prostate cancer 0 0
## Psoriasis vulgaris 0 0
## Sample collection protocol 0 0
## Stroke 2 2
## Type II diabetes mellitus 4 4
## Urinary tract infection 1 1
## Viral load 0 0
## unchanged
## Diet 34
## Helminthiasis 0
## HIV infection 10
## Multiple sclerosis 29
## Periodontitis 23
## Polycystic ovary syndrome 16
## Pulmonary tuberculosis 9
## COVID-19 25
## Parkinson's disease 33
## Phenotype 11
## Small for gestational age 0
## Aging 1
## Chronic constipation 7
## Gastric cancer 14
## Gingivitis 14
## Species design 14
## Chronic kidney disease 7
## Increased intestinal transit time 0
## Response to allogeneic hematopoietic stem cell transplant 0
## Schizophrenia 14
## Alcohol drinking 0
## Antimicrobial agent 10
## Environmental factor 17
## Human immunodeficiency virus 2
## Human papilloma virus infection 12
## Acute lymphoblastic leukemia 1
## Air pollution 6
## Cervical glandular intraepithelial neoplasia 2
## Crohn's disease 4
## Diarrhea 3
## Dry eye syndrome 4
## Epilepsy 3
## Fungal infectious disease 2
## Major depressive disorder 2
## Stress-related disorder 1
## Ulcerative colitis 2
## Vesicle membrane 0
## Alzheimer's disease 28
## Atopic asthma 7
## Delivery method 9
## Endometriosis 8
## Food allergy 9
## Graves disease 6
## Hematopoietic stem cell 3
## Hypertension 8
## Hypertrophy 2
## Iron biomarker measurement 1
## Irritable bowel syndrome 16
## Oral squamous cell carcinoma 0
## Treatment 9
## Treatment outcome measurement 0
## Tuberculosis 0
## Alcohol use disorder measurement 6
## Bone mineral content measurement 8
## Breast cancer 17
## Cognitive impairment 9
## Colorectal cancer 22
## Esophageal adenocarcinoma 4
## Gestational diabetes 27
## Health study participation 29
## Hypothyroidism 4
## Inflammatory bowel disease 1
## Lung cancer 10
## Metabolic process 5
## Obesity 27
## Phenylketonuria 4
## Population 4
## Response to antibiotic 6
## Response to antiviral drug 13
## Response to diet 16
## Response to transplant 14
## Simian immunodeficiency virus infection 4
## Smoking behavior 8
## Smoking behaviour measurement 5
## Smoking status measurement 5
## Streptococcus pneumoniae 3
## Traditional Chinese medicine type 4
## Transplant outcome measurement 5
## Age 2
## Asthma 13
## Atopic eczema 6
## Autism spectrum disorder 0
## Bone density 5
## Breastfeeding duration 5
## Cesarean section 10
## Coccidiosis 4
## Colon carcinoma 9
## Colorectal adenoma 11
## Endometrial cancer 3
## Heart failure 4
## Infant 4
## Ischemic stroke 3
## Lifestyle measurement 3
## Neurodevelopmental delay 5
## Obsessive-compulsive disorder 4
## Ovarian cancer 39
## Pregnancy 7
## Psoriasis 8
## Reproductive behaviour measurement 4
## Response to stress 9
## Rheumatoid arthritis 3
## Sampling site 2
## Socioeconomic status 2
## Transport 3
## Type I diabetes mellitus 4
## Abnormal stool composition 6
## Attention deficit-hyperactivity disorder 5
## Chlamydia trachomatis 3
## Chronic periodontitis 6
## Constipation 12
## Depressive disorder 5
## Diabetes mellitus 5
## Diet measurement 7
## Ethnic group 1
## Glioma 3
## Head and neck squamous cell carcinoma 11
## Hepatocellular carcinoma 0
## HIV mother to child transmission 8
## Male homosexuality 9
## Myocardial infarction 19
## Non-alcoholic fatty liver disease 5
## Papillary thyroid carcinoma 12
## Physical activity 1
## Prostate cancer 7
## Psoriasis vulgaris 14
## Sample collection protocol 9
## Stroke 17
## Type II diabetes mellitus 21
## Urinary tract infection 9
## Viral load 5
tabDiv(exps, "Richness", "Condition", perc = TRUE)## increased decreased
## Diet 0.100 0.330
## Helminthiasis 1.000 0.000
## HIV infection 0.110 0.540
## Multiple sclerosis 0.044 0.310
## Periodontitis 0.370 0.073
## Polycystic ovary syndrome 0.140 0.430
## Pulmonary tuberculosis 0.190 0.520
## COVID-19 0.200 0.360
## Parkinson's disease 0.240 0.340
## Phenotype 0.430 0.048
## Small for gestational age 0.000 1.000
## Aging 0.000 0.880
## Chronic constipation 0.500 0.000
## Gastric cancer 0.160 0.390
## Gingivitis 0.043 0.350
## Species design 0.240 0.410
## Chronic kidney disease 0.000 0.460
## Increased intestinal transit time 1.000 0.000
## Response to allogeneic hematopoietic stem cell transplant 0.000 1.000
## Schizophrenia 0.045 0.320
## Alcohol drinking 1.000 0.000
## Antimicrobial agent 0.110 0.370
## Environmental factor 0.000 0.230
## Human immunodeficiency virus 0.110 0.670
## Human papilloma virus infection 0.330 0.095
## Acute lymphoblastic leukemia 0.710 0.140
## Air pollution 0.450 0.250
## Cervical glandular intraepithelial neoplasia 0.670 0.000
## Crohn's disease 0.170 0.500
## Diarrhea 0.560 0.110
## Dry eye syndrome 0.000 0.500
## Epilepsy 0.570 0.000
## Fungal infectious disease 0.000 0.670
## Major depressive disorder 0.670 0.000
## Stress-related disorder 0.000 0.800
## Ulcerative colitis 0.000 0.670
## Vesicle membrane 0.830 0.170
## Alzheimer's disease 0.200 0.120
## Atopic asthma 0.330 0.083
## Delivery method 0.290 0.071
## Endometriosis 0.310 0.077
## Food allergy 0.000 0.250
## Graves disease 0.150 0.380
## Hematopoietic stem cell 0.000 0.500
## Hypertension 0.077 0.310
## Hypertrophy 0.600 0.000
## Iron biomarker measurement 0.170 0.670
## Irritable bowel syndrome 0.150 0.260
## Oral squamous cell carcinoma 0.200 0.800
## Treatment 0.200 0.350
## Treatment outcome measurement 0.200 0.800
## Tuberculosis 0.290 0.710
## Alcohol use disorder measurement 0.250 0.000
## Bone mineral content measurement 0.000 0.200
## Breast cancer 0.110 0.000
## Cognitive impairment 0.000 0.180
## Colorectal cancer 0.290 0.320
## Esophageal adenocarcinoma 0.000 0.330
## Gestational diabetes 0.110 0.160
## Health study participation 0.065 0.000
## Hypothyroidism 0.000 0.330
## Inflammatory bowel disease 0.290 0.570
## Lung cancer 0.000 0.170
## Metabolic process 0.000 0.290
## Obesity 0.220 0.180
## Phenylketonuria 0.120 0.380
## Population 0.330 0.000
## Response to antibiotic 0.000 0.250
## Response to antiviral drug 0.000 0.130
## Response to diet 0.120 0.210
## Response to transplant 0.300 0.230
## Simian immunodeficiency virus infection 0.000 0.330
## Smoking behavior 0.270 0.360
## Smoking behaviour measurement 0.290 0.000
## Smoking status measurement 0.290 0.000
## Streptococcus pneumoniae 0.000 0.400
## Traditional Chinese medicine type 0.120 0.380
## Transplant outcome measurement 0.000 0.290
## Age 0.360 0.450
## Asthma 0.110 0.170
## Atopic eczema 0.220 0.110
## Autism spectrum disorder 0.450 0.550
## Bone density 0.000 0.170
## Breastfeeding duration 0.170 0.000
## Cesarean section 0.200 0.130
## Coccidiosis 0.000 0.200
## Colon carcinoma 0.000 0.100
## Colorectal adenoma 0.071 0.140
## Endometrial cancer 0.170 0.330
## Heart failure 0.220 0.330
## Infant 0.200 0.000
## Ischemic stroke 0.330 0.170
## Lifestyle measurement 0.380 0.250
## Neurodevelopmental delay 0.170 0.000
## Obsessive-compulsive disorder 0.000 0.200
## Ovarian cancer 0.025 0.000
## Pregnancy 0.120 0.000
## Psoriasis 0.000 0.110
## Reproductive behaviour measurement 0.200 0.000
## Response to stress 0.100 0.000
## Rheumatoid arthritis 0.300 0.400
## Sampling site 0.200 0.400
## Socioeconomic status 0.400 0.200
## Transport 0.170 0.330
## Type I diabetes mellitus 0.000 0.200
## Abnormal stool composition 0.000 0.000
## Attention deficit-hyperactivity disorder 0.000 0.000
## Chlamydia trachomatis 0.200 0.200
## Chronic periodontitis 0.000 0.000
## Constipation 0.270 0.270
## Depressive disorder 0.140 0.140
## Diabetes mellitus 0.000 0.000
## Diet measurement 0.000 0.000
## Ethnic group 0.400 0.400
## Glioma 0.200 0.200
## Head and neck squamous cell carcinoma 0.000 0.000
## Hepatocellular carcinoma 0.500 0.500
## HIV mother to child transmission 0.000 0.000
## Male homosexuality 0.000 0.000
## Myocardial infarction 0.000 0.000
## Non-alcoholic fatty liver disease 0.000 0.000
## Papillary thyroid carcinoma 0.000 0.000
## Physical activity 0.400 0.400
## Prostate cancer 0.000 0.000
## Psoriasis vulgaris 0.000 0.000
## Sample collection protocol 0.000 0.000
## Stroke 0.095 0.095
## Type II diabetes mellitus 0.140 0.140
## Urinary tract infection 0.091 0.091
## Viral load 0.000 0.000
## unchanged
## Diet 0.57
## Helminthiasis 0.00
## HIV infection 0.36
## Multiple sclerosis 0.64
## Periodontitis 0.56
## Polycystic ovary syndrome 0.43
## Pulmonary tuberculosis 0.29
## COVID-19 0.45
## Parkinson's disease 0.42
## Phenotype 0.52
## Small for gestational age 0.00
## Aging 0.12
## Chronic constipation 0.50
## Gastric cancer 0.45
## Gingivitis 0.61
## Species design 0.34
## Chronic kidney disease 0.54
## Increased intestinal transit time 0.00
## Response to allogeneic hematopoietic stem cell transplant 0.00
## Schizophrenia 0.64
## Alcohol drinking 0.00
## Antimicrobial agent 0.53
## Environmental factor 0.77
## Human immunodeficiency virus 0.22
## Human papilloma virus infection 0.57
## Acute lymphoblastic leukemia 0.14
## Air pollution 0.30
## Cervical glandular intraepithelial neoplasia 0.33
## Crohn's disease 0.33
## Diarrhea 0.33
## Dry eye syndrome 0.50
## Epilepsy 0.43
## Fungal infectious disease 0.33
## Major depressive disorder 0.33
## Stress-related disorder 0.20
## Ulcerative colitis 0.33
## Vesicle membrane 0.00
## Alzheimer's disease 0.68
## Atopic asthma 0.58
## Delivery method 0.64
## Endometriosis 0.62
## Food allergy 0.75
## Graves disease 0.46
## Hematopoietic stem cell 0.50
## Hypertension 0.62
## Hypertrophy 0.40
## Iron biomarker measurement 0.17
## Irritable bowel syndrome 0.59
## Oral squamous cell carcinoma 0.00
## Treatment 0.45
## Treatment outcome measurement 0.00
## Tuberculosis 0.00
## Alcohol use disorder measurement 0.75
## Bone mineral content measurement 0.80
## Breast cancer 0.89
## Cognitive impairment 0.82
## Colorectal cancer 0.39
## Esophageal adenocarcinoma 0.67
## Gestational diabetes 0.73
## Health study participation 0.94
## Hypothyroidism 0.67
## Inflammatory bowel disease 0.14
## Lung cancer 0.83
## Metabolic process 0.71
## Obesity 0.60
## Phenylketonuria 0.50
## Population 0.67
## Response to antibiotic 0.75
## Response to antiviral drug 0.87
## Response to diet 0.67
## Response to transplant 0.47
## Simian immunodeficiency virus infection 0.67
## Smoking behavior 0.36
## Smoking behaviour measurement 0.71
## Smoking status measurement 0.71
## Streptococcus pneumoniae 0.60
## Traditional Chinese medicine type 0.50
## Transplant outcome measurement 0.71
## Age 0.18
## Asthma 0.72
## Atopic eczema 0.67
## Autism spectrum disorder 0.00
## Bone density 0.83
## Breastfeeding duration 0.83
## Cesarean section 0.67
## Coccidiosis 0.80
## Colon carcinoma 0.90
## Colorectal adenoma 0.79
## Endometrial cancer 0.50
## Heart failure 0.44
## Infant 0.80
## Ischemic stroke 0.50
## Lifestyle measurement 0.38
## Neurodevelopmental delay 0.83
## Obsessive-compulsive disorder 0.80
## Ovarian cancer 0.98
## Pregnancy 0.88
## Psoriasis 0.89
## Reproductive behaviour measurement 0.80
## Response to stress 0.90
## Rheumatoid arthritis 0.30
## Sampling site 0.40
## Socioeconomic status 0.40
## Transport 0.50
## Type I diabetes mellitus 0.80
## Abnormal stool composition 1.00
## Attention deficit-hyperactivity disorder 1.00
## Chlamydia trachomatis 0.60
## Chronic periodontitis 1.00
## Constipation 0.46
## Depressive disorder 0.71
## Diabetes mellitus 1.00
## Diet measurement 1.00
## Ethnic group 0.20
## Glioma 0.60
## Head and neck squamous cell carcinoma 1.00
## Hepatocellular carcinoma 0.00
## HIV mother to child transmission 1.00
## Male homosexuality 1.00
## Myocardial infarction 1.00
## Non-alcoholic fatty liver disease 1.00
## Papillary thyroid carcinoma 1.00
## Physical activity 0.20
## Prostate cancer 1.00
## Psoriasis vulgaris 1.00
## Sample collection protocol 1.00
## Stroke 0.81
## Type II diabetes mellitus 0.72
## Urinary tract infection 0.82
## Viral load 1.00Body sites with consistently increased or decreased alpha diversity:
tabDiv(exps, "Shannon", "Body site")## increased decreased unchanged
## Feces 372 558 1611
## Sputum 7 23 16
## Vagina 22 9 41
## Blood serum 9 0 0
## Oral cavity 15 6 25
## Posterior fornix of vagina 9 0 9
## Buccal mucosa 11 3 9
## Saliva 41 49 198
## Stomach 6 14 5
## Uterine cervix 9 1 20
## Uterine cervix,Vaginal fluid 9 1 0
## Skin of body 8 15 8
## Supragingival dental plaque 3 10 14
## Ileum 3 9 13
## Pharynx 6 1 2
## Space surrounding organism 2 7 13
## Tongue 0 5 16
## Axilla skin 5 1 11
## Caecum 4 8 33
## Rectum 3 7 20
## Throat 0 4 11
## Ascending colon 3 0 6
## Ascending colon,Colorectal mucosa,Sigmoid colon 3 0 2
## Colorectal mucosa 0 3 9
## Duodenum 0 3 6
## Intestine 1 4 19
## Lung 4 7 13
## Meconium 5 2 14
## Skin of forearm 3 0 3
## Sputum,Feces 1 4 1
## Urine 4 1 20
## Bile 2 0 3
## Blood 1 3 15
## Brachialis muscle 0 2 3
## Cecum mucosa 2 4 6
## Conjunctiva 1 3 12
## Conjunctival sac 1 3 3
## Esophagus 0 2 4
## Forelimb skin 2 0 4
## Mouth 8 6 28
## Oropharynx 1 3 5
## Thyroid gland 2 0 10
## Uterus 3 1 11
## Body proper,Insect leg 0 1 5
## Breast 3 4 10
## Breast,Milk 1 0 4
## Bulbar conjunctiva 3 2 5
## Colon 6 5 20
## Dental plaque 4 3 5
## Digestive tract 2 1 2
## Gingival groove 2 1 6
## Liver 5 4 0
## Milk 0 1 11
## Mucosa of stomach 1 0 4
## Rumen 2 1 4
## Small intestine 3 4 1
## Subgingival dental plaque 13 14 37
## Vagina,Uterine cervix 3 2 7
## Vaginal fluid 1 2 8
## Bronchus 0 0 6
## Endothelium of trachea 3 3 0
## Internal cheek pouch 0 0 11
## Jejunum 1 1 8
## Nasal cavity 1 1 5
## Nasopharynx 6 6 39
## Ovary 0 0 7
## Peritoneal fluid 0 0 6
## Posterior wall of oropharynx 2 2 1
## Skin epidermis 2 2 4
## Skin of abdomen 0 0 5
## Surface of tongue 2 2 3
## Ventral side of post-anal tail 0 0 6
tabDiv(exps, "Shannon", "Body site", perc = TRUE)## increased decreased unchanged
## Feces 0.150 0.220 0.63
## Sputum 0.150 0.500 0.35
## Vagina 0.310 0.120 0.57
## Blood serum 1.000 0.000 0.00
## Oral cavity 0.330 0.130 0.54
## Posterior fornix of vagina 0.500 0.000 0.50
## Buccal mucosa 0.480 0.130 0.39
## Saliva 0.140 0.170 0.69
## Stomach 0.240 0.560 0.20
## Uterine cervix 0.300 0.033 0.67
## Uterine cervix,Vaginal fluid 0.900 0.100 0.00
## Skin of body 0.260 0.480 0.26
## Supragingival dental plaque 0.110 0.370 0.52
## Ileum 0.120 0.360 0.52
## Pharynx 0.670 0.110 0.22
## Space surrounding organism 0.091 0.320 0.59
## Tongue 0.000 0.240 0.76
## Axilla skin 0.290 0.059 0.65
## Caecum 0.089 0.180 0.73
## Rectum 0.100 0.230 0.67
## Throat 0.000 0.270 0.73
## Ascending colon 0.330 0.000 0.67
## Ascending colon,Colorectal mucosa,Sigmoid colon 0.600 0.000 0.40
## Colorectal mucosa 0.000 0.250 0.75
## Duodenum 0.000 0.330 0.67
## Intestine 0.042 0.170 0.79
## Lung 0.170 0.290 0.54
## Meconium 0.240 0.095 0.67
## Skin of forearm 0.500 0.000 0.50
## Sputum,Feces 0.170 0.670 0.17
## Urine 0.160 0.040 0.80
## Bile 0.400 0.000 0.60
## Blood 0.053 0.160 0.79
## Brachialis muscle 0.000 0.400 0.60
## Cecum mucosa 0.170 0.330 0.50
## Conjunctiva 0.062 0.190 0.75
## Conjunctival sac 0.140 0.430 0.43
## Esophagus 0.000 0.330 0.67
## Forelimb skin 0.330 0.000 0.67
## Mouth 0.190 0.140 0.67
## Oropharynx 0.110 0.330 0.56
## Thyroid gland 0.170 0.000 0.83
## Uterus 0.200 0.067 0.73
## Body proper,Insect leg 0.000 0.170 0.83
## Breast 0.180 0.240 0.59
## Breast,Milk 0.200 0.000 0.80
## Bulbar conjunctiva 0.300 0.200 0.50
## Colon 0.190 0.160 0.65
## Dental plaque 0.330 0.250 0.42
## Digestive tract 0.400 0.200 0.40
## Gingival groove 0.220 0.110 0.67
## Liver 0.560 0.440 0.00
## Milk 0.000 0.083 0.92
## Mucosa of stomach 0.200 0.000 0.80
## Rumen 0.290 0.140 0.57
## Small intestine 0.380 0.500 0.12
## Subgingival dental plaque 0.200 0.220 0.58
## Vagina,Uterine cervix 0.250 0.170 0.58
## Vaginal fluid 0.091 0.180 0.73
## Bronchus 0.000 0.000 1.00
## Endothelium of trachea 0.500 0.500 0.00
## Internal cheek pouch 0.000 0.000 1.00
## Jejunum 0.100 0.100 0.80
## Nasal cavity 0.140 0.140 0.71
## Nasopharynx 0.120 0.120 0.76
## Ovary 0.000 0.000 1.00
## Peritoneal fluid 0.000 0.000 1.00
## Posterior wall of oropharynx 0.400 0.400 0.20
## Skin epidermis 0.250 0.250 0.50
## Skin of abdomen 0.000 0.000 1.00
## Surface of tongue 0.290 0.290 0.43
## Ventral side of post-anal tail 0.000 0.000 1.00
tabDiv(exps, "Richness", "Body site")## increased decreased unchanged
## Feces 231 352 839
## Oral cavity 15 4 19
## Sputum 0 11 5
## Supragingival dental plaque 0 9 9
## Ileum 3 11 10
## Mucosa of rectum 0 8 1
## Stomach 4 12 3
## Mouth 10 3 9
## Posterior fornix of vagina 8 1 2
## Uterine cervix 8 1 11
## Oropharynx 0 6 6
## Colon 9 4 12
## Rectum 1 6 8
## Skin epidermis 0 5 2
## Skin of body 4 9 6
## Uterine cervix,Vaginal fluid 7 2 1
## Conjunctival sac 0 4 1
## Throat 1 5 5
## Vagina 6 2 17
## Ascending colon 3 0 6
## Lung 0 3 3
## Small intestine 1 4 0
## Subgingival dental plaque 9 6 35
## Cecum mucosa 2 4 1
## Dental plaque 1 3 5
## Ear 2 0 3
## Esophagus 0 2 4
## Saliva 25 27 107
## Surface of tongue 4 2 1
## Tongue 2 4 7
## Urine 4 2 16
## Blood 1 2 4
## Caecum 5 6 12
## Intestine 2 1 16
## Meconium 2 3 7
## Nasal cavity 1 2 6
## Nasopharynx 8 9 25
## Rumen 0 1 4
## Vagina,Uterine cervix 1 0 11
## Breast 1 1 7
## Bronchus 0 0 6
## Buccal mucosa 2 2 6
## Conjunctiva 1 1 5
## Internal cheek pouch 0 0 7
## Liver 3 3 0
## Milk 2 2 5
## Ovary 0 0 7
## Peritoneal fluid 0 0 6
## Thyroid gland 0 0 12
tabDiv(exps, "Richness", "Body site", perc = TRUE)## increased decreased unchanged
## Feces 0.160 0.250 0.59
## Oral cavity 0.390 0.110 0.50
## Sputum 0.000 0.690 0.31
## Supragingival dental plaque 0.000 0.500 0.50
## Ileum 0.120 0.460 0.42
## Mucosa of rectum 0.000 0.890 0.11
## Stomach 0.210 0.630 0.16
## Mouth 0.450 0.140 0.41
## Posterior fornix of vagina 0.730 0.091 0.18
## Uterine cervix 0.400 0.050 0.55
## Oropharynx 0.000 0.500 0.50
## Colon 0.360 0.160 0.48
## Rectum 0.067 0.400 0.53
## Skin epidermis 0.000 0.710 0.29
## Skin of body 0.210 0.470 0.32
## Uterine cervix,Vaginal fluid 0.700 0.200 0.10
## Conjunctival sac 0.000 0.800 0.20
## Throat 0.091 0.450 0.45
## Vagina 0.240 0.080 0.68
## Ascending colon 0.330 0.000 0.67
## Lung 0.000 0.500 0.50
## Small intestine 0.200 0.800 0.00
## Subgingival dental plaque 0.180 0.120 0.70
## Cecum mucosa 0.290 0.570 0.14
## Dental plaque 0.110 0.330 0.56
## Ear 0.400 0.000 0.60
## Esophagus 0.000 0.330 0.67
## Saliva 0.160 0.170 0.67
## Surface of tongue 0.570 0.290 0.14
## Tongue 0.150 0.310 0.54
## Urine 0.180 0.091 0.73
## Blood 0.140 0.290 0.57
## Caecum 0.220 0.260 0.52
## Intestine 0.110 0.053 0.84
## Meconium 0.170 0.250 0.58
## Nasal cavity 0.110 0.220 0.67
## Nasopharynx 0.190 0.210 0.60
## Rumen 0.000 0.200 0.80
## Vagina,Uterine cervix 0.083 0.000 0.92
## Breast 0.110 0.110 0.78
## Bronchus 0.000 0.000 1.00
## Buccal mucosa 0.200 0.200 0.60
## Conjunctiva 0.140 0.140 0.71
## Internal cheek pouch 0.000 0.000 1.00
## Liver 0.500 0.500 0.00
## Milk 0.220 0.220 0.56
## Ovary 0.000 0.000 1.00
## Peritoneal fluid 0.000 0.000 1.00
## Thyroid gland 0.000 0.000 1.00Signature stats
sigs <- bugsigdbr::getSignatures(dat, tax.id.type = "metaphlan")Unique microbes
Number unique microbes contained in the signatures:
## [1] 7934Development of unique microbes captured over time:
Microbe set size distribution
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 4.000 8.137 10.000 467.000
gghistogram(lengths(sigs), bins = 30, ylab = "number of signatures",
xlab = "signature size", fill = "#00AFBB", ggtheme = theme_bw())
## [1] 6389Microbe co-occurrence
dat.feces <- subset(dat, `Body site` == "Feces")
cooc.mat <- microbeHeatmap(dat.feces, tax.level = "genus", anno = "genus")## Loading required namespace: safe
antag.mat <- microbeHeatmap(dat.feces, tax.level = "genus", anno = "genus", antagonistic = TRUE)
Get the top 20 genera most frequently reported as differentially abundant:
sigs.feces <- getSignatures(dat.feces, tax.id.type = "taxname",
tax.level = "genus", exact.tax.level = FALSE)
top20 <- sort(table(unlist(sigs.feces)), decreasing = TRUE)[1:20]
top20##
## Bacteroides Faecalibacterium Bifidobacterium Blautia
## 836 593 586 551
## Lactobacillus Streptococcus Ruminococcus Prevotella
## 519 508 484 482
## Clostridium Roseburia Parabacteroides Alistipes
## 477 477 436 405
## Akkermansia Dorea Coprococcus Veillonella
## 352 338 334 293
## Collinsella Enterococcus Anaerostipes Sutterella
## 290 289 287 276Subset heatmaps to the top 20 genera most frequently reported as differentially abundant:
## [1] TRUE## [1] TRUEDistinguish by direction of abundance change (increased / decreased):
# increased
sub.dat.feces <- subset(dat.feces, `Abundance in Group 1` == "increased")
sigs.feces.up <- getSignatures(sub.dat.feces, tax.id.type = "taxname",
tax.level = "genus", exact.tax.level = FALSE)
top20.up <- table(unlist(sigs.feces.up))[names(top20)]
top20.up##
## Bacteroides Faecalibacterium Bifidobacterium Blautia
## 414 204 316 260
## Lactobacillus Streptococcus Ruminococcus Prevotella
## 319 321 223 232
## Clostridium Roseburia Parabacteroides Alistipes
## 252 159 250 193
## Akkermansia Dorea Coprococcus Veillonella
## 232 143 140 171
## Collinsella Enterococcus Anaerostipes Sutterella
## 154 209 132 139
# decreased
sub.dat.feces <- subset(dat.feces, `Abundance in Group 1` == "decreased")
sigs.feces.down <- getSignatures(sub.dat.feces, tax.id.type = "taxname",
tax.level = "genus", exact.tax.level = FALSE)
top20.down <- table(unlist(sigs.feces.down))[names(top20)]
top20.down##
## Bacteroides Faecalibacterium Bifidobacterium Blautia
## 417 385 265 287
## Lactobacillus Streptococcus Ruminococcus Prevotella
## 198 182 257 247
## Clostridium Roseburia Parabacteroides Alistipes
## 220 314 182 208
## Akkermansia Dorea Coprococcus Veillonella
## 117 191 190 119
## Collinsella Enterococcus Anaerostipes Sutterella
## 132 78 151 137Plot the heatmap
# annotation
mat <- matrix(nc = 2, cbind(top20.up, top20.down))
bp <- ComplexHeatmap::anno_barplot(mat, gp = gpar(fill = c("#D55E00", "#0072B2"),
col = c("#D55E00", "#0072B2")),
height = unit(2, "cm"))
banno <- ComplexHeatmap::HeatmapAnnotation(`Abundance in Group 1` = bp)
lgd_list <- list(
Legend(labels = c("increased", "decreased"),
title = "Abundance in Group 1",
type = "grid",
legend_gp = gpar(col = c("#D55E00", "#0072B2"), fill = c("#D55E00", "#0072B2"))))
# same direction
# lcm <- sweep(cooc.mat, 2, matrixStats::colMaxs(cooc.mat), FUN = "/")
# we need to dampen the maximum here a bit down,
# otherwise 100% self co-occurrence takes up a large fraction of the colorscale,
sec <- apply(cooc.mat, 2, function(x) sort(x, decreasing = TRUE)[2])
cooc.mat2 <- cooc.mat
for(i in 1:ncol(cooc.mat2)) cooc.mat2[i,i] <- min(cooc.mat2[i,i], 1.4 * sec[i])
lcm <- sweep(cooc.mat2, 2, matrixStats::colMaxs(cooc.mat2), FUN = "/")
col <- circlize::colorRamp2(c(0,1), c("#EEEEEE", "red"))
ht1 <- ComplexHeatmap::Heatmap(lcm,
col = col,
name = "Relative frequency (top)",
cluster_columns = FALSE,
row_km = 3,
row_title = "same direction",
column_names_rot = 45,
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))
# opposite direction
acm <- sweep(antag.mat, 2, matrixStats::colMaxs(antag.mat), FUN = "/")
col <- circlize::colorRamp2(c(0,1), c("#EEEEEE", "blue"))
ht2 <- ComplexHeatmap::Heatmap(acm,
col = col,
name = "Relative frequency (bottom)",
cluster_columns = FALSE,
row_title = "opposite direction",
row_km = 3,
column_names_rot = 45,
row_names_gp = gpar(fontsize = 8),
column_names_gp = gpar(fontsize = 8))
# phylum
sfp <- bugsigdbr::getSignatures(dat.feces, tax.id.type = "metaphlan",
tax.level = "genus", exact.tax.level = FALSE)
sfp20 <- sort(table(unlist(sfp)), decreasing = TRUE)[1:20]
uanno <- bugsigdbr::extractTaxLevel(names(sfp20),
tax.id.type = "taxname",
tax.level = "mixed",
exact.tax.level = FALSE)
phyla.grid <- seq_along(unique(uanno))
panno <- ComplexHeatmap::HeatmapAnnotation(phylum = uanno)
uanno <- matrix(uanno, nrow = 1)
colnames(uanno) <- names(top20)
pcols <- c("#CC79A7", "#F0E442", "#009E73", "#56B4E9", "#E69F00")
uanno <- ComplexHeatmap::Heatmap(uanno, name = "Phylum",
col = pcols[phyla.grid],
cluster_columns = FALSE,
column_names_rot = 45,
column_names_gp = gpar(fontsize = 8))## There are 20 unique colors in the vector `col` and 20 unique values in
## `matrix`. `Heatmap()` will treat it as an exact discrete one-to-one
## mapping. If this is not what you want, slightly change the number of
## colors, e.g. by adding one more color or removing a color.
# put everything together
ht_list <- ht1 %v% banno %v% ht2 %v% uanno
ComplexHeatmap::draw(ht_list, annotation_legend_list = lgd_list, merge_legend = TRUE)
decorate_annotation("Abundance in Group 1", {
grid.text("# signatures", x = unit(-1, "cm"), rot = 90, just = "bottom", gp = gpar(fontsize = 8))
grid.text("*", x = unit(2.45, "cm"), y = unit(1.2, "cm"))
grid.text("*", x = unit(5.18, "cm"), y = unit(1, "cm"))
grid.text("*", x = unit(6.55, "cm"), y = unit(0.95, "cm"))
grid.text("*", x = unit(8.6, "cm"), y = unit(0.85, "cm"))
grid.text("*", x = unit(10, "cm"), y = unit(0.7, "cm"))
grid.text("*", x = unit(10.7, "cm"), y = unit(0.7, "cm"))
})
Signature similarity
Jaccard index
Inspect signature similarity for signatures from stomach samples based on Jaccard index:
stomachsub <- subset(dat, `Body site` == "Stomach")
sigsub <- bugsigdbr::getSignatures(stomachsub)
pair.jsim <- calcJaccardSimilarity(sigsub)Create a dendrogram of Jaccard dissimilarities (1.0 has no overlap, 0.0 are identical signatures).
