Functionality for obtaining meta-signatures for a column of interest
getMetaSignatures(
df,
column,
direction = c("BOTH", "UP", "DOWN"),
min.studies = 2,
min.taxa = 5,
comb.fun = sum,
...
)data.frame storing BugSigDB data. Typically obtained via
importBugSigDB.
character. Column of interest. Need to be a valid column name
of df.
character. Indicates direction of abundance change for signatures
to be included in the computation of meta-signatures. Use "UP" to restrict
computation to signatures with increased abundance in the exposed group. Use
"DOWN" to restrict to signatures with decreased abundance in the exposed
group. Defaults to "BOTH" which will not filter signatures by direction
of abundance change.
integer. Minimum number of studies for a category in column
to be included. Defaults to 2, which will then only compute meta-signatures for
categories investigated by at least two studies.
integer. Minimum size for meta-signatures. Defaults to 5, which will then only include meta-signatures containing at least 5 taxa.
function. Function for combining sample size of the exposed group
and sample size of the unexposed group into an overall study sample size. Defaults
to sum which will simply add sample sizes of exposed and unexposed group.
additionals argument passed on to getSignatures.
A list of meta-signatures, each meta-signature being a named
numeric vector. Names are the taxa of the meta-signature, numeric values
correspond to sample size weights associated with each taxon.
getSignatures
df <- importBugSigDB()
#> Using cached version from 2026-04-29 20:00:10
# Body-site specific meta-signatures composed from signatures reported as both
# increased or decreased across all conditions of study:
bs.meta.sigs <- getMetaSignatures(df, column = "Body site")
# Condition-specific meta-signatures from fecal samples, increased
# in conditions of study. Use taxonomic names instead of the default NCBI IDs:
df.feces <- df[df$`Body site` == "Feces", ]
cond.meta.sigs <- getMetaSignatures(df.feces, column = "Condition",
direction = "UP", tax.id.type = "taxname")
# Inspect the results
names(cond.meta.sigs)
#> [1] "Acute graft vs. host disease"
#> [2] "Acute lymphoblastic leukemia"
#> [3] "Age"
#> [4] "Age-related macular degeneration"
#> [5] "Air pollution"
#> [6] "Alcohol drinking"
#> [7] "Allergic rhinitis"
#> [8] "Alzheimer's disease"
#> [9] "Anemia"
#> [10] "Antimicrobial agent"
#> [11] "Anxiety disorder"
#> [12] "Aspartate aminotransferase measurement"
#> [13] "Asthma"
#> [14] "Atopic eczema"
#> [15] "Attention deficit hyperactivity disorder"
#> [16] "Attention deficit-hyperactivity disorder"
#> [17] "Autism"
#> [18] "Autism spectrum disorder"
#> [19] "Autoimmune disease"
#> [20] "Autoimmune type 1 diabetes"
#> [21] "Biliary atresia"
#> [22] "Bipolar disorder"
#> [23] "Body fat percentage"
#> [24] "Body mass index"
#> [25] "Body weight"
#> [26] "Breast cancer"
#> [27] "Breastfeeding duration"
#> [28] "COVID-19"
#> [29] "Cervical cancer"
#> [30] "Cesarean section"
#> [31] "Chronic constipation"
#> [32] "Chronic fatigue syndrome"
#> [33] "Chronic kidney disease"
#> [34] "Clinical treatment"
#> [35] "Clostridium difficile infection"
#> [36] "Cognitive impairment"
#> [37] "Colitis"
#> [38] "Colorectal adenoma"
#> [39] "Colorectal cancer"
#> [40] "Colorectal carcinoma"
#> [41] "Constipation"
#> [42] "Crohn's disease"
#> [43] "Delivery method"
#> [44] "Depressive disorder"
#> [45] "Diabetes mellitus"
#> [46] "Diabetic nephropathy"
#> [47] "Diabetic neuropathy"
#> [48] "Diabetic retinopathy"
#> [49] "Diarrhea"
#> [50] "Diet"
#> [51] "Diet measurement"
#> [52] "Eczema"
#> [53] "Endometriosis"
#> [54] "Environmental exposure measurement"
#> [55] "Environmental factor"
#> [56] "Epilepsy"
#> [57] "Estradiol measurement"
#> [58] "Ethnic group"
#> [59] "Exercise"
#> [60] "Food allergy"
#> [61] "Gastric adenocarcinoma"
#> [62] "Gastric cancer"
#> [63] "Gestational diabetes"
#> [64] "Glucose tolerance test"
#> [65] "Graft versus host disease"
#> [66] "HIV infection"
#> [67] "Health study participation"
#> [68] "Hepatocellular carcinoma"
#> [69] "High fat diet"
#> [70] "Human immunodeficiency virus"
#> [71] "Hypertension"
#> [72] "Inflammatory bowel disease"
#> [73] "Iron deficiency anemia"
#> [74] "Irritable bowel syndrome"
#> [75] "Ischemic stroke"
#> [76] "Leukemia"
#> [77] "Lifestyle measurement"
#> [78] "Low density lipoprotein cholesterol measurement"
#> [79] "Lung cancer"
#> [80] "Major depressive disorder"
#> [81] "Metastatic colorectal cancer"
#> [82] "Milk allergic reaction"
#> [83] "Multiple myeloma"
#> [84] "Multiple sclerosis"
#> [85] "Non-Hodgkins lymphoma"
#> [86] "Non-alcoholic fatty liver disease"
#> [87] "Non-alcoholic steatohepatitis"
#> [88] "Obesity"
#> [89] "Pancreatic carcinoma"
#> [90] "Parkinson's disease"
#> [91] "Physical activity"
#> [92] "Polycystic ovary syndrome"
#> [93] "Population"
#> [94] "Prediabetes syndrome"
#> [95] "Psoriasis"
#> [96] "Reproductive behaviour measurement"
#> [97] "Response to allogeneic hematopoietic stem cell transplant"
#> [98] "Response to antibiotic"
#> [99] "Response to antiviral drug"
#> [100] "Response to diet"
#> [101] "Response to immunochemotherapy"
#> [102] "Response to ketogenic diet"
#> [103] "Response to metformin"
#> [104] "Response to transplant"
#> [105] "Rheumatoid arthritis"
#> [106] "Sampling time"
#> [107] "Schizophrenia"
#> [108] "Seasonality measurement"
#> [109] "Socioeconomic status"
#> [110] "Stroke"
#> [111] "Total cholesterol measurement"
#> [112] "Traditional Chinese medicine type"
#> [113] "Transplant outcome measurement"
#> [114] "Treatment"
#> [115] "Type I diabetes mellitus"
#> [116] "Type II diabetes mellitus"
#> [117] "Ulcerative colitis"
#> [118] "Unipolar depression"
cond.meta.sigs["Bipolar disorder"]
#> $`Bipolar disorder`
#> Lactobacillales Streptococcaceae
#> 0.031597774 0.031597774
#> Streptococcus Bacteroidota
#> 0.031597774 0.030206677
#> Bilophila wadsworthia Dialister histaminiformans
#> 0.030206677 0.030206677
#> Leyella stercorea Megamonas funiformis
#> 0.030206677 0.030206677
#> Oxalobacter formigenes Parabacteroides distasonis
#> 0.030206677 0.030206677
#> Parasutterella excrementihominis Phocaeicola plebeius
#> 0.030206677 0.030206677
#> [Clostridium] symbiosum Actinomyces
#> 0.030206677 0.023251192
#> Actinomycetaceae Actinomycetales
#> 0.023251192 0.023251192
#> Alcaligenaceae Bacillaceae
#> 0.023251192 0.023251192
#> Bacilli Bacillus
#> 0.023251192 0.023251192
#> Corynebacteriaceae Corynebacterium
#> 0.023251192 0.023251192
#> Dorea Enterobacterales
#> 0.023251192 0.023251192
#> Gammaproteobacteria Lacticaseibacillus zeae
#> 0.023251192 0.023251192
#> Peptoniphilus Phascolarctobacterium
#> 0.023251192 0.023251192
#> Sutterella Veillonellaceae
#> 0.023251192 0.023251192
#> Lactobacillaceae Lactobacillus
#> 0.008346582 0.008346582
#> Acidaminococcus intestini Acinetobacter baumannii
#> 0.007352941 0.007352941
#> Acinetobacter johnsonii Acinetobacter radioresistens
#> 0.007352941 0.007352941
#> Anaerococcus vaginalis Citrobacter koseri
#> 0.007352941 0.007352941
#> Desulfovibrio piger Enterococcus gallinarum
#> 0.007352941 0.007352941
#> Erwinia amylovora Escherichia coli
#> 0.007352941 0.007352941
#> Escherichia fergusonii Fusobacterium nucleatum
#> 0.007352941 0.007352941
#> Fusobacterium varium Limosilactobacillus mucosae
#> 0.007352941 0.007352941
#> Porphyromonas uenonis Prevotella amnii
#> 0.007352941 0.007352941
#> Proteus mirabilis Providencia rettgeri
#> 0.007352941 0.007352941
#> Salmonella enterica Shewanella putrefaciens
#> 0.007352941 0.007352941
#> Shigella boydii Shigella flexneri
#> 0.007352941 0.007352941
#> Shigella sonnei Sodalis glossinidius
#> 0.007352941 0.007352941
#> Vibrio cholerae Yersinia enterocolitica
#> 0.007352941 0.007352941
#>