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 2025-05-29 17:03:46
# 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 lymphoblastic leukemia"
#> [2] "Acute myeloid leukemia"
#> [3] "Age"
#> [4] "Air pollution"
#> [5] "Alcohol consumption measurement"
#> [6] "Allergic rhinitis"
#> [7] "Alzheimer's disease"
#> [8] "Anemia"
#> [9] "Anorexia nervosa"
#> [10] "Antimicrobial agent"
#> [11] "Anxiety disorder"
#> [12] "Arthritis"
#> [13] "Asthma"
#> [14] "Atopic eczema"
#> [15] "Attention deficit hyperactivity disorder"
#> [16] "Autism"
#> [17] "Autism spectrum disorder"
#> [18] "Autoimmune disease"
#> [19] "Autoimmune type 1 diabetes"
#> [20] "Behcet's syndrome"
#> [21] "Bipolar disorder"
#> [22] "Body mass index"
#> [23] "Breast cancer"
#> [24] "Breastfeeding duration"
#> [25] "Breed"
#> [26] "COVID-19"
#> [27] "Cervical cancer"
#> [28] "Cesarean section"
#> [29] "Chronic constipation"
#> [30] "Chronic fatigue syndrome"
#> [31] "Chronic kidney disease"
#> [32] "Clinical treatment"
#> [33] "Clostridium difficile infection"
#> [34] "Cognitive impairment"
#> [35] "Colitis"
#> [36] "Colorectal adenoma"
#> [37] "Colorectal cancer"
#> [38] "Colorectal carcinoma"
#> [39] "Constipation"
#> [40] "Crohn's disease"
#> [41] "Delivery method"
#> [42] "Depressive disorder"
#> [43] "Diabetes mellitus"
#> [44] "Diabetic nephropathy"
#> [45] "Diabetic neuropathy"
#> [46] "Diabetic retinopathy"
#> [47] "Diarrhea"
#> [48] "Diet"
#> [49] "Diet measurement"
#> [50] "Eczema"
#> [51] "Endometriosis"
#> [52] "Environmental exposure measurement"
#> [53] "Environmental factor"
#> [54] "Epilepsy"
#> [55] "Ethnic group"
#> [56] "Exercise"
#> [57] "Fasting"
#> [58] "Food allergy"
#> [59] "Gastric cancer"
#> [60] "Gestational diabetes"
#> [61] "Graft versus host disease"
#> [62] "HIV infection"
#> [63] "HIV mother to child transmission"
#> [64] "Health study participation"
#> [65] "Hepatocellular carcinoma"
#> [66] "High fat diet"
#> [67] "Human immunodeficiency virus"
#> [68] "Hypertension"
#> [69] "Increased intestinal transit time"
#> [70] "Inflammatory bowel disease"
#> [71] "Insulin sensitivity measurement"
#> [72] "Iron deficiency anemia"
#> [73] "Irritable bowel syndrome"
#> [74] "Ischemic stroke"
#> [75] "Ketogenic diet"
#> [76] "Leukemia"
#> [77] "Lifestyle measurement"
#> [78] "Lung cancer"
#> [79] "Major depressive disorder"
#> [80] "Maternal milk"
#> [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] "Nucleic acid extraction protocol"
#> [89] "Obesity"
#> [90] "Pancreatic carcinoma"
#> [91] "Parkinson's disease"
#> [92] "Phenotype"
#> [93] "Physical activity"
#> [94] "Polycystic ovary syndrome"
#> [95] "Population"
#> [96] "Prediabetes syndrome"
#> [97] "Psoriasis"
#> [98] "Response to allogeneic hematopoietic stem cell transplant"
#> [99] "Response to antibiotic"
#> [100] "Response to antiviral drug"
#> [101] "Response to diet"
#> [102] "Response to immunochemotherapy"
#> [103] "Response to ketogenic diet"
#> [104] "Response to metformin"
#> [105] "Response to transplant"
#> [106] "Rheumatoid arthritis"
#> [107] "Sample treatment protocol"
#> [108] "Schizophrenia"
#> [109] "Smoking behavior"
#> [110] "Socioeconomic status"
#> [111] "Stroke"
#> [112] "Traditional Chinese medicine type"
#> [113] "Transplant outcome measurement"
#> [114] "Treatment"
#> [115] "Treatment outcome measurement"
#> [116] "Type I diabetes mellitus"
#> [117] "Type II diabetes mellitus"
#> [118] "Ulcerative colitis"
#> [119] "Unipolar depression"
#> [120] "Urinary tract infection"
#> [121] "Vitiligo"
cond.meta.sigs["Bipolar disorder"]
#> $`Bipolar disorder`
#> Flavonifractor Bilophila wadsworthia
#> 0.038958376 0.038753332
#> Lactobacillales Streptococcaceae
#> 0.032602009 0.032602009
#> Allisonella histaminiformans Bacteroidota
#> 0.031166701 0.031166701
#> Leyella stercorea Megamonas funiformis
#> 0.031166701 0.031166701
#> Oxalobacter formigenes Parabacteroides distasonis
#> 0.031166701 0.031166701
#> Parasutterella excrementihominis Phocaeicola plebeius
#> 0.031166701 0.031166701
#> [Clostridium] symbiosum Actinomyces
#> 0.031166701 0.023990158
#> Actinomycetaceae Actinomycetales
#> 0.023990158 0.023990158
#> Alcaligenaceae Bacillaceae
#> 0.023990158 0.023990158
#> Bacilli Bacillus
#> 0.023990158 0.023990158
#> Corynebacteriaceae Corynebacterium
#> 0.023990158 0.023990158
#> Enterobacterales Gammaproteobacteria
#> 0.023990158 0.023990158
#> Lacticaseibacillus zeae Peptoniphilus
#> 0.023990158 0.023990158
#> Phascolarctobacterium Sutterella
#> 0.023990158 0.023990158
#> Veillonellaceae Lactobacillaceae
#> 0.023990158 0.008611852
#> Lactobacillus Streptococcus
#> 0.008611852 0.008611852
#> Alistipes ihumii Alistipes sp. HGB5
#> 0.007586631 0.007586631
#> Azospirillum sp. CAG:239 Bacteroides
#> 0.007586631 0.007586631
#> Bacteroides eggerthii Bacteroides sp. CAG:770
#> 0.007586631 0.007586631
#> Bilophila sp. 4_1_30 Collinsella aerofaciens
#> 0.007586631 0.007586631
#> Collinsella sp. 4_8_47FAA Collinsella sp. CAG:166
#> 0.007586631 0.007586631
#> Eubacterium sp. CAG:202 Firmicutes bacterium CAG:240
#> 0.007586631 0.007586631
#> Firmicutes bacterium CAG:83 Fusobacterium sp. CAG:815
#> 0.007586631 0.007586631
#> Mitsuokella multacida Oscillibacter sp. KLE 1728
#> 0.007586631 0.007586631
#> Oscillibacter sp. KLE 1745 Oscillospiraceae bacterium VE202-24
#> 0.007586631 0.007586631
#> Paraprevotella clara Phocaeicola coprophilus
#> 0.007586631 0.007586631
#> Ruminococcaceae bacterium cv2 uncultured crAssphage
#> 0.007586631 0.007586631
#>