Showing 15 of total 15 results (show query)
bioc
bugsigdbr:R-side access to published microbial signatures from BugSigDB
The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT.
Maintained by Ludwig Geistlinger. Last updated 12 days ago.
dataimportgenesetenrichmentmetagenomicsmicrobiomebioconductor-package
10.1 match 3 stars 6.46 score 48 scripts
adw96
breakaway:Species Richness Estimation and Modeling
Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. 'breakaway' is the premier package for statistical analysis of microbial diversity. 'breakaway' implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.
Maintained by Amy D Willis. Last updated 1 years ago.
8.0 match 68 stars 8.18 score 211 scripts
yulab-smu
MMINP:Microbe-Metabolite Interactions-Based Metabolic Profiles Predictor
Implements a computational framework to predict microbial community-based metabolic profiles with 'O2PLS' model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances.
Maintained by Wenli Tang. Last updated 2 years ago.
metabolite-predictionmetabolitesmicrobes
12.9 match 13 stars 4.81 score 9 scriptsyulab-smu
TDbook:Companion Package for the Book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574, doi:10.1201/9781003279242)
The companion package that provides all the datasets used in the book "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574, doi:10.1201/9781003279242).
Maintained by Guangchuang Yu. Last updated 3 years ago.
10.6 match 13 stars 4.88 score 59 scripts
cxzdsa2332
idopNetwork:A Network Tool to Dissect Spatial Community Ecology
Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.
Maintained by Ang Dong. Last updated 2 years ago.
10.9 match 4 stars 4.30 score 3 scripts
helenkettle
microPop:Process-Based Modelling of Microbial Populations
Modelling interacting microbial populations - example applications include human gut microbiota, rumen microbiota and phytoplankton. Solves a system of ordinary differential equations to simulate microbial growth and resource uptake over time. This version contains network visualisation functions.
Maintained by Helen Kettle. Last updated 3 years ago.
6.8 match 2.64 score 11 scriptsbioc
MicrobiomeProfiler:An R/shiny package for microbiome functional enrichment analysis
This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis.
Maintained by Guangchuang Yu. Last updated 5 months ago.
microbiomesoftwarevisualizationkegg
2.3 match 37 stars 6.79 score 22 scriptsbioc
eudysbiome:Cartesian plot and contingency test on 16S Microbial data
eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition.
Maintained by Xiaoyuan Zhou. Last updated 5 months ago.
3.2 match 3.60 score 2 scripts
david-barnett
microViz:Microbiome Data Analysis and Visualization
Microbiome data visualization and statistics tools built upon phyloseq.
Maintained by David Barnett. Last updated 3 months ago.
microbiomemicrobiome-analysismicrobiota
1.8 match 114 stars 6.22 score 480 scriptsbioc
mbQTL:mbQTL: A package for SNP-Taxa mGWAS analysis
mbQTL is a statistical R package for simultaneous 16srRNA,16srDNA (microbial) and variant, SNP, SNV (host) relationship, correlation, regression studies. We apply linear, logistic and correlation based statistics to identify the relationships of taxa, genus, species and variant, SNP, SNV in the infected host. We produce various statistical significance measures such as P values, FDR, BC and probability estimation to show significance of these relationships. Further we provide various visualization function for ease and clarification of the results of these analysis. The package is compatible with dataframe, MRexperiment and text formats.
Maintained by Mercedeh Movassagh. Last updated 5 months ago.
snpmicrobiomewholegenomemetagenomicsstatisticalmethodregression
1.7 match 1 stars 4.00 score 3 scriptsbioc
ccrepe:ccrepe_and_nc.score
The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data.
Maintained by Emma Schwager. Last updated 5 months ago.
immunooncologystatisticsmetagenomicsbioinformaticssoftware
0.8 match 4.08 score 7 scripts
bognasmug
miLAG:Calculates Microbial Lag Duration (on the Population Level) from Provided Growth Curve Data
Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301–307 <doi:10.1111/2041-210X.14269>.
Maintained by Bogna Smug. Last updated 2 months ago.
0.5 match 3.60 score 3 scripts
johnihrie
MPN:Most Probable Number and Other Microbial Enumeration Techniques
Calculates the Most Probable Number (MPN) to quantify the concentration (density) of microbes in serial dilutions of a laboratory sample (described in Jarvis, 2010 <doi:10.1111/j.1365-2672.2010.04792.x>). Also calculates the Aerobic Plate Count (APC) for similar microbial enumeration experiments.
Maintained by John Ihrie. Last updated 5 months ago.
0.5 match 3.30 score 10 scripts
behnam-yousefi
mnda:Multiplex Network Differential Analysis (MNDA)
Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.
Maintained by Behnam Yousefi. Last updated 2 years ago.
0.5 match 1.04 score 11 scripts