Provides parsing functionality for over 30 metabolomics databases, with most available without having to create an account on given websites.
Once parsed, calculates given adducts and isotope patterns and inserts into one big database which can be used to annotate unknown m/z values.
Furthermore, formulas can be predicted for a given m/z, and these can be matched to ChemSpider, PubChem, SUPERNATURAL II, KNApSAcK and ChemIDplus for further annotation.
Current databases available: HMDB, ChEBI, LMDB, BMDB, MCDB, ECMDB, Wikidata, mVOC, VMH, T3DB, Exposome Explorer, FooDB, MetaCyc (requires account), DrugBank (requires account), ReSPECT, MaConDa, Blood Exposome DB, KEGG, SMPDB, LIPID MAPS, MetaboLights, DimeDB, Phenol Explorer, MassBank, YMDB, PAMDB, ANPDB, Metabolomics Workbench, PharmGKB, Reactome, mVOC and STOFF.
Featured in the 'MetaboShiny' package (Wolthuis, J. (2019) <doi:10.1007/s11306-020-01717-8>).
methods, pacman, rcdk, rJava, parallel, pbapply, enviPat,
data.table, RSQLite, DBI, gsubfn, utils, base, stringr,
WikidataQueryServiceR, webchem, jsonlite, R.utils, KEGGREST,
zip, ChemmineR, xml2, stringi, reshape2, Hmisc, httr, RJSONIO,
readxl, cmmr, progress, Rdisop, rlist

MetaDE: Microarray meta-analysis for differentially expressed
gene detection
MetaDE package implements 12 major meta-analysis methods
for differential expression analysis.

Meta analysis of multiple microarray data
Jia Li<jiajiaysc@gmail.com> and Xingbin
Wang<xingbinw@gmail.com>
A collection of functions for conducting genomic
meta-analysis in R.

An ensemble meta-prediction framework to integrate multiple regression
models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020)
<arXiv:2010.09971>.
A meta-analysis framework along with two weighted estimators as the ensemble
of empirical Bayes estimators, which combines the estimates from the different
external models. The proposed framework is flexible and robust in the ways
that (i) it is capable of incorporating external models that use a slightly
different set of covariates; (ii) it is able to identify the most relevant
external information and diminish the influence of information that is less
compatible with the internal data; and (iii) it nicely balances the bias-variance
trade-off while preserving the most efficiency gain. The proposed estimators
are more efficient than the naive analysis of the internal data and other
naive combinations of external estimators.
c(
person(given = "Tian", family = "Gu", role = c("aut"), email = "gutian@umich.edu"),
person(given = "Bhramar", family = "Mukherjee", role = c("aut"), email = "bhramar@umich.edu"),
person(given = "Michael", family = "Kleinsasser", role = c("cre"), email = "mkleinsa@umich.edu")
)
Tian Gu [aut],
Bhramar Mukherjee [aut],
Michael Kleinsasser [cre]