Detection of Low-Quality Peaks in Untargeted Metabolomics Data
Utilizes 12 peak quality metrics and 9 diverse machine learning algorithms to build a classifier for the automatic
assessment of peak integration quality of peaks from untargeted metabolomics analyses.
The 12 peak quality metrics were adapted from those defined in the following references:
Zhang, W., & Zhao, P.X. (2014) <doi:10.1186/1471-2105-15-S11-S5>
Toghi Eshghi, S., Auger, P., & Mathews, W.R. (2018) <doi:10.1186/s12014-018-9209-x>.
xcms, caret, reshape2, knitr, ggplot2, plotrix, tools, utils,
klaR, fastAdaboost, rpart, randomForest, kernlab, BiocStyle,
methods, graph, Rgraphviz

Utilizes 11 peak quality metrics and 8 diverse machine learning algorithms to build a classifier for the automatic
assessment of peak integration quality of peaks from untargeted metabolomics analyses.
The 12 peak quality metrics were adapted from those defined in the following references:
Zhang, W., & Zhao, P.X. (2014) <doi:10.1186/1471-2105-15-S11-S5>
Toghi Eshghi, S., Auger, P., & Mathews, W.R. (2018) <doi:10.1186/s12014-018-9209-x>.
reshape2, knitr, ggplot2, plotrix, tools, utils, klaR,
fastAdaboost, rpart, randomForest, kernlab, BiocStyle, methods,
graph, Rgraphviz, caret

Implements routines for metagenome sample taxonomy assignments collection,
aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2,
BWA, Kraken, and MetaPhlAn. Produces SVG and PDF heatmap-like plots comparing taxa
abundances across projects.
person("Pavel", "Senin", , "psenin@lanl.gov", c("aut", "cre"))
reshape2, plyr, dplyr, data.table, ggplot2, Cairo

Implements routines for metagenome sample taxonomy assignments collection,
aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2,
BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots
comparing taxa abundances across projects.
person("Pavel", "Senin", , "senin@hawaii.edu", c("aut", "cre"))

Evaluate Periodicity in Large Scale Data
c(
person("Gang", "Wu",, email = "wggucas@gmail.com", role = c("aut", "cre")),
person("Ron", "Anafi",, email = "ranafi@gmail.com", role = c("aut", "ctb")),
person("John", "Hogenesch",, email = "hogenesch@gmail.com", role = c("aut", "ctb")),
person("Michael", "Hughes",, email = "michael.evan.hughes@gmail.com", role = c("aut", "ctb")),
person("Karl", "Kornacker",, email = "kornacker@midohio.twcbc.com", role = c("aut", "ctb"))
)
Provides two functions-meta2d and meta3d for detecting
rhythmic signals from time-series datasets. For analyzing
time-series datasets without individual information, 'meta2d' is
suggested, which could incorporates multiple methods from ARSER,
JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For
analyzing time-series datasets with individual information, 'meta3d' is
suggested, which takes use of any one of these three methods to analyze
time-series data individual by individual and gives out integrated values
based on analysis result of each individual.
Gang Wu [aut, cre],
Ron Anafi [aut, ctb],
John Hogenesch [aut, ctb],
Michael Hughes [aut, ctb],
Karl Kornacker [aut, ctb]