Maaslin: MaAsLin is a multivariate statistical framework that finds...
In pooranis/maaslin: Maaslin

Description Usage Arguments Author(s) Examples

View source: R/Maaslin.R

MaAsLin performs boosted additive general linear models between one group of data (metadata/the predictors) and another group (in our case relative taxonomic abundances/the response). Used to discover associations between clinical metadata and microbial community relative abundance or function

Maaslin(strInputTSV,
	 strOutputDIR, 
	 strInputConfig = NULL, 
	 strInputR = NULL, 
	 dSignificanceLevel = 0.25,
	 dMinAbd = 0.0001,
	 dMinSamp = 0.1,
	 dOutlierFence = 0, 
	 dPOutlier = 0.05, 
	 strRandomCovariates = NULL, 
	 strMultTestCorrection = "BH",
	 fZeroInflated = FALSE, 
	 strModelSelection = "boost",
	 strMethod = "lm",
	 strTransform = "asinsqrt",
	 fNoQC = FALSE,
	 strForcedPredictors = NULL, 
	 strNoImpute = NULL,
	 strVerbosity = "DEBUG",
	 fOmitLogFile = FALSE,
	 fInvert = FALSE,
	 dSelectionFrequency = NA, 
	 fAllvAll = FALSE,
	 fPlotNA = FALSE, 
	 dPenalizedAlpha = 0.95, 
	 sAlternativeLibraryLocation = NULL)

`strInputTSV`	The main INPUT file: The sample file is maaslin_demo2.tsv
`strOutputDIR`	Output Directory
`strInputConfig`	Input Config file: The sample is located in data/maaslin_demo2.read.config
`strInputR`	Optional configuration script normalizing or processing data
`dSignificanceLevel`	Threshold to use for significance for the generated q-values (BH FDR). Anything equal to or lower than this is significant.
`dMinAbd`	Minimum relative abundance allowed in the data. Values below this are removed and imputed as the median of the sample data.
`dMinSamp`	Minimum percentage of samples in which a feature must have the minimum relative abundance in order not to be removed. Also this is the maximum percentage of samples for which a metadata can have NAs before being removed.
`dOutlierFence`	Outliers are defined as this number times the interquartile range added/subtracted from the 3rd/1st quartiles respectively. If set to 0 (default), outliers are defined by the Grubbs test.
`dPOutlier`	This is the significance cuttoff used to indicate an outlier or not. The closer to zero, the more significant an outlier must be to be removed.
`strRandomCovariates`	These metadata will be treated as random covariates. Comma delimited data feature names. These features must be listed in the read.config file. Example '-R RandomMetadata1,RandomMetadata2'.
`strMultTestCorrection`	This indicates which multiple hypothesis testing method will be used, available are holm, hochberg, hommel, bonferroni, BH, BY.
`fZeroInflated`	If true, the zero inflated version of the inference model indicated in -m is used. For instance if using lm, zero-inflated regression on a gaussian distribution is used.
`strModelSelection`	Indicates which of the variable selection techniques to use. Default=boost
`strMethod`	Indicates which of the statistical inference methods to run. Default=lm
`strTransform`	Indicates which link or transformation to use with a glm, if glm is not selected this argument will be set to none. Default=asinsqrt
`fNoQC`	Indicates if the quality control will be ran on the metadata/data. Default is FALSE
`strForcedPredictors`	Metadata features that will be forced into the model seperated by commas. These features must be listed in the read.config file. Example '-F Metadata2,Metadata6,Metadata10'.
`strNoImpute`	These data will not be imputed. Comma delimited data feature names. Example '-n Feature1,Feature4,Feature6'.
`strVerbosity`	Debug level
`fOmitLogFile`	Including this flag will stop the creation of the output log file. Default=FALSE
`fInvert`	When given, flag indicates to invert the background of figures to black. Defaule = FALSE
`dSelectionFrequency`	Selection Frequency for boosting (max 1 will remove almost everything). Interpreted as requiring boosting to select metadata 100
`fAllvAll`	When given, the flag indicates that each fixed covariate that is not indicated as Forced is compared once at a time per data feature (bug). Made to be used with the -F option to specify one part of the model while allowing the other to cycle through a group of covariates. Does not affect Random covariates, which are always included when specified.
`fPlotNA`	Plot data that was originally NA, by default they are not plotted. Default=FALSE
`dPenalizedAlpha`	The alpha for penalization (1.0=L1 regularization, LASSO; 0.0=L2 regularization, ridge regression.
`sAlternativeLibraryLocation`	An alternative location to find the lib directory. This dir and children will be searched for the first maaslin/src/lib dir.

Timothy Tickle<ttickle@hsph.harvard.edu>,
Curtis Huttenhower <chuttenh@hsph.harvard.edu>
Maintainers: Ayshwarya Subramanian<subraman@broadinstitute.org>,
Lauren McIver<lauren.j.mciver@gmail.com>,
George Weingart<george.weingart@gmail.com>

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InputTSV <- system.file('extdata','maaslin_demo2.tsv', package="Maaslin")
InputConfig <-system.file('extdata','maaslin_demo2.read.config', package="Maaslin")
Maaslin(InputTSV,'maaslin_example_output',strInputConfig=InputConfig)