Discover differentially abundant time intervals

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Description

Calculate time intervals of significant differential abundance. Currently only one method is implemented (ssanova). fitSSTimeSeries is called with method="ssanova".

Usage

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fitTimeSeries(obj, formula, feature, class, time, id, method = c("ssanova"),
  lvl = NULL, include = c("class", "time:class"), C = 0, B = 1000,
  norm = TRUE, log = TRUE, sl = 1000, ...)

Arguments

obj

metagenomeSeq MRexperiment-class object.

formula

Formula for ssanova. Of the form: abundance ~ ... where ... includes any pData slot value.

feature

Name or row of feature of interest.

class

Name of column in phenoData of MRexperiment-class object for class memberhip.

time

Name of column in phenoData of MRexperiment-class object for relative time.

id

Name of column in phenoData of MRexperiment-class object for sample id.

method

Method to estimate time intervals of differentially abundant bacteria (only ssanova method implemented currently).

lvl

Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level).

include

Parameters to include in prediction.

C

Value for which difference function has to be larger or smaller than (default 0).

B

Number of permutations to perform.

norm

When aggregating counts to normalize or not.

log

Log2 transform.

sl

Scaling value.

...

Options for ssanova

Value

List of matrix of time point intervals of interest, Difference in abundance area and p-value, fit, area permutations, and call.

A list of objects including:

  • timeIntervals - Matrix of time point intervals of interest, area of differential abundance, and pvalue.

  • data - Data frame of abundance, class indicator, time, and id input.

  • fit - Data frame of fitted values of the difference in abundance, standard error estimates and timepoints interpolated over.

  • perm - Differential abundance area estimates for each permutation.

  • call - Function call.

See Also

cumNorm fitSSTimeSeries plotTimeSeries

Examples

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data(mouseData)
res = fitTimeSeries(obj=mouseData,feature="Actinobacteria",
   class="status",id="mouseID",time="relativeTime",lvl='class',B=2)

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