fitMultipleTimeSeries: Discover differentially abundant time intervals for all...

In HCBravoLab/metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Discover differentially abundant time intervals for all bacteria

Description

Calculate time intervals of significant differential abundance over all bacteria of a particularly specified level (lvl). If not lvl is specified, all OTUs are analyzed. Warning, function can take a while

Usage

fitMultipleTimeSeries(obj, lvl = NULL, B = 1, featureOrder = NULL, ...)

Arguments

obj	metagenomeSeq MRexperiment-class object.
lvl	Vector or name of column in featureData of MRexperiment-class object for aggregating counts (if not OTU level).
B	Number of permutations to perform.
featureOrder	Hierarchy of levels in taxonomy as fData colnames
...	Options for fitTimeSeries, except feature.

Value

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

A list of lists for which each includes:

• 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 fitTimeSeries

Examples

data(mouseData)
res = fitMultipleTimeSeries(obj=mouseData,lvl='phylum',class="status",
          id="mouseID",time="relativeTime",B=1)

HCBravoLab/metagenomeSeq documentation built on Dec. 20, 2024, 4:06 p.m.