fitTimeSeries: Discover differentially abundant time intervals

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

Discover differentially abundant time intervals

Description

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

Usage

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,
  featureOrder = NULL,
  ...
)

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.
featureOrder	Hierarchy of levels in taxonomy as fData colnames
...	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

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

HCBravoLab/metagenomeSeq documentation built on March 17, 2024, 3:21 p.m.