fitModels: Fits impulse and constant models to a timecourse dataset
In ImpulseDE2: Differential expression analysis of longitudinal count data sets

Description Usage Arguments Value Author(s) See Also

View source: R/srcImpulseDE2_fitImpulse.R

[Model fitting function hierarchy: 1 out of 4] This primary wrapper coordinates fitting of impulse and constant model to separate conditions according to the differential expression mode (case-only or case-control).

1	fitModels(objectImpulseDE2, vecConfounders, boolCaseCtrl)

`objectImpulseDE2`	(object class ImpulseDE2Object) Object to be fit.
`vecConfounders`	(vector of strings number of confounding variables) Factors to correct for during batch correction. Names refer to columns in dfAnnotation.
`boolCaseCtrl`	(bool) Whether to perform case-control analysis. Does case-only analysis if FALSE.

objectImpulseDE2 (object class ImpulseDE2Object) Object with sigmoidal fit added: objectImpulseDE2@lsModelFits is updated to: lsModelFits (list length number of conditions fit (1 or 3) +1) {'case'} or {'case', 'control', 'combined'} One model fitting object for each condition: In case-only DE analysis, only the condition {'case'} is fit. In case-control DE analysis, the conditions {'case', 'control','combined} are fit. Each condition entry is a list of model fits for each gene. Each gene entry is a list of model fits to the individual models: Impulse model and constant model (if boolFitConst is TRUE). At this level, the sigmoid model fit can be added later. Each model fit per gene is a list of fitting parameters and results.

IdxGroups (list length number of conditions) Samples grouped by time points and by batches and time point vectors. Sample groups are stored in the form of index vectors in which samples of the same time point or batch have the same index.
- Condition ID (list length 5) List of index vectors and time points. One entry of this format for each condition.
  - vecTimepointsUnique (numeric vector length number of unique timepoints) Vector of unique time coordinates observed in this condition.
  - vecidxTimepoint (idx vector length number of samples) Index of the time coordinates of each sample (reference is vecTimepointsUnique).
  - lsvecBatchUnique (list number of confounders) List of string vectors. One vector per confounder: vector of unique batches in this confounder.
  - lsvecidxBatches (idx list length number of confounding variables) List of index vectors. One vector per confounding variable. Each vector has one entry per sample with the index of the batch ID within the given confounding variable of the given sample. Reference is the list of unique batch ids for each confounding variable.
  - vecSamples (vector number of samples) Names of samples fit for this condition in same order as index vectors above.
Condition ID (list length number of genes) List of fits for each gene to the samples of this condition. One entry of this format for all conditions fit.
- Gene ID (list length 2) Impulse and constant model fit to gene observations. One entry of this format for all gene IDs.
  - lsImpulseFit (list) List of impulse fit parameters and results.
    - vecImpulseParam (numeric vector length 6) {beta, h0, h1, h2, t1, t2} Maximum likelihood estimators of impulse model parameters.
    - vecImpulseValue (numeric vector length number of time points) Values of impulse model fit at time points used for fit.
    - lsvecBatchFactors (list length number of confounders) List of vectors of scalar batch correction factors for each sample. These are also maximum likelihood estimators. NULL if no confounders given.
    - scaDispParam (scalar) Dispersion parameter estimate used in fitting (hyper-parameter).
    - scaLL (scalar) Loglikelihood of data under maximum likelihood estimator model.
    - scaConvergence (scalar) Convergence status of optim on impulse model.
  - lsConstFit (list) List of constant fit parameters and results.
    - scaMu (scalar) Maximum likelihood estimator of negative binomial mean parameter.
    - lsvecBatchFactors (list length number of confounders) List of vectors of scalar batch correction factors for each sample. These are also maximum likelihood estimators. NULL if no confounders given.
    - scaDispParam (scalar) Dispersion parameter estimate used in fitting (hyper-parameter).
    - scaLL (scalar) Loglikelihood of data under maximum likelihood estimator model.
    - scaConvergence (scalar) Convergence status of optim on constant model.