ImpulseDE2Object-class: Container class for ImpulseDE2 output
In ImpulseDE2: Differential expression analysis of longitudinal count data sets

ImpulseDE2 output and intermediate results such as model fits.

dfDEAnalysis

(data frame samples x reported characteristics) Summary of fitting procedure and differential expression results for each gene.

Gene: Gene ID.
p: P-value for differential expression.
padj: Benjamini-Hochberg false-discovery rate corrected p-value for differential expression analysis.
loglik_full: Loglikelihood of full model.
loglik_red: Loglikelihood of reduced model.
df_full: Degrees of freedom of full model.
df_red: Degrees of freedom of reduced model
mean: Inferred mean parameter of constant model of first batch. From combined samples in case-ctrl.
allZero (bool) Whether there were no observed non-zero observations of this gene. If TRUE, fitting and DE analsysis were skipped and entry is NA.

Entries only present in case-only DE analysis:

converge_impulse: Convergence status of optim for impulse model fit (full model).
converge_const: Convergence status of optim for constant model fit (reduced model).

Entries only present in case-control DE analysis:

converge_combined: Convergence status of optim for impulse model fit to case and control samples combined (reduced model).
converge_case: Convergence status of optim for impulse model fit to samples of case condition (full model 1/2).
converge_control: Convergence status of optim for impulse model fit to samples of control condition (full model 2/2).

Entries only present if boolIdentifyTransients is TRUE:

converge_sigmoid: Convergence status of optim for sigmoid model fit to samples of case condition.
impulseTOsigmoid_p: P-value of loglikelihood ratio test impulse model fit versus sigmoidal model on samples of case condition.
impulseTOsigmoid_padj: Benjamini-Hochberg false-discovery rate corrected p-value of loglikelihood ratio test impulse model fit versus sigmoid model on samples of case condition.
sigmoidTOconst_p: P-value of loglikelihood ratio test sigmoidal model fit versus constant model on samples of case condition.
sigmoidTOconst_padj: Benjamini-Hochberg false-discovery rate corrected p-value of loglikelihood ratio test sigmoidal model fit versus constant model on samples of case condition.
isTransient (bool) Whether gene is transiently activated or deactivated and differentially expressed.
isMonotonous (bool) Whether gene is not transiently activated or deactivated and differentially expressed. This scenario corresponds to a montonous expression level increase or decrease.

vecDEGenes

(list number of genes) Genes IDs identified as differentially expressed by ImpulseDE2 at threshold scaQThres.

lsModelFits

(list length number of conditions fit (1 or 3)) '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 3) 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.
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.
  - ls SigmoidFit (list) List of sigmoidal fit parameters and results. NULL if boolIdentifyTransients is FALSE.
    - vecSigmoidParam (numeric vector length 4) beta, h0, h1, t Maximum likelihood estimators of sigmoidal model parameters.
    - vecSigmoidValue (numeric vector length number of time points) Values of sigmoid 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 sigmoidal model.

matCountDataProc

(matrix genes x samples) [Default NULL] Read count data, unobserved entries are NA. Processed matrix.

dfAnnotationProc

(data frame samples x covariates) Sample, Condition, Time (numeric), TimeCateg (str) (and confounding variables if given). Annotation table with covariates for each sample. Processed table.

vecDispersions

(numeric vector number of samples) Gene-wise negative binomial dispersion hyper-parameters.

vecSizeFactors

(numeric vector number of samples) Model scaling factors for each sample which take sequencing depth into account (size factors).

boolCaseCtrl

(bool) Whether to perform case-control analysis. Does case-only analysis if FALSE.

vecConfounders

(vector of strings number of confounding variables) Factors to correct for during batch correction. Have to supply dispersion factors if more than one is supplied. Names refer to columns in dfAnnotation.

scaNProc