model_fit: statistic tests for HTSet
In zhuchcn/HTSet: High through-put experiment dataset container

Description Usage Arguments Value Author(s) See Also Examples

This is a wrapper of several widely used statistical method for high through-put experimental data such as RNAseq. The limma-package performs linear model on continous data, or cooperate with the voom function to handle count data. The edgeR-package and DESeq2-package performs negative binomial generalized linear model on count data.

model_fit(
  object,
  design,
  coef,
  engine,
  args = list(),
  transform,
  adjust.method = "BH"
)

`object`	`HTSet`
`design`	matrix. Number of rows must batch number of samples of object. Usually the output of `model.matrix`.
`coef`	character. The coefficient to perform statistical tests. Must be in the column names of design.
`engine`	character. The engine to perform statistical analysis. Supported are limma, edgeR, and DESeq2.
`args`	list. A list of argumnets to be parsed to the backend statistical engine.
`transform`	function. The transform to be passed to `transform_by_sample`. No transform is performed by default.
`adjust.method`	character. Method used to adjust the p-values for multiple testing. See `p.adjust` for the complete list of supported methods. Default is "BH" for Benjamini-Hochberg test.

A list-like S3 class ModelFit object is returned with the elements as following.

`results`	A data.frame of the statistical test results for each gene/ feature. logFC estimate of the log2-fold-change corresponding to the effect. mean average log2-expression for the gene/feature accross all samples. Same as the AveExpr in limma's `topTable`, and the baseMean in DESeq2's `results`. stat the statistic value of the corresponding test. When using limma, this is the t-statistic value, same as the t column in the result of `topTable`. For edgeR, this is the f-statistic value for Quansi-likelihood test or the likelihood ratio statistic value for likelihood ratio test. Same as the column F or RT in the result of edgeR's `topTags`. As for DESeq2, this is the Wald statistic value for Wald test or the difference in deviance between the reduced model and the full model for likelihood ratio test. same as the stat column in the output of DESeq2's `results`. pval raw p-value padj adjusted p-value
`adjust.method`	Method used to correct for multiple testing
`design`	design matrix
`df`	degree of freedoms
`distribution`	The distribution that p values were calculated
`engine`	package used for statistical test
`coef`	coefficient tested
`params`	additional parameters parsed

Chenghao Zhu

HTSet-class lmFit voom glmQLFit glmLRT DESeq

data(exrna)
design = model.matrix(~ Condition, data = exrna$pdata)
coef = "ConditionSystemic Lupus Erythematosus"
fit1 = model_fit(object = exrna, design = design, coef = coef, engine = "limma", args = list(voom = TRUE))
fit2 = model_fit(object = exrna, design = design, coef = coef, engine = "edgeR")
fit3 = model_fit(object = exrna, design = design, coef = coef, engine = "edgeR", args = list(model = "lrt"))
fit4 = model_fit(object = exrna, design = design, coef = coef, engine = "DESeq2")