rnalysis.filtering.CountFilter.differential_expression_limma_voom

CountFilter.differential_expression_limma_voom(design_matrix: str | Path, comparisons: Iterable[Tuple[str, str, str]], covariates: Iterable[str] = (), lrt_factors: Iterable[str] = (), model_factors: Literal['auto'] | Iterable[str] = 'auto', r_installation_folder: str | Path | Literal['auto'] = 'auto', output_folder: str | Path | None = None, random_effect: str | None = None, quality_weights: bool = False, return_design_matrix: bool = False, return_code: bool = False, return_log: bool = False) → Tuple[DESeqFilter, ...]

Run differential expression analysis on the count matrix using the Limma-Voom pipeline. The count matrix you are analyzing should be normalized (typically to Reads Per Million). The analysis will be based on a design matrix supplied by the user. The design matrix should contain at least two columns: the first column contains all the sample names, and each of the following columns contains an experimental design factor (e.g. ‘condition’, ‘replicate’, etc). (see the User Guide and Tutorial for a complete example). The analysis formula will contain all the factors in the design matrix. To run this function, a version of R must be installed.

param design_matrix:

path to a csv file containing the experiment’s design matrix. The design matrix should contain at least two columns: the first column contains all the sample names, and each of the following columns contains an experimental design factor (e.g. ‘condition’, ‘replicate’, etc). (see the User Guide and Tutorial for a complete example). The analysis formula will contain all the factors in the design matrix.

type design_matrix:

str or Path

Parameters:

comparisons – specifies what comparisons to build results tables out of. each individual comparison should be a tuple with exactly three elements: the name of a factor in the design formula, the name of the numerator level for the fold change, and the name of the denominator level for the fold change. :type comparisons: Iterable of tuple(factor, numerator_value, denominator_value) :param lrt_factors: optionally, specify factors to be tested using the likelihood ratio test (LRT). If the factors are a continuous variable, you can also specify the number of polynomial degree to fit. :type lrt_factors: Iterable of tuple(factor, polynomial_degree) or None (default=None) :param covariates: optionally, specify a list of continuous covariates to include in the analysis. The covariates should be column names in the design matrix. The reported fold change values correspond to the expected fold change for every increase of 1 unit in the covariate. :param model_factors: optionally, specify a list of factors to include in the differential expression model. If ‘auto’, all factors in the design matrix will be included. :type model_factors: Iterable of factor names or ‘auto’ (default=’auto’) :param r_installation_folder: Path to the installation folder of R. For example: ‘C:/Program Files/R/R-4.2.1’ :type r_installation_folder: str, Path, or ‘auto’ (default=’auto’) :param output_folder: Path to a folder in which the analysis results, as well as the log files and R script used to generate them, will be saved. if output_folder is None, the results will not be saved to a specified directory. :type output_folder: str, Path, or None :param random_effect: optionally, specify a single factor to model as a random effect. This is useful when your experimental design is nested. limma-voom can only treat one factor as a random effect. :type random_effect: str or None :param quality_weights: if True, the analysis will use estimate sample-specific quality weights using the ‘arrayWeights’ function im limma. This is useful when lower quality samples are present in the data. :type quality_weights: bool (default=False) :param return_design_matrix: if True, the function will return the sanitized design matrix used in the analysis. :type return_design_matrix: bool (default=False) :param return_code: if True, the function will return the path to the R script used to generate the analysis results. :type return_code: bool (default=False) :param return_log: if True, the function will return the path to the analysis logfile, which includes session info. :type return_log: bool (default=False) :return: a tuple of DESeqFilter objects, one for each comparison