rnalysis.filtering.CountFilter.differential_expression_limma_voom

CountFilter.differential_expression_limma_voom(design_matrix: Union[str, Path], comparisons: Iterable[Tuple[str, str, str]], r_installation_folder: Union[str, Path, Literal['auto']] = 'auto', output_folder: Optional[Union[str, Path]] = None, random_effect: Optional[str] = None) → 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.

Parameters

  • design_matrix (str or Path) – 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.

  • comparisons (Iterable of tuple(factor, numerator_value, denominator_value)) – 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.

  • r_installation_folder (str, Path, or 'auto' (default='auto')) – Path to the installation folder of R. For example: ‘C:/Program Files/R/R-4.2.1’

  • output_folder (str, Path, or None) – 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.

  • random_effect (str or None) – 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.

Returns

a tuple of DESeqFilter objects, one for each comparison