rnalysis.filtering.CountFilter.differential_expression_limma_voom_simplified

CountFilter.differential_expression_limma_voom_simplified(design_matrix: str | Path, comparisons: Iterable[Tuple[str, str, str]], 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 simplified mode supports only pairwise comparisons. 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 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 R script used to generate the analysis results.

type return_code:

bool (default=False)

return:

a tuple of DESeqFilter objects, one for each comparison