Preprocessing: pp

Contents

Preprocessing: `pp`#

Any transformation of the data matrix that is not a tool. Other than tools, preprocessing steps usually don’t return an easily interpretable annotation, but perform a basic transformation on the data matrix.

Basic Preprocessing#

`pp.calculate_qc_metrics`	Calculate quality control metrics.
`pp.filter_samples`	Filter samples based on number of counts or proteins.
`pp.filter_proteins`	Filter proteins based on number of counts or samples.
`pp.highly_variable_proteins`	Identify highly variable proteins.
`pp.filter_proteins_per_replicate`	Filter proteins based on detection in replicates.
`pp.aggregate_proteins`	Aggregates protein intensities across samples using a given function.
`pp.aggregate_samples`	Aggregates sample expression across samples using a given function.
`pp.normalize_total`	Normalize expression values for each sample to sum to a constant value.
`pp.drop_excess_MQ_metadata`	Drop excess metadata columns from MaxQuant output.
`pp.remove_contaminants`	Remove contaminant proteins from the data matrix.
`pp.filter_min_consecutive_fractions`	Filters for proteins present in at least min_consecutive of specified consecutive fractions.

Enrichment#

`pp.calculate_enrichment_vs_untagged`	Calculates enrichment scores and p-values by comparing tagged samples against untagged controls.
`pp.calculate_enrichment_vs_all`	Calculates enrichment of each sample against all other samples as the background.

Imputation#

pp.impute_gaussian

Impute missing values using a Gaussian distribution.