rnalysis.filtering.FoldChangeFilter

class rnalysis.filtering.FoldChangeFilter(fname: str | Path | tuple, numerator_name: str, denominator_name: str, suppress_warnings: bool = False)

A class that contains a single column, representing the gene-specific fold change between two conditions.

this class does not support ‘inf’ and ‘0’ values, and importing a file with such values could lead to incorrect filtering and statistical analyses.

Attributes

df: pandas Series

A Series that contains the fold change values. The Series is modified upon usage of filter operations.

shape: tuple (rows, columns)

The dimensions of df.

columns: list

The columns of df.

fname: pathlib.Path

The path and filename for the purpose of saving df as a csv file. Updates automatically when filter operations are applied.

index_set: set

All of the indices in the current DataFrame (which were not removed by previously used filter methods) as a set.

index_string: string

A string of all feature indices in the current DataFrame separated by newline.

numerator: str

Name of the numerator used to calculate the fold change.

denominator: str

Name of the denominator used to calculate the fold change.

__init__(fname: str | Path | tuple, numerator_name: str, denominator_name: str, suppress_warnings: bool = False)

Load a fold-change table. Valid fold-change tables should contain exactly two columns: the first column containing gene names/indices, and the second column containing log2(fold change) values.

Parameters:

• fname (Union[str, Path]) – full path/filename of the .csv file to be loaded into the Filter object

• numerator_name (str) – name of the numerator condition in the fold-change table

• denominator_name (str) – name of the denominator condition in the fold-change table

• suppress_warnings (bool (default=False)) – if True, RNAlysis will not issue warnings about the loaded table’s structure or content.

FoldChangeFilter.biotypes_from_gtf(gtf_path)	Returns a DataFrame describing the biotypes in the table and their count.
FoldChangeFilter.biotypes_from_ref_table([...])	Returns a DataFrame describing the biotypes in the table and their count.
FoldChangeFilter.concatenate(other)
FoldChangeFilter.describe([percentiles])	Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset's distribution, excluding NaN values.
FoldChangeFilter.difference(*others[, ...])	Keep only the features that exist in the first Filter object/set but NOT in the others.
FoldChangeFilter.drop_columns(columns[, inplace])	Drop specific columns from the table.
FoldChangeFilter.filter_abs_log2_fold_change([...])	Filters out all features whose absolute log2 fold change is below the indicated threshold.
FoldChangeFilter.filter_biotype_from_gtf(...)	Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).
FoldChangeFilter.filter_biotype_from_ref_table([...])	Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).
FoldChangeFilter.filter_by_attribute([...])	Filters features according to user-defined attributes from an Attribute Reference Table.
FoldChangeFilter.filter_by_go_annotations(go_ids)	Filters genes according to GO annotations, keeping only genes that are annotated with a specific GO term.
FoldChangeFilter.filter_by_kegg_annotations(...)	Filters genes according to KEGG pathways, keeping only genes that belong to specific KEGG pathway.
FoldChangeFilter.filter_by_row_name(row_names)	Filter out specific rows from the table by their name (index).
FoldChangeFilter.filter_duplicate_ids([...])	Filter out rows with duplicate names/IDs (index).
FoldChangeFilter.filter_fold_change_direction([...])	Filters out features according to the direction in which they changed between the two conditions.
FoldChangeFilter.filter_missing_values([...])	Remove all rows with missing values.
FoldChangeFilter.filter_percentile(...[, ...])	Removes all entries above the specified percentile in the specified column.
FoldChangeFilter.filter_top_n(by[, n, ...])	Sort the rows by the values of specified column or columns, then keep only the top 'n' rows.
FoldChangeFilter.find_paralogs_ensembl([...])	Find paralogs within the same species using the Ensembl database.
FoldChangeFilter.find_paralogs_panther([...])	Find paralogs within the same species using the PantherDB database.
FoldChangeFilter.from_dataframe(df, name[, ...])
FoldChangeFilter.head([n])	Return the first n rows of the Filter object.
FoldChangeFilter.histogram(column[, bins, ...])
FoldChangeFilter.intersection(*others[, ...])	Keep only the features that exist in ALL of the given Filter objects/sets.
FoldChangeFilter.majority_vote_intersection(*others)	Returns a set/string of the features that appear in at least (majority_threhold * 100)% of the given Filter objects/sets.
FoldChangeFilter.map_orthologs_ensembl(...)	Map genes to their nearest orthologs in a different species using the Ensembl database.
FoldChangeFilter.map_orthologs_orthoinspector(...)	Map genes to their nearest orthologs in a different species using the OrthoInspector database.
FoldChangeFilter.map_orthologs_panther(...)	Map genes to their nearest orthologs in a different species using the PantherDB database.
FoldChangeFilter.map_orthologs_phylomedb(...)	Map genes to their nearest orthologs in a different species using the PhylomeDB database. This function generates a table describing all matching discovered ortholog pairs (both unique and non-unique) and returns it, and can also translate the genes in this data table into their nearest ortholog, as well as remove unmapped genes.
FoldChangeFilter.number_filters(column, ...)	Applay a number filter (greater than, equal, lesser than) on a particular column in the Filter object.
FoldChangeFilter.print_features()	Print the feature indices in the Filter object, sorted by their current order in the FIlter object, and separated by newline.
FoldChangeFilter.randomization_test(ref[, ...])	Perform a randomization test to examine whether the fold change of a group of specific genomic features is significantly different than the fold change of a background set of genomic features.
FoldChangeFilter.save_csv([alt_filename])	Saves the current filtered data to a .csv file.
FoldChangeFilter.save_parquet([alt_filename])	Saves the current filtered data to a .parquet file.
FoldChangeFilter.save_table([suffix, ...])	Save the current filtered data table.
FoldChangeFilter.sort(by[, ascending, ...])	Sort the rows by the values of specified column or columns.
FoldChangeFilter.split_by_attribute(attributes)	Splits the features in the Filter object into multiple Filter objects, each corresponding to one of the specified Attribute Reference Table attributes.
FoldChangeFilter.split_by_percentile(...[, ...])	Splits the features in the Filter object into two non-overlapping Filter objects: one containing features below the specified percentile in the specfieid column, and the other containing features about the specified percentile in the specified column.
FoldChangeFilter.split_fold_change_direction()	Splits the features in the FoldChangeFilter object into two non-overlapping FoldChangeFilter objects, based on the direction of their log2(fold change).
FoldChangeFilter.symmetric_difference(other)	Returns a set/string of the WBGene indices that exist either in the first Filter object/set OR the second, but NOT in both (set symmetric difference).
FoldChangeFilter.tail([n])	Return the last n rows of the Filter object.
FoldChangeFilter.text_filters(column, ...[, ...])	Applay a text filter (equals, contains, starts with, ends with) on a particular column in the Filter object.
FoldChangeFilter.transform(function[, ...])	Transform the values in the Filter object with the specified function.
FoldChangeFilter.translate_gene_ids(translate_to)	Translates gene names/IDs from one type to another.
FoldChangeFilter.union(*others[, return_type])	Returns a set/string of the union of features between multiple Filter objects/sets (the features that exist in at least one of the Filter objects/sets).