rnalysis.filtering.Filter

class rnalysis.filtering.Filter(fname: str | Path, drop_columns: str | List[str] = None, suppress_warnings: bool = False)

An all-purpose Filter object.

Attributes

df: pandas DataFrame

A DataFrame that contains the DESeq output file contents. The DataFrame 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.

__init__(fname: str | Path, drop_columns: str | List[str] = None, suppress_warnings: bool = False)

Load a table.

Parameters:

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

  • drop_columns (str, list of str, or None (default=None)) – if a string or list of strings are specified, the columns of the same name/s will be dropped from the loaded table.

Examples:
>>> from rnalysis import filtering
>>> d = filtering.Filter("tests/test_files/counted.csv")

Filter.biotypes_from_gtf(gtf_path[, ...])

Returns a DataFrame describing the biotypes in the table and their count.

Filter.biotypes_from_ref_table([...])

Returns a DataFrame describing the biotypes in the table and their count.

Filter.concatenate(other)

Filter.describe([percentiles])

Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset's distribution, excluding NaN values.

Filter.difference(*others[, return_type, ...])

Keep only the features that exist in the first Filter object/set but NOT in the others.

Filter.drop_columns(columns[, inplace])

Drop specific columns from the table.

Filter.filter_biotype_from_gtf(gtf_path[, ...])

Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).

Filter.filter_biotype_from_ref_table([...])

Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).

Filter.filter_by_attribute([attributes, ...])

Filters features according to user-defined attributes from an Attribute Reference Table.

Filter.filter_by_go_annotations(go_ids[, ...])

Filters genes according to GO annotations, keeping only genes that are annotated with a specific GO term.

Filter.filter_by_kegg_annotations(kegg_ids)

Filters genes according to KEGG pathways, keeping only genes that belong to specific KEGG pathway.

Filter.filter_by_row_name(row_names[, ...])

Filter out specific rows from the table by their name (index).

Filter.filter_duplicate_ids([keep, ...])

Filter out rows with duplicate names/IDs (index).

Filter.filter_missing_values([columns, ...])

Remove all rows whose values in the specified columns are missing (NaN).

Filter.filter_percentile(percentile, column)

Removes all entries above the specified percentile in the specified column.

Filter.filter_top_n(by[, n, ascending, ...])

Sort the rows by the values of specified column or columns, then keep only the top 'n' rows.

Filter.find_paralogs_ensembl([organism, ...])

Find paralogs within the same species using the Ensembl database.

Filter.find_paralogs_panther([organism, ...])

Find paralogs within the same species using the PantherDB database.

Filter.from_dataframe(df, name)

Filter.head([n])

Return the first n rows of the Filter object.

Filter.histogram(column[, bins, x_label, ...])

Filter.intersection(*others[, return_type, ...])

Keep only the features that exist in ALL of the given Filter objects/sets.

Filter.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.

Filter.map_orthologs_ensembl(map_to_organism)

Map genes to their nearest orthologs in a different species using the Ensembl database.

Filter.map_orthologs_orthoinspector(...[, ...])

Map genes to their nearest orthologs in a different species using the OrthoInspector database.

Filter.map_orthologs_panther(map_to_organism)

Map genes to their nearest orthologs in a different species using the PantherDB database.

Filter.map_orthologs_phylomedb(map_to_organism)

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.

Filter.number_filters(column, operator, value)

Applay a number filter (greater than, equal, lesser than) on a particular column in the Filter object.

Filter.print_features()

Print the feature indices in the Filter object, sorted by their current order in the FIlter object, and separated by newline.

Filter.save_csv([alt_filename])

Saves the current filtered data to a .csv file.

Filter.save_parquet([alt_filename])

Saves the current filtered data to a .parquet file.

Filter.save_table([suffix, alt_filename])

Save the current filtered data table.

Filter.sort(by[, ascending, na_position, ...])

Sort the rows by the values of specified column or columns.

Filter.split_by_attribute(attributes[, ref])

Splits the features in the Filter object into multiple Filter objects, each corresponding to one of the specified Attribute Reference Table attributes.

Filter.split_by_percentile(percentile, column)

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.

Filter.symmetric_difference(other[, return_type])

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).

Filter.tail([n])

Return the last n rows of the Filter object.

Filter.text_filters(column, operator, value)

Applay a text filter (equals, contains, starts with, ends with) on a particular column in the Filter object.

Filter.transform(function[, columns, inplace])

Transform the values in the Filter object with the specified function.

Filter.translate_gene_ids(translate_to[, ...])

Translates gene names/IDs from one type to another.

Filter.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).