rnalysis.filtering.Filter

class rnalysis.filtering.Filter(fname: Union[str, Path], drop_columns: Union[str, List[str]] = None)

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: Union[str, Path], drop_columns: Union[str, List[str]] = None)

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.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.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).