rnalysis.filtering.DESeqFilter

class rnalysis.filtering.DESeqFilter(fname: Union[str, Path, tuple], drop_columns: Union[str, List[str]] = None, log2fc_col: str = 'log2FoldChange', padj_col: str = 'padj', suppress_warnings: bool = False)

A class that receives a DESeq output file and can filter it according to various characteristics.

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, tuple], drop_columns: Union[str, List[str]] = None, log2fc_col: str = 'log2FoldChange', padj_col: str = 'padj', suppress_warnings: bool = False)

Load a differential expression table. A valid differential expression table should have a column containing log2(fold change) values for each gene, and another column containing adjusted p-values for each gene.

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.

• log2fc_col (str (default='Log2FoldChange')) – name of the table column containing log2(fold change) values.

• padj_col (str (default='padj')) – name of the table column containing adjusted p-values.

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

DESeqFilter.biotypes_from_gtf(gtf_path[, ...])	Returns a DataFrame describing the biotypes in the table and their count.
DESeqFilter.biotypes_from_ref_table([...])	Returns a DataFrame describing the biotypes in the table and their count.
DESeqFilter.describe([percentiles])	Generate descriptive statistics that summarize the central tendency, dispersion and shape of the dataset’s distribution, excluding NaN values.
DESeqFilter.difference(*others[, ...])	Keep only the features that exist in the first Filter object/set but NOT in the others.
DESeqFilter.drop_columns(columns[, inplace])	Drop specific columns from the table.
DESeqFilter.filter_abs_log2_fold_change([...])	Filters out all features whose absolute log2 fold change is below the indicated threshold.
DESeqFilter.filter_biotype_from_gtf(gtf_path)	Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).
DESeqFilter.filter_biotype_from_ref_table([...])	Filters out all features that do not match the indicated biotype/biotypes (for example: 'protein_coding', 'ncRNA', etc).
DESeqFilter.filter_by_attribute([...])	Filters features according to user-defined attributes from an Attribute Reference Table.
DESeqFilter.filter_by_go_annotations(go_ids)	Filters genes according to GO annotations, keeping only genes that are annotated with a specific GO term.
DESeqFilter.filter_by_kegg_annotations(kegg_ids)	Filters genes according to KEGG pathways, keeping only genes that belong to specific KEGG pathway.
DESeqFilter.filter_by_row_name(row_names[, ...])	Filter out specific rows from the table by their name (index).
DESeqFilter.filter_duplicate_ids([keep, ...])	Filter out rows with duplicate names/IDs (index).
DESeqFilter.filter_fold_change_direction([...])	Filters out features according to the direction in which they changed between the two conditions.
DESeqFilter.filter_missing_values([columns, ...])	Remove all rows whose values in the specified columns are missing (NaN).
DESeqFilter.filter_percentile(percentile, column)	Removes all entries above the specified percentile in the specified column.
DESeqFilter.filter_significant([alpha, ...])	Removes all features which did not change significantly, according to the provided alpha.
DESeqFilter.filter_top_n(by[, n, ascending, ...])	Sort the rows by the values of specified column or columns, then keep only the top 'n' rows.
DESeqFilter.find_paralogs_ensembl([...])	Find paralogs within the same species using the Ensembl database.
DESeqFilter.find_paralogs_panther([...])	Find paralogs within the same species using the PantherDB database.
DESeqFilter.from_dataframe(df, name[, ...])
DESeqFilter.head([n])	Return the first n rows of the Filter object.
DESeqFilter.intersection(*others[, ...])	Keep only the features that exist in ALL of the given Filter objects/sets.
DESeqFilter.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.
DESeqFilter.map_orthologs_ensembl(...[, ...])	Map genes to their nearest orthologs in a different species using the Ensembl database.
DESeqFilter.map_orthologs_orthoinspector(...)	Map genes to their nearest orthologs in a different species using the OrthoInspector database.
DESeqFilter.map_orthologs_panther(...[, ...])	Map genes to their nearest orthologs in a different species using the PantherDB database.
DESeqFilter.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.
DESeqFilter.number_filters(column, operator, ...)	Applay a number filter (greater than, equal, lesser than) on a particular column in the Filter object.
DESeqFilter.print_features()	Print the feature indices in the Filter object, sorted by their current order in the FIlter object, and separated by newline.
DESeqFilter.save_csv([alt_filename])	Saves the current filtered data to a .csv file.
DESeqFilter.save_parquet([alt_filename])	Saves the current filtered data to a .parquet file.
DESeqFilter.save_table([suffix, alt_filename])	Save the current filtered data table.
DESeqFilter.sort(by[, ascending, ...])	Sort the rows by the values of specified column or columns.
DESeqFilter.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.
DESeqFilter.split_by_percentile(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.
DESeqFilter.split_fold_change_direction()	Splits the features in the DESeqFilter object into two non-overlapping DESeqFilter objects, based on the direction of their log2foldchange.
DESeqFilter.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).
DESeqFilter.tail([n])	Return the last n rows of the Filter object.
DESeqFilter.text_filters(column, operator, value)	Applay a text filter (equals, contains, starts with, ends with) on a particular column in the Filter object.
DESeqFilter.transform(function[, columns, ...])	Transform the values in the Filter object with the specified function.
DESeqFilter.translate_gene_ids(translate_to)	Translates gene names/IDs from one type to another.
DESeqFilter.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).
DESeqFilter.volcano_plot([alpha, ...])	Plots a volcano plot (log2(fold change) vs -log10(adj.