From 1280eecd3557c15614a0a10a3f421c1fba825ae7 Mon Sep 17 00:00:00 2001 From: GuyTeichman <48219633+GuyTeichman@users.noreply.github.com> Date: Fri, 9 Jun 2023 19:24:18 +0300 Subject: [PATCH] updated docs for V3.9.0 --- HISTORY.rst | 4 +- docs/build/.buildinfo | 2 +- docs/build/.doctrees/api_reference.doctree | Bin 6709491 -> 6834895 bytes docs/build/.doctrees/environment.pickle | Bin 744660 -> 761861 bytes docs/build/.doctrees/history.doctree | Bin 179466 -> 194564 bytes docs/build/.doctrees/index.doctree | Bin 18220 -> 18220 bytes docs/build/.doctrees/readme.doctree | Bin 54560 -> 55080 bytes docs/build/.doctrees/rnalysis.doctree | Bin 4849561 -> 4957130 bytes ...nrichment.FeatureSet.go_enrichment.doctree | Bin 163843 -> 167966 bytes ...ichment.FeatureSet.kegg_enrichment.doctree | Bin 111951 -> 118336 bytes ...FeatureSet.user_defined_enrichment.doctree | Bin 76976 -> 83379 bytes ...enrichment.RankedSet.go_enrichment.doctree | Bin 163827 -> 167950 bytes 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docs/build/rnalysis.filtering.FoldChangeFilter.save_table.html create mode 100644 docs/source/rnalysis.filtering.CountFilter.save_parquet.rst create mode 100644 docs/source/rnalysis.filtering.CountFilter.save_table.rst create mode 100644 docs/source/rnalysis.filtering.DESeqFilter.save_parquet.rst create mode 100644 docs/source/rnalysis.filtering.DESeqFilter.save_table.rst create mode 100644 docs/source/rnalysis.filtering.Filter.save_parquet.rst create mode 100644 docs/source/rnalysis.filtering.Filter.save_table.rst create mode 100644 docs/source/rnalysis.filtering.FoldChangeFilter.save_parquet.rst create mode 100644 docs/source/rnalysis.filtering.FoldChangeFilter.save_table.rst diff --git a/HISTORY.rst b/HISTORY.rst index 2df4aa9b..77efe47d 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -13,13 +13,13 @@ and addresses multiple bug fixes, including issues with automatic session report Added ******* * Added additional parameters to enrichment bar plots (enrichment.enrichment_bar_plot), including a new plot style ('lollipop') and observed/expected labels on the graph. -* Added new parameter to Principal Component Analysis plots (CountFilter.pca) 'plot_grid', which can enable or disable adding a grid to PCA plots. +* Added a new parameter to Principal Component Analysis plots (CountFilter.pca) 'plot_grid', which can enable or disable adding a grid to PCA plots. * RNAlysis can now load and save data tables in Parquet format (.parquet) * Added new actions to the Help menu, allowing users to report issues, suggest issues, or open discussions. Changed ******** -* Functions in the FASTQ model are now added to automatic session report. +* Functions in the FASTQ model are now added to automatic session reports. * Many of the functions in RNAlysis should now run faster. * Font type, size, and color for help tooltips should now match the global font settings. * True/False toggle switches now scale with font size. diff --git a/docs/build/.buildinfo b/docs/build/.buildinfo index f28187ec..a92a4d22 100644 --- a/docs/build/.buildinfo +++ b/docs/build/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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-
-
-_is_paired_end
-

indicates whether it is single-end or paired-end Pipeline

-
-
static _param_string(args: tuple, kwargs: dict)
@@ -207,10 +201,12 @@

rnalysis.fastq module

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-
-
-functions
-
+
+
+ functions
+

list of functions to perform

+
+
@@ -229,10 +225,12 @@

rnalysis.fastq module

-
-
-params
-
+
+
+ params
+

list of function parameters

+
+
@@ -279,12 +277,6 @@

rnalysis.fastq module

-
-
-_is_paired_end
-

indicates whether it is single-end or paired-end Pipeline

-
-
static _param_string(args: tuple, kwargs: dict)
@@ -340,10 +332,12 @@

rnalysis.fastq module

-
-
-functions
-
+
+
+ functions
+

list of functions to perform

+
+
@@ -362,10 +356,12 @@

rnalysis.fastq module

-
-
-params
-
+
+
+ params
+

list of function parameters

+
+
@@ -388,8 +384,10 @@

rnalysis.fastq module

-
-class rnalysis.fastq._FASTQPipeline(is_paired_end: bool = False)
+
+ class rnalysis.fastq._FASTQPipeline

Bases: GenericPipeline, ABC

@@ -412,12 +410,6 @@

rnalysis.fastq module

-
-
-_is_paired_end
-

indicates whether it is single-end or paired-end Pipeline

-
-
static _param_string(args: tuple, kwargs: dict)
@@ -473,10 +465,12 @@

rnalysis.fastq module

-
-
-functions
-
+
+
+ functions
+

list of functions to perform

+
+
@@ -495,10 +489,12 @@

rnalysis.fastq module

-
-
-params
-
+
+
+ params
+

list of function parameters

+
+
@@ -941,8 +937,34 @@

rnalysis.filtering module -
-static _pca_plot(final_df: DataFrame, pc1_var: float, pc2_var: float, sample_grouping: GroupedColumns, labels: bool, title: str, title_fontsize: float, label_fontsize: float, tick_fontsize: float, proportional_axes: bool) Figure
+
+ static _pca_plot(final_df: DataFrame, pc1_var: float, pc2_var: float, sample_grouping: GroupedColumns, + labels: bool, + title: str, + title_fontsize: float, label_fontsize: float, tick_fontsize: float, proportional_axes: bool, + plot_grid: bool) Figure

Internal method, used to plot the results from CountFilter.pca().

Parameters
@@ -2277,8 +2299,58 @@

rnalysis.filtering module -
-pca(samples: Union[GroupedColumns, Literal['all']] = 'all', n_components: PositiveInt = 3, power_transform: bool = True, labels: bool = True, title: Union[str, Literal['auto']] = 'auto', title_fontsize: float = 20, label_fontsize: float = 16, tick_fontsize: float = 12, proportional_axes: bool = False) Tuple[PCA, List[Figure]]
+
+ pca(samples: Union[GroupedColumns, Literal['all']] = 'all', n_components: PositiveInt = 3, power_transform: bool = True, labels: bool = True, title: Union[str, Literal['auto']] = 'auto', title_fontsize: float = 20, label_fontsize: float = 16, tick_fontsize: float = 12, proportional_axes: bool = False, plot_grid: bool = True) Tuple[PCA, List[Figure]]

Performs Principal Component Analysis (PCA), visualizing the principal components that explain the most variance between the different samples. The function will standardize the data prior to PCA, and then plot the requested number of pairwise PCA projections.

Parameters
@@ -2292,6 +2364,8 @@

rnalysis.filtering moduleReturns

@@ -2367,25 +2441,181 @@

rnalysis.filtering module -
-save_csv(alt_filename: Optional[Union[str, Path]] = None)
-

Saves the current filtered data to a .csv file.

-
-
Parameters
-

alt_filename (str, pathlib.Path, or None (default)) – If None, file name will be generated automatically according to the filtering methods used. If it’s a string, it will be used as the name of the saved file. Example input: ‘myfilename’

-
-
-

- -
-
-scatter_sample_vs_sample(sample1: ColumnNames, sample2: ColumnNames, xlabel: Union[str, Literal['auto']] = 'auto', ylabel: Union[str, Literal['auto']] = 'auto', title: Union[str, Literal['auto']] = 'auto', title_fontsize: float = 20, label_fontsize: float = 16, tick_fontsize: float = 12, annotation_fontsize: float = 10, highlight: Optional[Sequence[str]] = None, point_color: Color = '#6d7178', highlight_color: Color = '#00aaff', opacity: Fraction = 0.65, point_size: float = 10, interactive: bool = True, show_cursor: bool = False) Figure
-

Generate a scatter plot where every dot is a feature, the x value is log10 of reads (counts, RPM, RPKM, TPM, etc) in sample1, the y value is log10 of reads in sample2. If the plot is generated in interactive mode, data points can be labeled by clicking on them.

-
-
Parameters
-
    -
  • sample1 (string or list of strings) – Name of the first sample from the CountFilter object. If sample1 is a list, they will be avarged as replicates.

  • -
  • sample2 (string or list of strings) – Name of the second sample from the CountFilter object. If sample2 is a list, they will be averaged as replicates.

  • +
    + save_csv(alt_filename: Optional[Union[str, Path]] = None)
    +

    Saves the current filtered data to a .csv file.

    +
    +
    Parameters
    +

    alt_filename (str, pathlib.Path, + or None (default)) – If None, file name will be generated + automatically according to the filtering methods used. If it’s a string, it will be used as the name of + the saved file. Example input: ‘myfilename’

    +
    +
    +
    +
+ +
+
+ save_parquet(alt_filename: Optional[Union[str, Path]] = None)
+

Saves the current filtered data to a .parquet file.

+
+
Parameters
+

alt_filename (str, pathlib.Path, + or None (default)) – If None, file name will be generated + automatically according to the filtering methods used. If it’s a string, it will be used as the name + of the saved file. Example input: ‘myfilename’

+
+
+
+
+ +
+
+ save_table(suffix: Literal['.csv', '.tsv', '.parquet'] = '.csv', alt_filename: Optional[Union[str, Path]] = None)
+

Save the current filtered data table.

+
+
Parameters
+
+
    +
  • suffix ('.csv', '.tsv', or '.parquet' + (default='.csv')) – the file suffix

  • +
  • alt_filename (str, pathlib.Path, or None + (default)) – If None, file name will be generated automatically + according to the filtering methods used. If it’s a string, it will be used as the name of + the saved file. Example input: ‘myfilename’

  • +
+
+
+
+
+ +
+
+ scatter_sample_vs_sample(sample1: ColumnNames, + sample2: ColumnNames, + xlabel: Union[str, Literal['auto']] = 'auto', ylabel: Union[str, Literal['auto']] = 'auto', title: Union[str, Literal['auto']] = 'auto', title_fontsize: float = 20, label_fontsize: float = 16, tick_fontsize: float = 12, annotation_fontsize: float = 10, highlight: Optional[Sequence[str]] = None, point_color: Color = '#6d7178', highlight_color: Color = '#00aaff', opacity: Fraction = 0.65, point_size: float = 10, interactive: bool = True, show_cursor: bool = False) Figure
+

Generate a scatter plot where every dot is a feature, the x value is log10 of reads (counts, RPM, RPKM, + TPM, etc) in sample1, the y value is log10 of reads in sample2. If the plot is generated in interactive + mode, data points can be labeled by clicking on them.

+
+
Parameters
+
+
- -
-
-sort(by: Union[str, List[str]], ascending: Union[bool, List[bool]] = True, na_position: str = 'last', inplace: bool = True)
-

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

-
-
Parameters
-
    -
  • by (str or list of str) – Names of the column or columns to sort by.

  • -
  • ascending (bool or list of bool, default True) – Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, it must have the same length as ‘by’.

  • +
    + save_csv(alt_filename: Optional[Union[str, Path]] = None)
    +

    Saves the current filtered data to a .csv file.

    +
    +
    Parameters
    +

    alt_filename (str, pathlib.Path, + or None (default)) – If None, file name will be generated + automatically according to the filtering methods used. If it’s a string, it will be used as the name of + the saved file. Example input: ‘myfilename’

    +
    +
    +
    +
+ +
+
+ save_parquet(alt_filename: Optional[Union[str, Path]] = None)
+

Saves the current filtered data to a .parquet file.

+
+
Parameters
+

alt_filename (str, pathlib.Path, + or None (default)) – If None, file name will be generated + automatically according to the filtering methods used. If it’s a string, it will be used as the name + of the saved file. Example input: ‘myfilename’

+
+
+
+
+ +
+
+ save_table(suffix: Literal['.csv', '.tsv', '.parquet'] = '.csv', alt_filename: Optional[Union[str, Path]] = None)
+

Save the current filtered data table.

+
+
Parameters
+
+
    +
  • suffix ('.csv', '.tsv', or '.parquet' + (default='.csv')) – the file suffix

  • +
  • alt_filename (str, pathlib.Path, or None + (default)) – If None, file name will be generated automatically + according to the filtering methods used. If it’s a string, it will be used as the name of + the saved file. Example input: ‘myfilename’

  • +
+
+
+
+
+ +
+
+ sort(by: Union[str, List[str]], ascending: Union[bool, List[bool]] = True, na_position: str = 'last', inplace: bool = True)
+

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

+
+
Parameters
+
+
    +
  • by (str or list of str) – + Names of the column or columns to sort by.

  • +
  • ascending (bool or list + of bool, default True) – Sort ascending vs. descending. + Specify list for multiple sort orders. If this is a list of bools, it must have the same + length as ‘by’.

  • na_position ('first' or 'last', default 'last') – If ‘first’, puts NaNs at the beginning; if ‘last’, puts NaNs at the end.

  • inplace (bool, default True) – If True, perform operation in-place. Otherwise, returns a sorted copy of the Filter object without modifying the original.

@@ -5292,26 +5621,125 @@

rnalysis.filtering module -
-save_csv(alt_filename: Optional[Union[str, Path]] = None)
-

Saves the current filtered data to a .csv file.

-
-
Parameters
-

alt_filename (str, pathlib.Path, or None (default)) – If None, file name will be generated automatically according to the filtering methods used. If it’s a string, it will be used as the name of the saved file. Example input: ‘myfilename’

-
-
-

- -
-
-sort(by: Union[str, List[str]], ascending: Union[bool, List[bool]] = True, na_position: str = 'last', inplace: bool = True)
-

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

-
-
Parameters
-
Return type
@@ -7687,8 +8897,129 @@

rnalysis.enrichment module -
-user_defined_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', alpha: Fraction = 0.05, biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
+
+ user_defined_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', + alpha: Fraction = 0.05, + biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

Calculates enrichment and depletion of the FeatureSet for user-defined attributes against a background set. The attributes are drawn from an Attribute Reference Table. The background set is determined by either the input variable ‘background_genes’, or by the input variable ‘biotype’ and a Biotype Reference Table. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment score = 0) appears with the smallest value in the scale.

Parameters
@@ -7696,18 +9027,50 @@

rnalysis.enrichment moduleReturn type @@ -7939,8 +9302,386 @@

rnalysis.enrichment module -
-go_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', propagate_annotations: Literal['classic', 'elim', 'weight', 'all.m', 'no'] = 'elim', aspects: Union[Literal['any', 'biological_process', 'cellular_component', 'molecular_function'], Iterable[Literal['biological_process', 'cellular_component', 'molecular_function']]] = 'any', evidence_types: Union[Literal['any', 'experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = 'any', excluded_evidence_types: Union[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = (), databases: Union[str, Iterable[str], Literal['any']] = 'any', excluded_databases: Union[str, Iterable[str]] = (), qualifiers: Union[Literal['any', 'not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'any', excluded_qualifiers: Union[Literal['not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'not', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, plot_ontology_graph: bool = True, ontology_graph_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
+
+ go_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', propagate_annotations: Literal['classic', 'elim', 'weight', 'all.m', 'no'] = 'elim', aspects: Union[Literal['any', 'biological_process', 'cellular_component', 'molecular_function'], Iterable[Literal['biological_process', 'cellular_component', 'molecular_function']]] = 'any', evidence_types: Union[Literal['any', 'experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = 'any', excluded_evidence_types: Union[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = (), databases: Union[str, Iterable[str], Literal['any']] = 'any', excluded_databases: Union[str, Iterable[str]] = (), qualifiers: Union[Literal['any', 'not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'any', excluded_qualifiers: Union[Literal['not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'not', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', plot_ontology_graph: bool = True, ontology_graph_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

Calculates enrichment and depletion of the FeatureSet for Gene Ontology (GO) terms against a background set. The GO terms and annotations are drawn via the GO Solr search engine GOlr, using the search terms defined by the user. The background set is determined by either the input variable ‘background_genes’, or by the input variable ‘biotype’ and a Biotype Reference Table. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment score = 0) appears with the smallest value in the scale.

Parameters
@@ -7975,24 +9716,39 @@

rnalysis.enrichment module _images/ontology_graph.png
@@ -8067,8 +9823,264 @@

rnalysis.enrichment module -
-kegg_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, plot_pathway_graphs: bool = True, pathway_graphs_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
+
+ kegg_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', plot_pathway_graphs: bool = True, pathway_graphs_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

Calculates enrichment and depletion of the FeatureSet for Kyoto Encyclopedia of Genes and Genomes (KEGG) curated pathways against a background set. The background set is determined by either the input variable ‘background_genes’, or by the input variable ‘biotype’ and a Biotype Reference Table. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment score = 0) appears with the smallest value in the scale.

Parameters
@@ -8076,20 +10088,54 @@

rnalysis.enrichment modulehttps://www.uniprot.org/help/api_idmapping

  • alpha (float between 0 and 1 (default=0.05)) – Indicates the FDR threshold for significance.

  • -
  • statistical_test ('fisher', 'hypergeometric' or 'randomization' (default='fisher')) – determines the statistical test to be used for enrichment analysis. Note that some propagation methods support only some of the available statistical tests.

  • -
  • biotype (str specifying a specific biotype, list/set of strings each specifying a biotype, or 'all'. Default 'protein_coding'.) – determines the background genes by their biotype. Requires specifying a Biotype Reference Table. ‘all’ will include all genomic features in the reference table, ‘protein_coding’ will include only protein-coding genes from the reference table, etc. Cannot be specified together with ‘background_genes’.

  • -
  • background_genes (set of feature indices, filtering.Filter object, or enrichment.FeatureSet object) – a set of specific feature indices to be used as background genes. Cannot be specified together with ‘biotype’.

  • -
  • biotype_ref_path (str or pathlib.Path (default='predefined')) – the path of the Biotype Reference Table. Will be used to generate background set if ‘biotype’ is specified.

  • -
  • exclude_unannotated_genes (bool (deafult=True)) – if True, genes that have no annotation associated with them will be excluded from the enrichment analysis. This is the recommended practice for enrichment analysis, since keeping unannotated genes in the analysis increases the chance of discovering spurious enrichment results.

  • -
  • return_nonsignificant (bool (default=False)) – if True, the results DataFrame will include all tested pathways - both significant and non-significant ones. If False (default), only significant pathways will be returned.

  • -
  • save_csv (bool, default False) – If True, will save the results to a .csv file, under the name specified in ‘fname’.

  • -
  • fname (str or pathlib.Path) – The full path and name of the file to which to save the results. For example: ‘C:/dir/file’. No ‘.csv’ suffix is required. If None (default), fname will be requested in a manual prompt.

  • -
  • return_fig (bool (default=False)) – if True, returns a matplotlib Figure object in addition to the results DataFrame.

  • -
  • plot_horizontal (bool (default=True)) – if True, results will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot.

  • -
  • plot_pathway_graphs (bool (default=True)) – if True, will generate pathway graphs depicting the significant KEGG pathways.

  • -
  • pathway_graphs_format ('pdf', 'png', 'svg', or None (default=None)) – if pathway_graphs_format is not ‘none’, the pathway graphs will additonally be generated in the specified file format.

  • -
  • randomization_reps (int larger than 0 (default=10000)) – if using a randomization test, determine how many randomization repititions to run. Otherwise, this parameter will not affect the analysis.

  • -
  • parallel_backend (Literal[PARALLEL_BACKENDS] (default='loky')) – Determines the babckend used to run the analysis. if parallel_backend not ‘sequential’, will calculate the statistical tests using parallel processing. In most cases parallel processing will lead to shorter computation time, but does not affect the results of the analysis otherwise.

  • +
  • statistical_test ('fisher', 'hypergeometric' or 'randomization' + (default='fisher')) – determines the statistical test to be used for enrichment analysis. + Note that some propagation methods support only some of the available statistical tests.

  • +
  • biotype (str specifying a specific biotype, list/set of strings + each specifying a biotype, or 'all'. Default 'protein_coding'.) – determines the + background genes by their biotype. Requires specifying a Biotype Reference Table. ‘all’ will include all genomic + features in the reference table, ‘protein_coding’ will include only protein-coding genes from the reference + table, etc. Cannot be specified together with ‘background_genes’.

  • +
  • background_genes (set of feature indices, filtering.Filter + object, or enrichment.FeatureSet object) – a set of specific feature indices to be used + as background genes. Cannot be specified together with ‘biotype’.

  • +
  • biotype_ref_path (str or pathlib.Path (default='predefined')) + – the path of the Biotype Reference Table. Will be used to generate background set if ‘biotype’ is specified. +

  • +
  • exclude_unannotated_genes (bool (deafult=True)) – if + True, genes that have no annotation associated with them will be excluded from the enrichment analysis. This is + the recommended practice for enrichment analysis, since keeping unannotated genes in the analysis increases the + chance of discovering spurious enrichment results.

  • +
  • return_nonsignificant (bool (default=False)) – if True, + the results DataFrame will include all tested pathways - both significant and non-significant ones. If False + (default), only significant pathways will be returned.

  • +
  • save_csv (bool, default False) – If True, will save the results + to a .csv file, under the name specified in ‘fname’.

  • +
  • fname (str or pathlib.Path) – The full path and name of the file + to which to save the results. For example: ‘C:/dir/file’. No ‘.csv’ suffix is required. If None (default), fname + will be requested in a manual prompt.

  • +
  • return_fig (bool (default=False)) – if True, returns a + matplotlib Figure object in addition to the results DataFrame.

  • +
  • plot_horizontal (bool (default=True)) – if True, results + will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot.

  • +
  • show_expected (bool (default=False)) – if True, the + observed/expected values will be shown on the plot.

  • +
  • plot_style ('bar' or 'lollipop' + (default='bar')) – style for the plot. Either ‘bar’ for a bar plot or ‘lollipop’ for a + lollipop plot in which the lollipop size indicates the size of the observed gene set.

  • +
  • plot_pathway_graphs (bool (default=True)) – if True, + will generate pathway graphs depicting the significant KEGG pathways.

  • +
  • pathway_graphs_format ('pdf', 'png', 'svg', + or None (default=None)) – if pathway_graphs_format is not ‘none’, the + pathway graphs will additonally be generated in the specified file format.

  • +
  • randomization_reps (int larger than 0 (default=10000)) – + if using a randomization test, determine how many randomization repititions to run. Otherwise, this parameter + will not affect the analysis.

  • +
  • parallel_backend + (Literal[PARALLEL_BACKENDS] (default='loky')) – + Determines the babckend used to run the analysis. if parallel_backend not ‘sequential’, will calculate the + statistical tests using parallel processing. In most cases parallel processing will lead to shorter computation + time, but does not affect the results of the analysis otherwise.

  • Return type
    @@ -8193,8 +10239,76 @@

    rnalysis.enrichment module -
    -single_set_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], alpha: Fraction = 0.05, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False)
    +
    + single_set_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], alpha: Fraction = 0.05, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) +

    Calculates enrichment and depletion of the sorted RankedSet for user-defined attributes WITHOUT a background set, using the generalized Minimum Hypergeometric Test (XL-mHG, developed by Prof. Zohar Yakhini and colleagues and generalized by Florian Wagner). The attributes are drawn from an Attribute Reference Table. P-values are calculated using using the generalized Minimum Hypergeometric Test. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment = 0) appears with the smallest value in the scale.

    Parameters
    @@ -8233,8 +10347,337 @@

    rnalysis.enrichment module -
    -single_set_go_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, propagate_annotations: Literal['classic', 'elim', 'weight', 'all.m', 'no'] = 'elim', aspects: Union[Literal['any', 'biological_process', 'cellular_component', 'molecular_function'], Iterable[Literal['biological_process', 'cellular_component', 'molecular_function']]] = 'any', evidence_types: Union[Literal['any', 'experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = 'any', excluded_evidence_types: Union[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = (), databases: Union[str, Iterable[str], Literal['any']] = 'any', excluded_databases: Union[str, Iterable[str]] = (), qualifiers: Union[Literal['any', 'not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'any', excluded_qualifiers: Union[Literal['not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'not', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, plot_ontology_graph: bool = True, ontology_graph_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
    +
    + single_set_go_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, propagate_annotations: Literal['classic', 'elim', 'weight', 'all.m', 'no'] = 'elim', aspects: Union[Literal['any', 'biological_process', 'cellular_component', 'molecular_function'], Iterable[Literal['biological_process', 'cellular_component', 'molecular_function']]] = 'any', evidence_types: Union[Literal['any', 'experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = 'any', excluded_evidence_types: Union[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic'], Iterable[Literal['experimental', 'phylogenetic', 'computational', 'author', 'curator', 'electronic']]] = (), databases: Union[str, Iterable[str], Literal['any']] = 'any', excluded_databases: Union[str, Iterable[str]] = (), qualifiers: Union[Literal['any', 'not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'any', excluded_qualifiers: Union[Literal['not', 'contributes_to', 'colocalizes_with'], Iterable[Literal['not', 'contributes_to', 'colocalizes_with']]] = 'not', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', plot_ontology_graph: bool = True, ontology_graph_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

    Calculates enrichment and depletion of the sorted RankedSet for Gene Ontology (GO) terms WITHOUT a background set, using the generalized Minimum Hypergeometric Test (XL-mHG, developed by Prof. Zohar Yakhini and colleagues and generalized by Florian Wagner). The GO terms and annotations are drawn via the GO Solr search engine GOlr, using the search terms defined by the user. P-values are calculated using the generalized Minimum Hypergeometric Test. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment = 0) appears with the smallest value in the scale.

    Parameters
    @@ -8261,24 +10704,37 @@

    rnalysis.enrichment module _images/ontology_graph_singlelist.png
    @@ -8300,25 +10756,265 @@

    rnalysis.enrichment module -
    -single_set_kegg_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, plot_pathway_graphs: bool = True, pathway_graphs_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
    +
    + single_set_kegg_enrichment(organism: Union[str, int, Literal['auto'], Literal['Arabodopsis thaliana', 'Caenorhabditis elegans', 'Danio rerio', 'Drosophila melanogaster', 'Escherichia coli', 'Homo sapiens', 'Mus musculus', 'Saccharomyces cerevisiae', 'Schizosaccharomyces pombe']] = 'auto', gene_id_type: Union[str, Literal['auto'], Literal['UniProtKB AC/ID', 'UniParc', 'UniRef50', 'UniRef90', 'UniRef100', 'Gene Name', 'CRC64', 'Ensembl', 'Ensembl Genomes', 'Ensembl Genomes Protein', 'Ensembl Genomes Transcript', 'Ensembl Protein', 'Ensembl Transcript', 'GeneID', 'KEGG', 'PATRIC', 'UCSC', 'WBParaSite', 'WBParaSite Transcript/Protein', 'ArachnoServer', 'Araport', 'CGD', 'ConoServer', 'dictyBase', 'EchoBASE', 'euHCVdb', 'FlyBase', 'GeneCards', 'GeneReviews', 'HGNC', 'LegioList', 'Leproma', 'MaizeGDB', 'MGI', 'MIM', 'neXtProt', 'OpenTargets', 'Orphanet', 'PharmGKB', 'PomBase', 'PseudoCAP', 'RGD', 'SGD', 'TubercuList', 'VEuPathDB', 'VGNC', 'WormBase', 'WormBase Protein', 'WormBase Transcript', 'Xenbase', 'ZFIN', 'eggNOG', 'GeneTree', 'HOGENOM', 'OMA', 'OrthoDB', 'TreeFam', 'CCDS', 'EMBL/GenBank/DDBJ', 'EMBL/GenBank/DDBJ CDS', 'GI number', 'PIR', 'RefSeq Nucleotide', 'RefSeq Protein', 'ChiTaRS', 'GeneWiki', 'GenomeRNAi', 'PHI-base', 'CollecTF', 'BioCyc', 'PlantReactome', 'Reactome', 'UniPathway', 'CPTAC', 'ProteomicsDB']] = 'auto', alpha: Fraction = 0.05, exclude_unannotated_genes: bool = True, return_nonsignificant: bool = False, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', plot_pathway_graphs: bool = True, pathway_graphs_format: Literal['pdf', 'png', 'svg', 'none'] = 'none', parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

    Calculates enrichment and depletion of the sorted RankedSet for Kyoto Encyclopedia of Genes and Genomes (KEGG) curated pathways WITHOUT a background set, using the generalized Minimum Hypergeometric Test (XL-mHG, developed by Prof. Zohar Yakhini and colleagues and generalized by Florian Wagner). P-values are calculated using the generalized Minimum Hypergeometric Test. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment = 0) appears with the smallest value in the scale.

    Parameters
    -
      -
    • organism (str or int) – organism name or NCBI taxon ID for which the function will fetch GO annotations.

    • -
    • gene_id_type (str or 'auto' (default='auto')) – the identifier type of the genes/features in the FeatureSet object (for example: ‘UniProtKB’, ‘WormBase’, ‘RNACentral’, ‘Entrez Gene ID’). If the annotations fetched from the KEGG server do not match your gene_id_type, RNAlysis will attempt to map the annotations’ gene IDs to your identifier type. For a full list of legal ‘gene_id_type’ names, see the UniProt website: https://www.uniprot.org/help/api_idmapping

    • -
    • alpha (float between 0 and 1) – Indicates the FDR threshold for significance.

    • -
    • exclude_unannotated_genes (bool (deafult=True)) – if True, genes that have no annotation associated with them will be excluded from the enrichment analysis. This is the recommended practice for enrichment analysis, since keeping unannotated genes in the analysis increases the chance of discovering spurious enrichment results.

    • -
    • return_nonsignificant (bool (default=False)) – if True, the results DataFrame will include all tested GO terms - both significant and non-significant terms. If False (default), only significant KEGG pathways will be returned.

    • -
    • save_csv (bool, default False) – If True, will save the results to a .csv file, under the name specified in ‘fname’.

    • -
    • fname (str or pathlib.Path) – The full path and name of the file to which to save the results. For example: ‘C:/dir/file’. No ‘.csv’ suffix is required. If None (default), fname will be requested in a manual prompt.

    • -
    • return_fig (bool (default=False)) – if True, returns a matplotlib Figure object in addition to the results DataFrame.

    • -
    • plot_horizontal (bool (default=True)) – if True, results will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot.

    • -
    • plot_pathway_graphs (bool (default=True)) – if True, will generate pathway graphs depicting the significant KEGG pathways.

    • -
    • pathway_graphs_format ('pdf', 'png', 'svg', or None (default=None)) – if pathway_graphs_format is not ‘none’, the pathway graphs will additonally be generated in the specified file format.

    • -
    • parallel_backend (Literal[PARALLEL_BACKENDS] (default='loky')) – Determines the babckend used to run the analysis. if parallel_backend not ‘sequential’, will calculate the statistical tests using parallel processing. In most cases parallel processing will lead to shorter computation time, but does not affect the results of the analysis otherwise.

    • -
    +
    +
      +
    • organism (str or int) – organism name or NCBI taxon ID for + which the function will fetch GO annotations.

    • +
    • gene_id_type (str or 'auto' (default='auto')) + – the identifier type of the genes/features in the FeatureSet object (for example: ‘UniProtKB’, ‘WormBase’, + ‘RNACentral’, ‘Entrez Gene ID’). If the annotations fetched from the KEGG server do not match your + gene_id_type, RNAlysis will attempt to map the annotations’ gene IDs to your identifier type. For a full + list of legal ‘gene_id_type’ names, see the UniProt website: https://www.uniprot.org/help/api_idmapping +

    • +
    • alpha (float between 0 and 1) – Indicates the FDR threshold for significance. +

    • +
    • exclude_unannotated_genes (bool (deafult=True)) – if + True, genes that have no annotation associated with them will be excluded from the enrichment analysis. This + is the recommended practice for enrichment analysis, since keeping unannotated genes in the analysis + increases the chance of discovering spurious enrichment results.

    • +
    • return_nonsignificant (bool (default=False)) – if + True, the results DataFrame will include all tested GO terms - both significant and non-significant terms. + If False (default), only significant KEGG pathways will be returned.

    • +
    • save_csv (bool, default False) – If True, will save the + results to a .csv file, under the name specified in ‘fname’.

    • +
    • fname (str or pathlib.Path) – The full path and name of the + file to which to save the results. For example: ‘C:/dir/file’. No ‘.csv’ suffix is required. If None + (default), fname will be requested in a manual prompt.

    • +
    • return_fig (bool (default=False)) – if True, returns + a matplotlib Figure object in addition to the results DataFrame.

    • +
    • plot_horizontal (bool (default=True)) – if True, + results will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot. +

    • +
    • show_expected (bool (default=False)) – if True, the + observed/expected values will be shown on the plot.

    • +
    • plot_style ('bar' or 'lollipop' + (default='bar')) – style for the plot. Either ‘bar’ for a bar plot or ‘lollipop’ for + a lollipop plot in which the lollipop size indicates the size of the observed gene set.

    • +
    • plot_pathway_graphs (bool (default=True)) – if True, + will generate pathway graphs depicting the significant KEGG pathways.

    • +
    • pathway_graphs_format + ('pdf', 'png', 'svg', or None (default=None)) + – if pathway_graphs_format is not ‘none’, the pathway graphs will additonally be generated in the specified + file format.

    • +
    • parallel_backend + (Literal[PARALLEL_BACKENDS] (default='loky')) + – Determines the babckend used to run the analysis. if parallel_backend not ‘sequential’, will calculate the + statistical tests using parallel processing. In most cases parallel processing will lead to shorter + computation time, but does not affect the results of the analysis otherwise.

    • +
    Return type

    pd.DataFrame (default) or Tuple[pd.DataFrame, matplotlib.figure.Figure]

    @@ -8437,8 +11133,129 @@

    rnalysis.enrichment module -
    -user_defined_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', alpha: Fraction = 0.05, biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) Union[DataFrame, Tuple[DataFrame, Figure]]
    +
    + user_defined_enrichment(attributes: Union[List[str], str, List[int], int, Literal['all']], statistical_test: Literal['fisher', 'hypergeometric', 'randomization'] = 'fisher', + alpha: Fraction = 0.05, + biotype: Union[str, List[str], Literal['all']] = 'all', background_genes: Optional[Union[Set[str], Filter, FeatureSet]] = None, attr_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', biotype_ref_path: Union[str, Path, Literal['predefined']] = 'predefined', exclude_unannotated_genes: bool = True, return_nonsignificant: bool = True, save_csv: bool = False, fname=None, return_fig: bool = False, plot_horizontal: bool = True, show_expected: bool = False, plot_style: Literal['bar', 'lollipop'] = 'bar', randomization_reps: PositiveInt = 10000, random_seed: Optional[int] = None, parallel_backend: Literal['multiprocessing', 'loky', 'threading', 'sequential'] = 'loky', gui_mode: bool = False) + Union[DataFrame, Tuple[DataFrame, Figure]] +

    Calculates enrichment and depletion of the FeatureSet for user-defined attributes against a background set. The attributes are drawn from an Attribute Reference Table. The background set is determined by either the input variable ‘background_genes’, or by the input variable ‘biotype’ and a Biotype Reference Table. P-values are corrected for multiple comparisons using the Benjamini–Hochberg step-up procedure (original FDR method). In plots, for the clarity of display, complete depletion (linear enrichment score = 0) appears with the smallest value in the scale.

    Parameters
    @@ -8446,18 +11263,50 @@

    rnalysis.enrichment moduleReturn type @@ -8501,22 +11350,109 @@

    rnalysis.enrichment module -
    -rnalysis.enrichment.enrichment_bar_plot(results_table_path: Union[str, Path], alpha: Fraction = 0.05, enrichment_score_column: Union[str, Literal['log2_fold_enrichment', 'log2_enrichment_score']] = 'log2_fold_enrichment', n_bars: Union[PositiveInt, Literal['all']] = 'all', title: str = 'Enrichment results', center_bars: bool = True, plot_horizontal: bool = True, ylabel: Union[str, Literal['$\\log_2$(Fold Enrichment)', '$\\log_2$(Enrichment Score)']] = '$\\log_2$(Fold Enrichment)', ylim: Union[float, Literal['auto']] = 'auto') Figure
    +
    + rnalysis.enrichment.enrichment_bar_plot(results_table_path: Union[str, Path], alpha: Fraction = 0.05, enrichment_score_column: Union[str, Literal['log2_fold_enrichment', 'log2_enrichment_score']] = 'log2_fold_enrichment', n_bars: Union[PositiveInt, Literal['all']] = 'all', title: str = 'Enrichment results', center_bars: bool = True, plot_horizontal: bool = True, ylabel: Union[str, Literal['$\\log_2$(Fold Enrichment)', '$\\log_2$(Enrichment Score)']] = '$\\log_2$(Fold Enrichment)', + ylim: Union[float, Literal['auto']] = 'auto', plot_style: Literal['bar', 'lollipop'] = 'bar', show_expected: bool = False) + Figure

    Generate an enrichment bar-plot based on an enrichment results table. For the clarity of display, complete depletion (linear enrichment = 0) appears with the smallest value in the scale.

    Parameters
    -
      -
    • results_table_path (str or Path) – Path to the results table returned by enrichment functions.

    • -
    • alpha (float between 0 and 1 (default=0.05)) – the threshold for statistical significance. Used to draw significance asterisks on the graph.

    • -
    • enrichment_score_column (str (default='log2_fold_enrichment')) – name of the table column containing enrichment scores.

    • -
    • n_bars (int > 1 or 'all' (default='all')) – number of bars to display in the bar plot. If n_bars=’all’, all the results will be displayed on the graph. Otherwise, only the top n results will be displayed on the graph.

    • -
    • title (str) – plot title.

    • -
    • plot_horizontal (bool (default=True)) – if True, results will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot.

    • -
    • ylabel (str (default=r"$log_2$(Fold Enrichment)")) – plot y-axis label.

    • -
    • center_bars (bool (default=True)) – if True, center the bars around Y=0. Otherwise, ylim is determined by min/max values.

    • -
    • ylim (float or 'auto' (default='auto')) – set the Y-axis limits. If ylim`=’auto’, determines the axis limits automatically based on the data. If `ylim is a number, set the Y-axis limits to [-ylim, ylim].

    • -
    +
    +
      +
    • results_table_path (str or Path) – Path to the results table + returned by enrichment functions.

    • +
    • alpha (float between 0 and 1 (default=0.05)) – the + threshold for statistical significance. Used to draw significance asterisks on the graph.

    • +
    • enrichment_score_column (str + (default='log2_fold_enrichment')) – name of the table column containing enrichment + scores.

    • +
    • n_bars (int > 1 or 'all' + (default='all')) – number of bars to display in the bar plot. If n_bars=’all’, all + the results will be displayed on the graph. Otherwise, only the top n results will be displayed on the + graph.

    • +
    • title (str) – plot title.

    • +
    • plot_horizontal (bool (default=True)) – if True, + results will be plotted with a horizontal bar plot. Otherwise, results will be plotted with a vertical plot. +

    • +
    • ylabel (str (default=r"$log_2$(Fold + Enrichment)")) – plot y-axis label.

    • +
    • center_bars (bool (default=True)) – if True, center + the bars around Y=0. Otherwise, ylim is determined by min/max values.

    • +
    • ylim (float or 'auto' + (default='auto')) – set the Y-axis limits. If ylim`=’auto’, determines the axis + limits automatically based on the data. If `ylim is a number, set the Y-axis limits to [-ylim, ylim]. +

    • +
    • plot_style ('bar' or 'lollipop' + (default='bar')) – style for the plot. Either ‘bar’ for a bar plot or ‘lollipop’ for + a lollipop plot in which the lollipop size indicates the size of the observed gene set.

    • +
    • show_expected (bool (default=False)) – if True, the + observed/expected values will be shown on the plot.

    • +
    Returns

    Figure object containing the bar plot

    @@ -8791,7 +11727,7 @@

    rnalysis.utils moduleSphinx using a theme provided by Read the Docs. - + @@ -8802,7 +11738,7 @@

    rnalysis.utils module + - \ No newline at end of file + diff --git a/docs/build/contributing.html b/docs/build/contributing.html index 8641d87a..68ce184d 100644 --- a/docs/build/contributing.html +++ b/docs/build/contributing.html @@ -4,7 +4,7 @@ - How can I contribute to RNAlysis? — RNAlysis 3.8.0 documentation + How can I contribute to RNAlysis? — RNAlysis 3.9.0 documentation @@ -12,31 +12,31 @@ - - - - - - - + + + + + + + - - - - + + + + -
    +