indicates whether it is single-end or paired-end Pipeline
list of functions to perform
list of function parameters
Bases: GenericPipeline, ABC
GenericPipeline
ABC
Internal method, used to plot the results from CountFilter.pca().
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.
Saves the current filtered data to a .csv file.
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’
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.
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.
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’
Saves the current filtered data to a .parquet file.
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 the current filtered data table.
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’
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.
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.
xlabel (str or 'auto') – optional. If not specified, sample1 will be used as xlabel.
ylabel (str or 'auto') – optional. If not specified, sample2 will be used as ylabel.
title (str or 'auto') – optional. If not specified, a title will be generated automatically.
Sort the rows by the values of specified column or columns.
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’.
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.
Add a function to the pipeline. Arguments can be stated with or without the correspoding keywords. For example: Pipeline.add_function(‘sort’, ‘columnName’, ascending=False, na_position=’first’). Pipelines support virtually all functions in the filtering module, including filtering functions, normalization functions, splitting functions and plotting functions. Do not include the ‘inplace’ argument when adding functions to a pipeline; instead, include it when applying the pipeline to a Filter object using Pipeline.apply_to(). +
Add a function to the pipeline. Arguments can be stated with or without the correspoding keywords. For example: Pipeline.add_function(‘sort’, ‘columnName’, ascending=False, na_position=’first’). Pipelines support virtually all functions in the filtering module, including filtering functions, normalization functions, splitting functions and plotting functions. Do not include the ‘inplace’ argument when adding functions to a pipeline; instead, include it when applying the pipeline to a Filter object using Pipeline.apply_to(). :param func: function to be added :type func: function or name of function from the filtering module :param args: unkeyworded arguments for the added function in their natural order. For example: 0.1, True @@ -7349,8 +7876,386 @@
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.
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.
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.
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.
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.
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.
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.
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.
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.
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_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.
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.
pd.DataFrame (default) or Tuple[pd.DataFrame, matplotlib.figure.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.
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.
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.
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]. +
Figure object containing the bar plot