class pypage.GeneSets(genes: Optional[np.ndarray] = None,
pathways: Optional[np.ndarray] = None,
ann_file: Optional[str] = None,
n_bins: Optional[int] = 3,
first_col_is_genes: Optional[bool] = False)Objects of this class store information about gene-sets and should be passed to PAGE object as an input. To initialize GeneSets user should provide either tab delimited annotation file in index format (each line starts with a gene-set name, followed by genes) or a binary matrix encoding gene membership.
ann_file: str
tab delimited annotation file in index format
first_col_is_genes:
specifies whether first element in each line of the annotation file is a gene
genes: np.ndarray
gene names in pathways matrix, alternative to ann_file
pathways: np.ndarray
binary matrix encoding gene-set membership, alternative to ann_file
n_bins: int
number of bins to use when binning membership array
GeneSets.convert_from_to(input_format: str,
output_format: str,
species: Optional[str] = 'human')This function is used to convert gene names in the annotation to another format.
Available formats: ensg (ensemble gene ids), enst (ensemble transcript ids), refseq, entrez (gene ids), gs (gene symbol).
input_format: str
input format of the annotation
output_format: str
output format of the annotation
species: str
species, available: human, mouse
class pypage.ExpressionProfile(genes: np.ndarray,
expression: np.ndarray,
is_bin: bool = False,
n_bins: Optional[int] = 10)Objects of this class store information about gene expression and should be passed to PAGE object as an input.
genes: np.ndarray
The array with gene names.
expression: np.ndarray
The array representing either the continuous expression value
of genes, or the bin/cluster that gene belongs to.
is_bin: bool
Specifies that the provided array is already prebinned.
n_bins: int
number of bins to bin the expression array into.
ExpressionProfile.convert_from_to(input_format: str,
output_format: str,
species: Optional[str] = 'human')This function is used to convert gene names in the expression profile to another format.
Available formats: ensg (ensemble gene ids), enst (ensemble transcript ids), refseq, entrez (gene ids), gs (gene symbol).
input_format: str
input format of the annotation
output_format: str
output format of the annotation
species: str
species, available: human, mouse
class pypage.PAGE(
expression: ExpressionProfile,
genesets: GeneSets,
n_shuffle: int = 1e3,
alpha: float = 1e-2,
k: int = 10,
filter_redundant: bool = False,
n_jobs: Optional[int] = 1,
function: Optional[str] = 'cmi',
redundancy_ratio: Optional[float] = .1)The main object of the package that performs the computation of differentially active genes and stores the results.
expression: ExpressionProfile
ExpressionProfile object containing differential gene expression.
genesets: GeneSets
GeneSets object containing gene annotations.
n_shuffle: int
The number of permutations in the statistical test.
alpha: float
The maximum p-value threshold to consider a pathway informative
with respect to the permuted mutual information distribution
k: int
The number of contiguous uninformative pathways to consider before
stopping the informative pathway search
filter_redundant: bool
Specify whether to perform the pathway redundancy search
n_jobs: int
The number of parallel jobs to use in the analysis
(`default = all available cores`)
function: str
Specify whether conditional mutual information ('cmi') or mutual information ('mi') should be calculated.
redundancy_ratio: float
The redundacy ratio to use (the bigger the threshold the lesser number of gene-sets will be in the output). To understand it refer to the paper.
PAGE.run()The function to run computation of differentially active genes. As a result it produces a pandas dataframe and a Heatmap object which can also be accessed as PAGE.results and PAGE.hm attributes.
PAGE.get_enriched_genes(pathway: str)The function that returns the information about which gene-set genes are present in which expression bin.
name: str
The name of the gene-set.
Heatmap objects are used to produce graphical representations of pyPAGE results.
Objects of this class are automatically generated by PAGE, so we will not concentrate on its input parameters here.
Heatmap.show(max_rows: Optional[int] = 50,
show_reg: Optional[bool] = False,
max_val: Optional[int] = 5,
title: str = '')Show the heatmap representation pyPAGE results.
max_rows: int
Maximal number of rows in the ouput
show_reg: bool
Specifies whether expression of a regulator should be used (works only if gene-sets are named by their regulators).
max_val: int
Max value for a colorbar.
title: str
Title of the heatmap
Heatmap.save(output_name:str,
max_rows: Optional[int] = 50,
show_reg: Optional[bool] = False,
max_val: Optional[int] = 5,
title: str = '')Save the heatmap representation pyPAGE results.
output_name: str
The name of the output file.
max_rows: int
Maximal number of rows in the ouput
show_reg: bool
Specifies whether expression of a regulator should be used (works only if gene-sets are named by their regulators).
max_val: int
Max value for a colorbar.
title: str
Title of the heatmap
Heatmap.convert_from_to(input_format: str,
output_format: str,
species: Optional[str] = 'human')This function is used to convert regulator gene names to another format (the one that is used in the differential expression profile).
Available formats: ensg (ensemble gene ids), enst (ensemble transcript ids), refseq, entrez (gene ids), gs (gene symbol).
input_format: str
input format of the annotation
output_format: str
output format of the annotation
species: str
species, available: human, mouse
Heatmap.add_gene_expression(genes: np.ndarray,
expression: np.ndarray)This function should be used if you want to visualize the expression of the regulators in cases when it is not contained in PAGE object. For example, when PAGE is run to identify RBP regulons using differential stability and you want to add RBP expression to the heatmap output.
genes: np.ndarray
Array of gene names
expression: np.ndarray
Array of differential expression values.