__version__ = '1.5.0'
from typing import List
from typing import Optional
from .EnrichmentAnalysis import Enrichmentscope
from .General.Omicscope import Omicscope
from .General.Snapshot import Omicscope_Snapshot
from .MultipleData.Nebula import nebula
if __name__ != "__main__":
print("OmicScope v " + __version__ + " For help: https://omicscope.readthedocs.io/en/latest/ or https://omicscope.ib.unicamp.br " +
"If you use in published research, please cite:\n" +
"'Reis-de-Oliveira, G., et al (2024). OmicScope unravels systems-level insights from quantitative proteomics data'.")
[docs]def OmicScope(Table: str,
Method: str,
ControlGroup: Optional[str] = None,
ExperimentalDesign: str = 'static',
pvalue: str = 'pAdjusted',
PValue_cutoff: float = 0.05,
normalization_method: Optional[str] = None,
imputation_method: Optional[str] = None,
FoldChange_cutoff: float = 0.0,
logTransform: bool = True,
ExcludeContaminants: bool = True,
degrees_of_freedom: int = 2,
independent_ttest=True,
** kwargs) -> Omicscope:
"""OmicScope - Differential Proteomics
OmicScope was designed to be compatible with several Proteomics software,
such as Progenesis Qi for Proteomics, PatternLab V, MaxQuant,
DIA-NN, ProteomeDiscoverer, and FragPipe.
Additionally, users can also input data from other Omics sources (e.g.Transcriptomics),
using `General` and `Snapshot` methods. In General, users can analyse data in a pre-specified format using excel workbooks.
On the other hand, Snapshot enables users to import pre-analyzed data into OmicScope quickly.
OmicScope are able to perform differential proteomics analysis, returning p-value, adjusted p-value
(Benjamini-Hochberg approach), and fold-changes.
Args:
Table (str): Quantitative data.
Method (str): Method used to import data.
ControlGroup (Optional[str], optional): Control group. Defaults to None.
ExperimentalDesign (str, optional): Experimental design to perform statistical analysis.
Options: 'static', 'longitudinal'. Defaults to 'static'.
pvalue (str, optional): Statistical parameter to consider entities differentially regulated.
Options: 'pvalue', 'pAdjusted', 'pTukey'. Defaults to 'pAdjusted'.
PValue_cutoff (float, optional): Statistical cutoff. Defaults to 0.05.
normalization_method (str, optional): Data normalization can be performed. Options:
Options: 'average', 'median', 'quantile'. Defaults to None.
imputation_method (str, optional): Impute values to data instead of NaN.
Options: "median", "mean", "knn". Defaults to None.
FoldChange_cutoff (float, optional): Absolute fold-change cutoff. Defaults to 0.0.
logTransform (bool, optional): Log-transform protein abundances. Defaults to False.
ExcludeContaminants (bool, optional): Exclude the list of Contaminant proteins. Defaults to True.
degrees_of_freedom (int, optional): Degrees of freedom to run longitudinal analysis. Defaults to 2.
independent_ttest (bool, optional): while running a t-test, the user can specify if data sampling
is independent (default) or paired (independent_ttest=False). Defaults to True.
Returns:
OmicScope: Return a OmicScope obj. The quantitation data is stored to obj.quant_data.
"""
if Method == 'Snapshot':
omics = Omicscope_Snapshot(
Table,
pvalue,
PValue_cutoff,
FoldChange_cutoff)
elif Method== 'OmicScope':
import pickle
from copy import copy
with open(Table, 'rb') as f:
omics = pickle.load(f)
try:
if omics.Analysis == 'ORA' or omics.Analysis == 'GSEA':
print("You've imported the EnrichmentScope outcomes.")
except:
print("You've imported OmicScope module outcomes.")
else:
omics = Omicscope(
Table,
Method,
ControlGroup,
ExperimentalDesign,
pvalue,
PValue_cutoff,
normalization_method,
imputation_method,
FoldChange_cutoff,
logTransform,
ExcludeContaminants,
degrees_of_freedom,
independent_ttest,
**kwargs)
return omics
[docs]def EnrichmentScope(OmicScope: Omicscope,
Analysis: str = 'ORA',
dbs: List[str] = ['KEGG_2021_Human'],
padjust_cutoff: float = 0.05,
organism: str = 'human',
background=None) -> Enrichmentscope:
"""EnrichmentScope - Enrichment Analysis
EnrichmentScope is the module designed to perform over-representation and Gene-Set Enrichment Analyses of proteins and genes.
In EnrichmentScope, several figures enable user to see enriched terms with their respective proteins.
Args:
OmicScope (Omicscope): Omicscope object
Analysis (str): Over-representation Analysis (ORA) or Gene-Set Enrichment Analysis (GSEA). Defaults to 'ORA'.
dbs (List[str]): List of enrichment databases to perform the enrichment analysis. Defaults to ['KEGG_2021_Human'].
padjust_cutoff (float, optional): P-Adjusted cutoff . Defaults to 0.05.
organism (str, optional): Organism to perform enrichment analysis. Defaults to 'human'.
background (int, list, str, bool): Background genes. By default, all genes listed in the `gene_sets` input will be used
as background. Alternatively, user can use all genes evaluated in study (Recommended, background = True). Still,
user can define a specific number (integer) to use as background (Not recommended), such as number of reviewed proteins
in the target organism on Uniprot.
Returns:
Enrichmentscope: Return a EnrichmentScope obj. The results is stored to obj.results.
"""
enrichment = Enrichmentscope(
OmicScope,
Analysis,
dbs,
padjust_cutoff,
organism,
background)
return enrichment
[docs]def Nebula(folder: str,
palette: str = 'Dark2',
pvalue_cutoff: float = 0.05) -> nebula:
"""Nebula - Multiple group comparison
Nebula is the module to integrate all data generated by OmicScope and EnrichmentScope pipelines.
Args:
folder (str): path to folder that contains all .omics files
palette (str): Palette to assign colors and discriminate groups
pvalue_cutoff (float): P-value threshold to consider differentially regulated proteins
Returns:
Nebula: Return a Nebula obj.
"""
multiple_groups = nebula(folder, palette=palette, pvalue_cutoff=pvalue_cutoff)
return multiple_groups
