Omicscope pipeline

omicscope.EnrichmentScope(OmicScope: Omicscope, Analysis: str = 'ORA', dbs: List[str] = ['KEGG_2021_Human'], padjust_cutoff: float = 0.05, organism: str = 'human', background=None) → Enrichmentscope[source]

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.

Parameters:

  • 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.

omicscope.Nebula(folder: str, palette: str = 'Dark2', pvalue_cutoff: float = 0.05) → nebula[source]

Nebula - Multiple group comparison

Nebula is the module to integrate all data generated by OmicScope and EnrichmentScope pipelines.

Parameters:

  • 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.

omicscope.OmicScope(Table: str, Method: str, ControlGroup: str | None = None, ExperimentalDesign: str = 'static', pvalue: str = 'pAdjusted', PValue_cutoff: float = 0.05, normalization_method: str | None = None, imputation_method: str | None = None, FoldChange_cutoff: float = 0.0, logTransform: bool = True, ExcludeContaminants: bool = True, degrees_of_freedom: int = 2, independent_ttest=True, **kwargs) → Omicscope[source]

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.

Parameters:

  • 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.