pilotpy.tl.compute_diff_expressions

pilotpy.tl.compute_diff_expressions(adata, cell_type: str = None, proportions: DataFrame = None, selected_genes: list = None, font_size: int = 18, group1: str = 'Tumor 1', group2: str = 'Tumor 2', label_name: str = 'Predicted_Labels', fc_thr: float = 0.5, pval_thr: float = 0.01, sample_col: str = 'sampleID', col_cell: str = 'cell_types', path=None, normalization=False, n_top_genes=2000, highly_variable_genes_=True, number_n=5, number_p=5, marker='o', color='w', markersize=8, font_weight_legend='normal', size_legend=12, figsize=(15, 15), dpi=100)

Using limma R package, lmFit fits a linear model using weighted least squares for each gene. Comparisons between groups (log fold-changes) are obtained as contrasts of these fitted linear models. Empirical Bayes smoothing of standard errors (shrinks standard errors that are much larger or smaller than those from other genes towards the average standard error).

Parameters

adataAnnData

Annotated data matrix.

cell_typestr, optional

Specify cell type name to check its differential expression genes. The default is None.

proportionspd.DataFrame, optional

Cell types proportions in each sample. The default is None.

selected_geneslist, optional

Specify gene names to be considered for checking their differentiation.

font_sizeint, optional

Font size for plot labels and legends. The default is 18.

group1str, optional

Name of the first patient sub-group for comparison. The default is ‘Tumor 1’.

group2str, optional

Name of the second patient sub-group for comparison. The default is ‘Tumor 2’.

label_namestr, optional

Name of the column containing the labels of patient sub-groups. The default is ‘Predicted_Labels’.

fc_thrfloat, optional

Specify the fold change threshold. The default is 0.5.

pval_thrfloat, optional

Specify the adjusted p-value threshold. The default is 0.01.

sample_colstr, optional

Name of the column containing sample IDs. The default is ‘sampleID’.

col_cellstr, optional

Name of the column containing cell type annotations. The default is ‘cell_types’.

pathstr, optional

Path to save the results. The default is None.

normalizationbool, optional

Perform gene expression normalization. The default is False.

n_top_genesint, optional

Number of top variable genes to consider. The default is 2000.

highly_variable_genes_bool, optional

Determine highly variable genes. The default is True.

number_nint, optional

The number of labels that the user wants to show over the plot for negative thresholds. The default is 5.

number_pint, optional

The number of labels that the user wants to show over the plot for positive thresholds. The default is 5.

markerstr, optional

Marker style for the labels in the volcano plot. The default is ‘o’.

colorstr, optional

Marker color for the labels in the volcano plot. The default is ‘w’.

markersizeint, optional

Marker size for the labels in the volcano plot. The default is 8.

font_weight_legendstr, optional

Font weight for legend labels. The default is ‘normal’.

size_legendint, optional

Font size for legend labels. The default is 12.

figsize: tuple, optional

Figure size. The default is (15,15).

dpiint, optional

Dots per inch for the saved plot image. Default is 100.

Returns

None

Generates and displays a volcano plot of fold changes between two interested patient sub-groups. Saves a statistical table of each gene. Saves significantly differentiated genes in each group.