GSForge.plots.results package¶

Module contents¶

class GSForge.plots.results.MeanVsLFC(*args, **params)¶

Bases: GSForge.plots.abstract_plot_models.ResultPlottingOperation

Mean vs log-fold-change scatter plot used for visualizing differential gene expression results.

Parameters

source (Union[GSForge.GeneSet, xarray.Dataset, pandas.DataFrame]) – Data source containing the log-fold change, and p-value variables.
log_fold_change_var (str) – The name of the log-fold change column. Must be a variable within source.
p_value_var (str) – The name of the p-value column. Must be a variable within source.
mean_value_var (str) – The name of the base mean column. Must be a variable within source.
log_fold_change_cutoff (float) – Cutoff to use in grouping and coloring genes. Defaults to 2.0.
p_value_cutoff (float) – Cutoff to use in grouping and coloring genes. Defaults to 1e-6.
label_selected_genes (bool) – Apply (if True) annotations of genes that pass both the log-fold-change and p-value cutoff values.
apply_default_opts (bool) – Whether to apply the default styling.

Returns

A holoviews scatter plot of log-fold-change versus mean values.

Return type

holoviews.Overlay

Parameters inherited from:

GSForge.plots.abstract_plot_models.AbstractPlottingOperation: apply_default_opts, plot_options

GSForge.plots.abstract_plot_models.ResultPlottingOperation: source

log_fold_change_var = param.String(readonly=False): The name of the log-fold change column. Must be a variable within source.
p_value_var = param.String(readonly=False): The name of the p-value column. Must be a variable within source.
log_fold_change_cutoff = param.Number(readonly=False): Cutoff to use in grouping and coloring genes. Defaults to 2.0.
p_value_cutoff = param.Number(readonly=False): Cutoff to use in grouping and coloring genes. Defaults to 1e-6.
mean_value_var = param.String(readonly=False): The name of the base mean column. Must be a variable within source.
label_selected_genes = param.Boolean(readonly=False): Apply (if True) annotations of genes that pass both the log-fold-change and p-value cutoff values.

log_fold_change_var = None¶

p_value_var = None¶

log_fold_change_cutoff = 2.0¶

p_value_cutoff = 1e-06¶

mean_value_var = None¶

label_selected_genes = False¶

static bokeh_opts()¶

static matplotlib_opts()¶

static mean_vs_lfc(source: xarray.core.dataset.Dataset, log_fold_change_var: str, p_value_var: str, mean_value_var: str, log_fold_change_cutoff: float = 2.0, p_value_cutoff: float = 1e-06, label_selected_genes: bool = False, gene_dim='Gene') → holoviews.core.overlay.NdOverlay¶

name = 'MeanVsLFC'¶

class GSForge.plots.results.Volcano(*args, **params)¶