scgen.SCGEN.reg_mean_plot

SCGEN.reg_mean_plot(adata, axis_keys, labels, path_to_save='./reg_mean.pdf', save=True, gene_list=None, show=False, top_100_genes=None, verbose=False, legend=True, title=None, x_coeff=0.3, y_coeff=0.8, fontsize=14, **kwargs)

Plots mean matching figure for a set of specific genes.

Parameters

adata : ~anndata.AnnData

AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model. Must have been setup with batch_key and labels_key, corresponding to batch and cell type metadata, respectively.

axis_keys : dict

Dictionary of adata.obs keys that are used by the axes of the plot. Has to be in the following form:

{“x”: “Key for x-axis”, “y”: “Key for y-axis”}.

labels : dict

Dictionary of axes labels of the form {“x”: “x-axis-name”, “y”: “y-axis name”}.

path_to_save : basestring

path to save the plot.

save : boolean

Specify if the plot should be saved or not.

gene_list : list

list of gene names to be plotted.

show : bool

if True: will show to the plot after saving it.

Examples

>>> import anndata
>>> import scgen
>>> import scanpy as sc
>>> train = sc.read("./tests/data/train.h5ad", backup_url="https://goo.gl/33HtVh")
>>> scgen.SCGEN.setup_anndata(train)
>>> network = scgen.SCGEN(train)
>>> network.train()
>>> unperturbed_data = train[((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "control"))]
>>> pred, delta = network.predict(
>>>     adata=train,
>>>     adata_to_predict=unperturbed_data,
>>>     ctrl_key="control",
>>>     stim_key="stimulated"
>>>)
>>> pred_adata = anndata.AnnData(
>>>     pred,
>>>     obs={"condition": ["pred"] * len(pred)},
>>>     var={"var_names": train.var_names},
>>>)
>>> CD4T = train[train.obs["cell_type"] == "CD4T"]
>>> all_adata = CD4T.concatenate(pred_adata)
>>> network.reg_mean_plot(
>>>     all_adata,
>>>     axis_keys={"x": "control", "y": "pred", "y1": "stimulated"},
>>>     gene_list=["ISG15", "CD3D"],
>>>     path_to_save="tests/reg_mean.pdf",
>>>     show=False
>>> )