insitupy.InSituExperiment

class insitupy.InSituExperiment

A class to manage and analyze multiple spatially resolved single-cell transcriptomics experiments.

This class provides functionality for managing datasets, performing differential gene expression analysis, querying metadata, visualizing data, and saving/loading experiments. It operates on multiple datasets, each represented as an InSituData object, and maintains associated metadata in a pandas.DataFrame.

Examples

>>> # Create an InSituExperiment object
>>> experiment = InSituExperiment()

>>> # Add a dataset
>>> experiment.add(data="path/to/dataset", mode="insitupy", metadata={"experiment": "test"})

>>> # Perform differential gene expression analysis
>>> experiment.dge(target_id=0, ref_id=1, target_annotation_tuple=("cell_type", "neuron"))

>>> # Save the experiment
>>> experiment.saveas("path/to/save", overwrite=True)

>>> # Query the experiment
>>> subset = experiment.query({"experiment": ["test"]})

>>> # Plot UMAPs
>>> experiment.plot_umaps(color="cell_type", title_column="experiment")

__init__()

Initialize an InSituExperiment object.

Methods

__init__()	Initialize an InSituExperiment object.
add(data[, mode, metadata])	Add a dataset to the experiment and update metadata.
add_metadata_column(column_name, values)
append_metadata(new_metadata, by[, overwrite])	Append metadata to the existing InSituExperiment object.
collect_anndatas(*args, **kwargs)
concat(objs[, new_col_name])	Concatenate multiple InSituExperiment objects.
copy()	Create a deep copy of the InSituExperiment object.
dge(target_id[, ref_id, ...])	Wrapper function for performing differential gene expression analysis within an InSituExperiment object.
from_config(config_path[, mode])	Create an InSituExperiment object from a configuration file.
from_regions(data, region_key[, region_names])	Creates an InSituExperiment object from specified regions in the given InSituData.
get_n_cells([cells_layer])	Get the total number of cells across all datasets.
import_from_anndata(adata, uid_column, ...)	Import observation and observation matrix data from an AnnData object into the experiment.
import_obs(*args, **kwargs)
iterdata()	Iterate over the metadata rows and corresponding data.
load_all([skip])	Load all data modalities for all datasets.
load_annotations()
load_cells()
load_images([names, nuclei_type, ...])
load_regions()
load_transcripts([transcript_filename])
plot_embedding(basis[, cells_layer, color, ...])	Create a plot with embeddings of all datasets as subplots using scanpy's sc.pl.embedding function.
plot_overview(*args, **kwargs)
plot_umaps([cells_layer, color, ...])	Create a plot with UMAPs of all datasets as subplots using scanpy's pl.umap function.
query(criteria)	Query the experiment based on metadata criteria.
read(path)	Read an InSituExperiment object from a specified folder.
remove_history()
remove_metadata_columns(columns)	Remove specified columns from the internal metadata.
save([verbose, overwrite_metadata, ...])
saveas(path[, overwrite, verbose])	Save all datasets to a specified folder.
show(index[, verbose])	Displays the dataset at the specified index.
show_modality(modality[, uid_column])
sync_colors(keys[, cells_layer, palette, ...])	Synchronize color dictionaries for categorical metadata across datasets.
to_anndata([cells_layer, label_col, ...])	Concatenate all datasets into a single AnnData object.

Attributes

annotations	Displays a summary of the 'annotations' modality for all datasets.
cells	Displays a summary of cells for all datasets.
colors	Color dictionaries created by sync_colors().
data	List of datasets as InSituData objects.
images	Displays a summary of the 'images' modality for all datasets.
metadata	Returns a copy of the experiment-level metadata.
path	Save path of the InSituExperiment object.
regions	Displays a summary of the 'regions' modality for all datasets.
transcripts	Displays a summary of the 'transcripts' modality for all datasets.