tirank.LoadData

tirank.LoadData.load_bulk_exp(path_to_bulk_exp)[source]

Loads a bulk expression file (CSV or TXT) into a DataFrame.

Parameters:: path_to_bulk_exp (str) – The file path to the bulk expression data. Assumes genes are rows and samples are columns.
Returns:: A pandas DataFrame of the expression data.
Return type:: pd.DataFrame

tirank.LoadData.load_bulk_clinical(path_to_bulk_cli)[source]

Loads a bulk clinical data file (CSV, TXT, or Excel) into a DataFrame.

Parameters:: path_to_bulk_cli (str) – The file path to the bulk clinical data. Assumes samples are rows and clinical variables are columns.
Returns:: A pandas DataFrame of the clinical data.
Return type:: pd.DataFrame

tirank.LoadData.check_bulk(savePath, bulkExp, bulkClinical)[source]

Checks and filters bulk data for common samples.

Intersects the samples in the bulk expression columns and the bulk clinical index. Filters both DataFrames to keep only the common samples and saves the results as pickle files.

Parameters:

savePath (str) – The main project directory path to save results.
bulkExp (pd.DataFrame) – Bulk expression DataFrame (genes x samples).
bulkClinical (pd.DataFrame) – Bulk clinical DataFrame (samples x variables).

Returns:

None

tirank.LoadData.load_sc_data(path_to_sc_h5ead, savePath)[source]

Loads single-cell RNA-seq data from an .h5ad file.

Parameters:

path_to_sc_h5ead (str) – The file path to the .h5ad file.
savePath (str) – The main project directory path to save the loaded AnnData object as ‘anndata.pkl’.

Returns:

The loaded AnnData object.

Return type:

sc.AnnData

tirank.LoadData.load_st_data(path_to_st_floder, savePath)[source]

Loads spatial transcriptomics (ST) data from a 10x Visium folder.

Uses scanpy.read_visium() to load the data.

Parameters:

path_to_st_floder (str) – The file path to the directory containing the 10x Visium output (e.g., ‘spatial’, ‘filtered_feature_bc_matrix.h5’).
savePath (str) – The main project directory path to save the loaded AnnData object as ‘anndata.pkl’.

Returns:

The loaded AnnData object.

Return type:

sc.AnnData

tirank.LoadData.view_dataframe(df, nrow=10, ncol=8)[source]

Prints a top-left subset of a DataFrame for quick viewing.

Parameters:

df (pd.DataFrame) – The DataFrame to view.
nrow (int, optional) – The number of rows to show. Defaults to 10.
ncol (int, optional) – The number of columns to show. Defaults to 8.

Returns:

None

tirank.LoadData.transfer_exp_profile(scAnndata)[source]

Converts the .X matrix of an AnnData object to a pandas DataFrame.

Handles both sparse and dense .X matrices. The resulting DataFrame is structured as genes (rows) x cells/spots (columns).

Parameters:: scAnndata (sc.AnnData) – The AnnData object.
Returns:: The expression matrix as a pandas DataFrame.
Return type:: pd.DataFrame

Functions

`tirank.LoadData.check_bulk`	Checks and filters bulk data for common samples.
`tirank.LoadData.load_bulk_clinical`	Loads a bulk clinical data file (CSV, TXT, or Excel) into a DataFrame.
`tirank.LoadData.load_bulk_exp`	Loads a bulk expression file (CSV or TXT) into a DataFrame.
`tirank.LoadData.load_sc_data`	Loads single-cell RNA-seq data from an .h5ad file.
`tirank.LoadData.load_st_data`	Loads spatial transcriptomics (ST) data from a 10x Visium folder.
`tirank.LoadData.transfer_exp_profile`	Converts the .X matrix of an AnnData object to a pandas DataFrame.
`tirank.LoadData.view_dataframe`	Prints a top-left subset of a DataFrame for quick viewing.