swan_vis.utils

Functions

calc_pi(adata, t_df, obs_col='dataset') : Calculate the percent isoform per gene per condition given by obs_col. Default column to use is adata.obs index column, dataset.

Parameters:
        adata (anndata AnnData): Annotated data object from the SwanGraph
        t_df (pandas DataFrame): Pandas Dataframe that has index to
                gene id mapping
        obs_col (str): Column name from adata.obs table to group on.
                Default: 'dataset'

Returns:
        df (pandas DataFrame): Pandas DataFrame where rows are the different
                conditions from `obs_col` and the columns are transcript ids in the
                SwanGraph, and values represent the percent isoform usage per gene
                per condition.
        sums (pandas DataFrame): Pandas DataFrame where rows are the different
                conditions from `obs_col` and the columns are transcript ids in the
                SwanGraph, and values represent the cumulative counts per isoform
                per condition.

calc_total_counts(adata, obs_col='dataset', layer='counts') : Calculate cumulative expression per adata entry based on condition given by obs_col. Default column to use is adata.obs index column, dataset.

Parameters:
        adata (anndata AnnData): Annotated data object from the SwanGraph
        obs_col (str): Column name from adata.obs table to group on.
                Default: 'dataset'
        layer (str): Layer of AnnData to pull from. Default = 'counts'

Returns:
        df (pandas DataFrame): Pandas DataFrame where rows are the different
                conditions from `obs_col` and the columns are transcript ids in the
                SwanGraph, and values represent the cumulative counts per isoform
                per condition.

calc_tpm(adata, obs_col='dataset', how='mean', recalc=False) : Calculate the TPM per condition given by obs_col. Default column to use is adata.obs index column, dataset.

Parameters:
        adata (anndata AnnData): Annotated data object from the SwanGraph
        obs_col (str or list of str): Column name from adata.obs table to group on.
                Default: 'dataset'
        how (str): How to compute tpm across multiple datasets {'mean', 'max'}
        recalc (bool): Whether tpm data should be recalculated or tpm
                values should just be averaged
                Default: False

Returns:
        df (pandas DataFrame): Pandas datafrom where rows are the different
                conditions from `obs_col` and the columns are transcript ids in the
                SwanGraph, and values represent the TPM value per isoform per
                condition.

make_uns_key(kind, obs_col, obs_conditions, die_kind='iso') : Make a key name to reference die, det, or deg results in the .uns part of SwanGraph.adata.

Parameters:
        kind (str): Choose 'det', 'die', 'deg' (for differential transcript,
                isoform switching / differential isoform expression,
                differential gene expression respectively)
        obs_col (str): Column name from self.adata.obs table to group on.
        obs_conditions (list of str, len 2): Which conditions from obs_col
                to compare? Required if obs_col has more than 2 unique values.
        die_kind (str): Which DIE test results. {'iso', 'tss', 'tes', 'ic'}
                Default: 'tss'

Returns:
        uns_name (str): Name of .uns key

read(file) : Read a SwanGraph from a saved pickle file.

Parameters:
        file (str): Name / path to saved Swan file

Returns:
        sg (SwanGraph): SwanGraph stored in file

save_fig(oname) : Save the current figure as a png with a given file prefix.

Parameters:
        oname (str): Path / prefix to saved image

validate_gtf(fname) : Validates that the input GTF is valid input to Swan.

Parameters:
        fname (str): Path / name of GTF file