`larry._analysis`¶

Submodules¶

Package Contents¶

Classes¶

LARRY_t0_test_indices

Helper class that provides a standard way to create an ABC using

Functions¶

`count_clonal_lineages`(→ pandas.DataFrame)	Count clonal lineages over groups (e.g., time points).
`count_d2_daughter_cells`(adata[, bins, plot_hist, ...])	plot histogram and umap of n_daughters
`count_clones_by_timepoint`(→ pandas.DataFrame)	Annotates adata with pandas.DataFrame: adata.uns['clone_x_timepoint'].
`estimate_growth_rates`(→ dict)	Estimate growth rate from counts of daughter cells relative to d2 progenitors, across multiple timepoints.
`plot_growth_rates`(→ None)	Parameters:
`get_lineage_obs`(→ pandas.DataFrame)	Parameters:
`calculate_dominate_fate`(→ pandas.DataFrame)	Parameters:
`get_annotated_metadata_d2_lineage_cells`(→ pandas.DataFrame)	Get the annotated metadata table for d2 lineage cells
`growth_rate_grouped_violin_plot`(adata[, ncols])
`temporal_cmap`([t, idx_slice, pad_left, pad_right])

Attributes¶

`__module_name__`
`__author__`
`__email__`

larry._analysis.__module_name__ = __init__.py¶

larry._analysis.__author__¶

larry._analysis.__email__¶

larry._analysis.count_clonal_lineages(adata: anndata.AnnData, lineage_key: str = 'clone_idx', groupby_key: str = 'Time point', return_df: bool = False) → pandas.DataFrame¶

Count clonal lineages over groups (e.g., time points).

adata: type: anndata.AnnData
lineage_key: type: str default: “clone_idx”
groupby_key: type: str default: “Time point”

count_df

larry._analysis.count_d2_daughter_cells(adata, bins=50, plot_hist=True, plot_UMAPs=True)¶: plot histogram and umap of n_daughters

larry._analysis.count_clones_by_timepoint(adata: anndata.AnnData, lineage_key: str = 'clone_idx', time_key: str = 'Time point', key_added: str = 'lineage_time_counts', return_df: bool = False) → pandas.DataFrame¶

Annotates adata with pandas.DataFrame: adata.uns[‘clone_x_timepoint’].

adata: type: anndata.AnnData
lineage_key: type: str default: “clone_idx”
time_key: type: str default: “Time point”
return_df: type: bool default: False

None or clone_x_timepoint: type: NoneType or pandas.DataFrame

larry._analysis.estimate_growth_rates(adata: anndata.AnnData, pseudocount: int = 1, return_dict: bool = False, plot: bool = True, plot_colors: list = ['dodgerblue', 'crimson'], plot_labels: list = ['d4', 'd6']) → dict¶

Estimate growth rate from counts of daughter cells relative to d2 progenitors, across multiple timepoints.

count_dict

pseudocount

GrowthRateDict

larry._analysis.plot_growth_rates(adata: anndata.AnnData, colors: list = ['dodgerblue', 'crimson'], labels: list = ['d4', 'd6']) → None¶: None

larry._analysis.get_lineage_obs(adata: anndata.AnnData, lineage_idx: float, lineage_key: str = 'clone_idx', obs_key: str = 'Cell type annotation', time_key: str = 'Time point') → pandas.DataFrame¶

adata: type: anndata.AnnData
lineage_idx: type: float
lineage_key: type: str default: “clone_idx”
obs_key: type: str default: “Cell type annotation”
time_key: type: str default: “Time point”

lineage_obs: type: pandas.DataFrame

larry._analysis.calculate_dominate_fate(adata: anndata.AnnData, t_query: list = [4, 6], return_df: bool = False) → pandas.DataFrame¶

adata: type: anndata.AnnData
t_query: type: list default: [4, 6]

major_fate_df: type: pandas.DataFrame

larry._analysis.get_annotated_metadata_d2_lineage_cells(adata: anndata.AnnData, return_df: bool = False) → pandas.DataFrame¶

Get the annotated metadata table for d2 lineage cells

adata

d2_lin_cells_ann_meta: type: pandas.DataFrame

larry._analysis.growth_rate_grouped_violin_plot(adata, ncols=3)¶

class larry._analysis.LARRY_t0_test_indices(adata, time_key='Time point', t0=2, lineage_key='clone_idx', test_key='test')¶

Bases: larry._utils.ABCParse

Helper class that provides a standard way to create an ABC using inheritance.

property future_time¶

property test_lineages¶

property t0_obs¶

__call__(sample=1)¶

larry._analysis.temporal_cmap(t=None, idx_slice=1, pad_left=1, pad_right=1)¶