larry._tools._cell_fates

Module Contents

Classes

CellFates

Functions

_sum_normalize_fate(→ pandas.DataFrame)	Given a DataFrame of integer counted fate/state observations, normalize to the total within the sample.
state_description_at_time(df[, time_key, state_label_key])

larry._tools._cell_fates._sum_normalize_fate(fate_df: pandas.DataFrame) → pandas.DataFrame

Given a DataFrame of integer counted fate/state observations, normalize to the total within the sample.

fate_df

DataFrame with fates along the columns and lineages along the index. type: pandas.DataFrame

1. If not already filtered for null-valued fates at the time of observations, these will return NaNs due to the resulting div. by zero.

2. Can have a multi-index but select for only a single timepoint if that’s the desired behavior.

larry._tools._cell_fates.state_description_at_time(df, time_key='Time point', state_label_key='Annotation')

class larry._tools._cell_fates.CellFates(adata, time_key='Time point', lineage_key='clone_idx', state_label_key='Annotation')

property fates

property lineage_state_descriptions: pandas.DataFrame

__parse__(kwargs, ignore=['self'])

__setup__(kwargs, ignore=['self'])

subset(subset_time=None)

# subset for lineages with cells at d2 and d6 # cf.subset([2, 6]) # subset for lineages with cells at all time points

_lineage_state_descriptions()

_configure_fates()

temporal_fate_descriptions()

combine_fate_matrices(t_combine: list = [4, 6], normalize=True)

Combine fate matrices across timepoints (e.g., d4 and d6)

clustermap(fate_df)

cf.clustermap(cf.combined)

format_state_for_merge(state_df)

state_to_cell_indexed_fate_bias(state_df)

Transform cell state df to a cell-indexed fate bias df

Can pass: cf.d6_state, cf.d4_state

__call__(subset_time=[2, 6], fate_t=6)