larry._utils._count_fate_values

Module Contents

Classes

IndexSubsets

Container for keep track of subset indices.

FateValues

Functions

_has_t(df, time_key, query_t)

return if ALL specified values are contained in passed list

filter_fate(adata[, fate_time, fate, time_key, state_key])

time_occupance([fate_time, return_df])

Parameters:

fated_idx(, lineage_key, time_key)

generate a list of lineages that are seen at d2 as well as d4 and d6

annotate_fated() → None)

We use this function to denote lineages with cells at d2

count_t0_cell_fates(adata[, key_added, return_df])

d2_cell_fate_matrix(adata[, time_key, lineage_key])

count_fate_values(adata[, origin_time, fate_time, ...])

Count fate values.

Attributes

NoneType

larry._utils._count_fate_values.NoneType
class larry._utils._count_fate_values.IndexSubsets(adata, time_key='Time point', lineage_key='clone_idx')

Bases: larry._utils._abc_parse.ABCParse

Container for keep track of subset indices.

property lineage_traced
_configure_time_subset()
_configure_lineage_traced_time_subset()
larry._utils._count_fate_values._has_t(df, time_key, query_t: list)

return if ALL specified values are contained in passed list

larry._utils._count_fate_values.filter_fate(adata, fate_time=[4, 6], fate=['undiff'], time_key='Time point', state_key='Cell type annotation')
larry._utils._count_fate_values.time_occupance(adata: anndata.AnnData, lineage_key: str = 'clone_idx', exclude_fate: tuple = ('Cell type annotation', ['undiff']), fate_time=[4, 6], time_key: str = 'Time point', return_df=False) → Union[pandas.DataFrame, None]
adata

type: anndata.AnnData

lineage_key

type: str

time_key

type: str

time_occupance

type: pandas.DataFrame

larry._utils._count_fate_values.fated_idx(adata, fate_time=[4, 6], exclude_fate: tuple = ('Cell type annotation', ['undiff']), lineage_key: str = 'clone_idx', time_key: str = 'Time point') → list

generate a list of lineages that are seen at d2 as well as d4 and d6

  1. This function returns the indices of lineages NOT cells.

larry._utils._count_fate_values.annotate_fated(adata, lineage_key='clone_idx', time_key='Time point', t0=2, fate_time=[4, 6], key_added='fate_observed', t0_key_added='t0_fated', exclude_fate: tuple = ('Cell type annotation', ['undiff'])) → None

We use this function to denote lineages with cells at d2 and one or more cells in [d4, d6]

Updates adata.obs with two columns -> adata.obs[[‘fate_observed’, ‘t0_fated’]]

class larry._utils._count_fate_values.FateValues(adata, origin_time=[2], fate_time=[4, 6], annotation_key='Cell type annotation', time_key='Time point', lineage_key='clone_idx')

Bases: larry._utils._abc_parse.ABCParse

_count_values_at_lineage_fate(df, labels_excluded=['undiff'])

df is the pandas.DataFrame obs table for a single clonal lineage

__call__() → pandas.DataFrame

Takes ~13s for the in vitro dataset

larry._utils._count_fate_values.count_t0_cell_fates(adata, key_added='cell_fate_df', return_df=False)
larry._utils._count_fate_values.d2_cell_fate_matrix(adata, time_key: str = 'Time point', lineage_key: str = 'clone_idx')
larry._utils._count_fate_values.count_fate_values(adata, origin_time=[2], fate_time=[4, 6], annotation_key='Cell type annotation', time_key='Time point', lineage_key='clone_idx', key_added='fate_counts', return_dfs=False)

Count fate values.