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rapids_singlecell.get.aggregate

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rapids_singlecell.get.aggregate#

rapids_singlecell.get.aggregate(adata, by, func, *, axis=None, mask=None, dof=1, layer=None, obsm=None, varm=None, return_sparse=False)[source]#

Aggregate data matrix based on some categorical grouping.

This function is useful for pseudobulking as well as plotting.

Aggregation to perform is specified by func, which can be a single metric or a list of metrics. Each metric is computed over the group and results in a new layer in the output AnnData object.

If none of layer, obsm, or varm are passed in, X will be used for aggregation data. If func only has length 1 or is just an AggType, then aggregation data is written to X. Otherwise, it is written to layers or xxxm as appropriate for the dimensions of the aggregation data.

Params#

adata

AnnData to be aggregated.

by

Key of the column to be grouped-by.

func

How to aggregate.

axis

Axis on which to find group by column.

mask

Boolean mask (or key to column containing mask) to apply along the axis.

dof

Degrees of freedom for variance. Defaults to 1.

layer

If not None, key for aggregation data.

obsm

If not None, key for aggregation data.

varm

If not None, key for aggregation data.

return_sparse

Whether to return a sparse matrix. Only works for sparse input data.

rtype:

AnnData

returns:

Aggregated AnnData.

Examples

Calculating mean expression and number of nonzero entries per cluster:

>>> import scanpy as sc, pandas as pd
>>> import rapids_singlecell as rsc
>>> pbmc = sc.datasets.pbmc3k_processed().raw.to_adata()
>>> rsc.get.anndata_to_GPU(pbmc)
>>> pbmc.shape
(2638, 13714)
>>> aggregated = rsc.get.aggregate(pbmc, by="louvain", func=["mean", "count_nonzero"])
>>> aggregated
AnnData object with n_obs × n_vars = 8 × 13714
    obs: 'louvain'
    var: 'n_cells'
    layers: 'mean', 'count_nonzero'

We can group over multiple columns:

>>> pbmc.obs["percent_mito_binned"] = pd.cut(pbmc.obs["percent_mito"], bins=5)
>>> rsc.get.aggregate(pbmc, by=["louvain", "percent_mito_binned"], func=["mean", "count_nonzero"])
AnnData object with n_obs × n_vars = 40 × 13714
    obs: 'louvain', 'percent_mito_binned'
    var: 'n_cells'
    layers: 'mean', 'count_nonzero'

Note that this filters out any combination of groups that wasn’t present in the original data.

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