Building Meta Cell Groups¶

For incredibily large datasets where it's helpful to create meta cell groupings, AnnSQL conveniently is packaged with functionality to build groupings of cells by specfic observational aggregations. In this example we build groupings of all mean gene expression per group (cell).

Import Libraries¶

In [2]:

import scanpy as sc
import pandas as pd
from AnnSQL import AnnSQL

import scanpy as sc import pandas as pd from AnnSQL import AnnSQL

Open a database¶

In [11]:

asql = AnnSQL(db="db/splatter/data_10000.asql")
asql.query("DESCRIBE obs")

asql = AnnSQL(db="db/splatter/data_10000.asql") asql.query("DESCRIBE obs")

Out[11]:

	column_name	column_type	null	key	default	extra
0	cell_id	VARCHAR	YES	None	None	None
1	cell_type	BIGINT	YES	None	None	None

Build the meta groupings based on the obs.cell_type grouping¶

In [13]:

asql.build_meta_cells(primary_cluster="cell_type", aggregate_type="AVG", chunk_size=750, print_progress=True)

asql.build_meta_cells(primary_cluster="cell_type", aggregate_type="AVG", chunk_size=750, print_progress=True)

Processing chunk 1 of 750
Processing chunk 751 of 1500
Processing chunk 1501 of 2250
Processing chunk 2251 of 3000
Processing chunk 3001 of 3750
Processing chunk 3751 of 4500
Processing chunk 4501 of 5250
Processing chunk 5251 of 6000
Processing chunk 6001 of 6750
Processing chunk 6751 of 7500
Processing chunk 7501 of 8250
Processing chunk 8251 of 9000
Processing chunk 9001 of 9750
Processing chunk 9751 of 10500
meta_cells table created. You may now query the table for results.

Take a look at the meta_cell table¶

In this example, there are 5 cell types (1-5) and no secondary grouping (None). The remaining columns represent the average expression of all genes in each cell type grouping.

In [14]:

asql.query("SELECT * FROM meta_cells")

asql.query("SELECT * FROM meta_cells")

Out[14]:

	cell_type	None	cell_count	cell_id	gene_0	gene_1	gene_2	gene_3	gene_4	gene_5	...	gene_9990	gene_9991	gene_9992	gene_9993	gene_9994	gene_9995	gene_9996	gene_9997	gene_9998	gene_9999
0	1	None	1961.0	None	0.037226	0.058134	0.095869	0.013259	0.001530	0.004080	...	0.192249	0.111168	0.065783	0.088220	4.595614	0.142784	0.036206	0.876594	0.180010	0.018358
1	2	None	1980.0	None	0.048990	0.050000	0.089899	0.013131	0.000000	0.004545	...	0.160101	0.068182	0.070707	0.104545	4.339394	0.122727	0.048485	0.816667	0.176263	0.028788
2	3	None	2046.0	None	0.040078	0.122678	5.152493	0.010753	0.000000	0.007820	...	0.143206	0.082600	0.071848	0.102639	4.347996	0.160802	0.042522	0.844086	0.187195	0.021505
3	4	None	1973.0	None	0.047136	0.047643	0.059807	0.008109	0.003548	0.006082	...	0.168272	0.075013	0.040547	0.082615	2.714141	0.045109	0.038013	0.573746	0.129752	0.017739
4	5	None	2040.0	None	0.038725	0.052941	0.077941	0.011765	0.000980	0.005882	...	0.173039	0.085294	0.044608	0.076471	2.862255	0.018137	0.048039	0.582353	0.154902	0.014216

5 rows × 10004 columns