Smoothing count matrices

Create an AnnData objects with smoothed counts.

**Notebook for creating AnnData objects with smoothed counts. Added Sept. 25, 2024 **

**See the paper**

By operating on the nearest neighbor graph, we create a mini-pseudobulk expression profile of neighbors for each cell, thus smoothing the count data. Important parameters therefore include the number of neighbors for each cell and the “smooth_mode” which takes “adjacency” or “connectivity”. This dictates how the neighborhood graph is used. “Adjacency” weights all neighbors equally, while “connectivity” puts more weight on close neighbors.

Installation

Install the package using the following commands:

python3 -m pip install gssnng

# or to from github
python3 -m pip install git+https://github.com/IlyaLab/gssnng

Example script

Copy the script out from the cloned repo and run, check the paths if you get an error.

cp gssnng/gssnng/test/example_smoothing_counts.py  .

python3.10 example_smoothing_counts.py

Usage

See gssnng/notebooks for examples on all methods.

  1. Read in an AnnData object using scanpy (an h5ad file).

  2. Get gene sets formatted as a .gmt file. (default is UP, also uses _UP, _DN, and split gene sets _UP+_DN), see below for more details.

  3. Smooth, each category defined by the groupby will be an AnnData.

  4. Returned list is a tuple of (AnnData, Groupby-Category)

from gssnng import smoothing

 q = sc.datasets.pbmc3k_processed()

 q_list = smoothing.smooth_adata(adata=q,                    # AnnData object
                                    groupby='louvain',          # Will sample neighbors within this group, can take a list
                                    smooth_mode='connectivity', # Smooths matrix using distance weights from NN graph.
                                    recompute_neighbors=11,     # Rebuild nearest neighbor graph with groups, 0 turns off function
                                    cores=4)                    # Smoothed in parallel.

 # q_list is a list of tuples  (anndata, label)
 for qi in q_list:
     print(qi[1])  # the groupby-category names
     qi[0].X = qi[0].obsm['X_smooth'].copy()

 # the new AnnData object with smoothed counts in X
 q_new = anndata.concat([qi[0] for qi in q_list])

Parameters

These parameters are used with the “scores_cells.with_gene_sets” function.:

adata:  AnnData object from scanpy.read_*
AnnData containing the cells to be scored

groupby: [str, list, dict]
either a column label in adata.obs, and all categories taken, or a dict specifies one group.
SEE DESCRIPTION BELOW

smooth_mode: "adjacency", "connectivity", or "off"
Dictates how to use the neighborhood graph.
`adjacency` weights all neighbors equally, `connectivity` weights close neighbors more

recompute_neighbors: int
should neighbors be recomputed within each group, 0 for no, >0 for yes and specifies N

cores: int
number of parallel processes to work through groupby groups

Groupby

The specific neighborhood for each cell can be controlled by using the groupby parameter. In the example above, by setting groupby=’louvain’, only cells within a louvain cluster will be considered as being part of the neighborhood and will available for sampling.

Groupby specifies a column name that’s found in the AnnData.obs table, and it can also take a list of column names. In that case, cells will be grouped as the intersection of categories. For example, using groupby=[‘louvain’,’phenotype’] will take cells that are first in a given louvain cluster and then also in a given phenotype group. By also setting the recompute_neighbors, the nearest neighbor graph is recomputed within this subset of cells. Controlling the neighborhood leads to more controlled smoothing of the count matrix and is more suitable for downstream comparisons.

References

rank biased overlap: https://arxiv.org/pdf/1408.3587.pdf

singscore: https://pubmed.ncbi.nlm.nih.gov/30400809/

anndata: https://anndata.readthedocs.io/en/latest/

MSigDB: https://www.gsea-msigdb.org/gsea/msigdb/

ssGSEA: https://gsea-msigdb.github.io/ssGSEA-gpmodule/v10/index.html

decoupler: https://academic.oup.com/bioinformaticsadvances/article/2/1/vbac016/6544613

omnipath: https://omnipathdb.org/