# SketchKH
Distribution-Informed Sketching with Kernel Herding
## Overview
We provide a set of functions for distribution-aware sketching of multiple profiled single-cell samples via Kernel Herding. Our sketches select a small, representative set of cells from each profiled sample so that all major immune cell-types and their relative frequencies are well-represented.
* Please see our paper for more information (ACM-BCB 2022) : https://arxiv.org/abs/2207.00584
* Updated : June 27, 2023
## Installation
Dependencies
* Python >= 3.6, anndata >= 0.7.6, numpy >= 1.22.4, scipy >= 1.7.1, tqdm
You can install the package with `pip` by,
```
pip install sketchKH
```
Alternatively, you can clone the git repository by,
```
git clone https://github.com/CompCy-lab/SketchKH.git
```
## Example usage
To perform sketching, first read in a preprocessed `.h5ad` adata object. This dataset contains multiple profiled single-cell samples. Hence, sketches will select a limited set of cells from each profiled sample. We refer to each profiled sample as a *sample-set*.
```python
import anndata
import os
adata = anndata.read_h5ad(os.path.join('data', 'nk_cell_preprocessed.h5ad'))
```
Then simply sketch your data with 500 cells per sample-set by,
```python
# Inputs
# adata: annotated data object (dimensions = cells x features)
# sample_set_key: string referring to the key within adata.obs that contains the sample-sets to subsample
# sample_set_inds: (alternative to specifying sample_set_key) list of arrays containing the indices of the sample-sets to subsample
# gamma: scale parameter for the normal distribution standard deviation in random Fourier frequency feature computation
# frequency_seed: random state
# num_subsamples: number of cells to subsample per sample-set
# n_jobs: number of tasks
# ----------------------------
# Returns:
# kh_indices: list of indices referencing the subsampled cells per sample-set
# adata_subsample: downsampled annotated data object (dimensions = num_subsamples*sample-sets x features)
# ----------------------------
from sketchKH import *
kh_indices, adata_subsample = sketch(adata, sample_set_key = 'FCS_File', gamma = 1, num_subsamples = 500, frequency_seed = 0, n_jobs = -1)
```
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"description": "# SketchKH\nDistribution-Informed Sketching with Kernel Herding \n\n## Overview\nWe provide a set of functions for distribution-aware sketching of multiple profiled single-cell samples via Kernel Herding. Our sketches select a small, representative set of cells from each profiled sample so that all major immune cell-types and their relative frequencies are well-represented. \n* Please see our paper for more information (ACM-BCB 2022) : https://arxiv.org/abs/2207.00584\n* Updated : June 27, 2023\n\n\n\n## Installation\nDependencies\n* Python >= 3.6, anndata >= 0.7.6, numpy >= 1.22.4, scipy >= 1.7.1, tqdm\n\nYou can install the package with `pip` by,\n```\npip install sketchKH\n```\n\nAlternatively, you can clone the git repository by,\n```\ngit clone https://github.com/CompCy-lab/SketchKH.git\n```\n\n## Example usage\nTo perform sketching, first read in a preprocessed `.h5ad` adata object. This dataset contains multiple profiled single-cell samples. Hence, sketches will select a limited set of cells from each profiled sample. We refer to each profiled sample as a *sample-set*. \n\n```python\nimport anndata\nimport os\nadata = anndata.read_h5ad(os.path.join('data', 'nk_cell_preprocessed.h5ad'))\n```\n\nThen simply sketch your data with 500 cells per sample-set by,\n```python\n\n# Inputs\n# adata: annotated data object (dimensions = cells x features)\n# sample_set_key: string referring to the key within adata.obs that contains the sample-sets to subsample\n# sample_set_inds: (alternative to specifying sample_set_key) list of arrays containing the indices of the sample-sets to subsample \n# gamma: scale parameter for the normal distribution standard deviation in random Fourier frequency feature computation\n# frequency_seed: random state\n# num_subsamples: number of cells to subsample per sample-set\n# n_jobs: number of tasks\n# ----------------------------\n\n# Returns:\n# kh_indices: list of indices referencing the subsampled cells per sample-set\n# adata_subsample: downsampled annotated data object (dimensions = num_subsamples*sample-sets x features)\n\n# ----------------------------\nfrom sketchKH import *\nkh_indices, adata_subsample = sketch(adata, sample_set_key = 'FCS_File', gamma = 1, num_subsamples = 500, frequency_seed = 0, n_jobs = -1)\n```\n",
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