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        "description": "# PARC\nPARC, \u201cphenotyping by accelerated refined community-partitioning\u201d - is a fast, automated, combinatorial  graph-based clustering approach that integrates hierarchical graph construction (HNSW) and data-driven graph-pruning with the new Leiden community-detection algorithm.\n\n## Getting Started\n### install using pip\n```\nconda create --name ParcEnv pip // (optional)\npip install parc // tested on linux\n```\n### install by cloning repository and running setup.py \n```\ngit clone https://github.com/ShobiStassen/PARC.git \npython3 setup.py install // cd into the directory of the cloned PARC folder containing setup.py and issue this command\n```\n\n### install dependencies separately if needed\npip install leidenalg, igraph and hnswlib\n\n## Example Usage 1. (small test sets) - IRIS and Digits dataset from sklearn\n\n```\nfrom parc import PARC\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\n\n// load sample IRIS data\n//data (n_obs x k_dim, 150x4)\niris = datasets.load_iris()\nX = iris.data\ny=iris.target\n\nplt.scatter(X[:,0],X[:,1], c = y) // colored by 'ground truth'\nplt.show()\n\nParc1 = parc.PARC(X,y) // instantiate PARC\nParc1.run_PARC() // run the clustering\nparc_labels = Parc1.labels\n\n# View scatterplot colored by PARC labels\n\nplt.scatter(X[:, 0], X[:, 1], c=parc_labels)\nplt.show()\n\n// load sample digits data\ndigits = datasets.load_digits()\nX = digits.data // (n_obs x k_dim, 1797x64) \ny = digits.target\nParc2 = parc.PARC(X,y, jac_std_global='median') // 'median' is default pruning level\nParc2.run_PARC()\nparc_labels = Parc2.labels\n\n```\n## Example Usage 2. (mid-scale scRNA-seq): 10X PBMC (Zheng et al., 2017)\n[pre-processed datafile](https://drive.google.com/file/d/1H4gOZ09haP_VPCwsYxZt4vf3hJ1GZj3b/view?usp=sharing)\n\n[annotations](Datasets/annotations_zhang.txt)\n\n```\nimport PARC\nimport csv\n\n## load data (50 PCs of filtered gene matrix pre-processed as per Zheng et al. 2017)\n\nX = csv.reader(open(\"'./pca50_pbmc68k.txt\", 'rt'),delimiter = \",\")\nX = np.array(list(X)) // (n_obs x k_dim, 68579 x 50)\nX = X.astype(\"float\")\n// OR with pandas as: X = pd.read_csv(\"'./pca50_pbmc68k.txt\").values.astype(\"float\")\n\ny = [] // annotations\nwith open('/annotations_zhang.txt', 'rt') as f: \n    for line in f: y.append(line.strip().replace('\\\"', ''))\n// OR with pandas as: y =  list(pd.read_csv('./data/zheng17_annotations.txt', header=None)[0])   \n\n\nparc1 = parc.PARC(X,y) // instantiate PARC\nparc1.run_PARC() // run the clustering\nparc_labels = parc1.labels \n```\n![](Images/10X_PBMC_PARC_andGround.png) tsne plot of annotations and PARC clustering\n\n## Example Usage 3. 10X PBMC (Zheng et al., 2017) integrating Scanpy pipeline\n\n[raw datafile](https://github.com/10XGenomics/single-cell-3prime-paper/tree/master/pbmc68k_analysis)\n\n```\npip install scanpy\n```\n\n```\nimport scanpy.api as sc\nimport pandas as pd\n//load data\npath = './data/zheng17_filtered_matrices_mex/hg19/'\nadata = sc.read(path + 'matrix.mtx', cache=True).T  # transpose the data\nadata.var_names = pd.read_csv(path + 'genes.tsv', header=None, sep='\\t')[1]\nadata.obs_names = pd.read_csv(path + 'barcodes.tsv', header=None)[0]\n\n// annotations as per correlation with pure samples\nannotations = list(pd.read_csv('./data/zheng17_annotations.txt', header=None)[0])\nadata.obs['annotations'] = pd.Categorical(annotations)\n\n//pre-process as per Zheng et al., and take first 50 PCs for analysis\nsc.pp.recipe_zheng17(adata)\nsc.tl.pca(adata, n_comps=50)\nparc1 = parc.PARC(adata2.obsm['X_pca'], annotations)\nparc_labels = parc1.labels\nadata2.obs[\"PARC\"] = pd.Categorical(parc_labels)\n\n//visualize\nsc.pl.umap(adata, color='annotations')\nsc.pl.umap(adata, color='PARC')\n```\n## Example Usage 4. Large-scale (70K subset and 1.1M cells) Lung Cancer cells (multi-ATOM imaging cytometry based features)\n\n[normalized image-based feature matrix 70K cells](https://drive.google.com/open?id=1LeFjxGlaoaZN9sh0nuuMFBK0bvxPiaUz)\n\n[Lung Cancer cells annotation 70K cells](https://drive.google.com/open?id=1iwXQkdwEwplhZ1v0jYWnu2CHziOt_D9C)\n\n[1.1M cell features and annotations](https://data.mendeley.com/datasets/nnbfwjvmvw/draft?a=dae895d4-25cd-4bdf-b3e4-57dd31c11e37)\n\n```\nimport PARC\nimport pandas as pd\n\n// load data: digital mix of 7 cell lines from 7 sets of pure samples (1.1M cells x 26 features)\nX = pd.read_csv(\"'./LungData.txt\").values.astype(\"float\") \ny = list(pd.read_csv('./LungData_annotations.txt', header=None)[0]) // list of cell-type annotations\n\n// run PARC\nparc1 = parc.PARC(X, y)\nparc_labels = parc1.labels\n\n```\n![](Images/70K_Lung_github_overview.png) tsne plot of annotations and PARC clustering, heatmap of features\n\n## References to dependencies \n- Leiden (pip install leidenalg) (V.A. 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