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    "info": {
        "author": "Xikang Feng",
        "author_email": "xikanfeng2@gmail.com",
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        "description": "# I-Impute: a coherent strategy to impute singlecell RNA sequencing data\n\nI-Impute is a coherent method to impute scRNA-seq data. I-Impute leverages continuous similarities and dropout probabilities and refines the data iteratively to make the final outputself-consistent. I-Impute exhibits robust imputation ability and follows the coherent principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data.\n\n## Pre-requirements\n* python3\n* numpy>=1.16.1\n* pandas>=0.23.4,<0.24\n* scipy>=1.3.0\n* scikit-learn>=0.21.1\n* tasklogger>=0.4.0\n\n### install requirements\n```Bash\npip install -r requirements.txt\n```\n\n## Installation\n\n### Installation with pip\nTo install with pip, run the following from a terminal:\n```Bash\npip install i-impute\n```\n\n### Installation from Github\nTo clone the repository and install manually, run the following from a terminal:\n```Bash\ngit clone https://github.com/xikanfeng2/I-Impute.git\ncd I-Impute\npython setup.py install\n```\n\n## Usage\n\n### Quick start\nThe following code runs MAGIC on simulation data located in the I-Impute repository.\n\n```Python\nimport iimpute\nimport pandas as pd\n\n# read your reads count or RPKM or TPM data\ndata = pd.read_csv('simluation-data/sim-counts.csv', index_col=0)\n\n# create I-Impute object\niimpute_operator = iimpute.IImpute(normalize=False)\n\n# impute\nimputed_data = iimpute_operator.impute(data)\n\n# store result to a file\nimputed_data.to_csv('your file name')\n\n# iterative mode\niimpute_operator = iimpute.IImpute(normalize=False, iteration=True)\n\n# impute\nimputed_data = iimpute_operator.impute(data)\n\n# store result to a file\nimputed_data.to_csv('your file name')\n```\n\n### Parameters\n```Python\nIImpute(n=20, c_drop=0.5, p_pca=0.4, alpha=0.01, normalize=True, iteration=False, verbose=1)\n```\nParameters\n\n* n : int, optional, default: 20\n\n    The nth of nearest neighbors on which to build kernel when calculating affinity matrix.\n\n* c_drop : float, optional, default: 0.5\n\n    Dropout event cutoff. For entry whose dropout probability is less than c_drop, we consider it as a real observation, its original value will remain. Otherwise, we conduct the imputation with the aid of information from similar cells.\n\n* p_pca : float, optional, default: 0.4\n\n    Percentage of variance explained by the selected components of PCA. It determines the nmumber of PCs used to calculate the distance between cells.\n\n* alpha : float, optional, default: 0.01\n\n    L1 penalty for Lasso regression.\n\n* normalize : boolean, optional, default: True\n\n    By default, I-Impute takes in an unnormalized matrix and performs library size normalization during the denoising step. However, if your data is already normalized or normalization is not desired, you can set normalize=False.\n\n* iteration : boolean, optional, default: False\n\n    The imputation process only performs once when False (it is equivalent to C-Impute described in our paper). The imputation process will iterate n times to achieve self-constistent imputation matrix.\n\n* verbose : `int` or `boolean`, optional, default: 1\n\n    If `True` or `> 0`, print status messages\n\n## Cite us\nTBD\n\n## Help\nIf you have any questions or require assistance using I-Impute, please contact us with xikanfeng2@gmail.com.\n\n",
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