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    "info": {
        "author": "Tobias O. Stannius",
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        "description": "# CELLEX\nCELLEX (CELL-type EXpression-specificity) is a tool for computing cell-type Expression Specificity (ES) profiles. It employs a \"wisdom of the crowd\"-approach by integrating multiple ES metrics, thus combining complementary cell-type ES profiles, to capture multiple aspects of ES and obtain improved robustness.\n\n\n\n## Contents\n* [Quick start](#Quick-start)\n* [Documentation](docs/)\n\n\n# Quick start\nThis brief tutorial showcases the core features of CELLEX.\n\n## Setup\nDownload this repository and place it in the same directory as the script or Jupyter Notebook you wish to use CELLEX with.\n\n## Import modules\n```python\nimport numpy as np # needed for formatting data for this tutorial\nimport pandas as pd # needed for formatting data for this tutorial\nimport CELLEX.cellex as cellex # needed when importing directly from this repo\n```\n\n## Load input data and metadata\n```python\ndata = pd.read_csv(\"./data.csv\", index_col=0)\nmetadata = pd.read_csv(\"./metadata.csv\", index_col=0)\n```\n\n### Data format\nData may consist of UMI counts (integer) for each **gene** and **cell**.\n\n|               | cell_1                | ... | cell_9                 |\n|---------------|-----------------------|-----|------------------------|\n| gene_x        | 0                     | ... | 4                      |\n| ...           | ...                   | ... | ...                    |\n| gene_z        | 3                     | ... | 1                      |\n\nShape: *m* genes by *n* cells.\n\n### Metadata format\nMetadata should consist of cell id's and matching annotation (string).\n\n| cell_id                | cell_type |\n|------------------------|-----------|\n| cell_1                 | type_A    |\n| ...                    | ...       |\n| cell_9                 | type_C    |\n\nShape: *n* cells by 2.\n\n## Create ESObject and compute ESmu\n\n```python\neso = cellex.ESObject(df=data, annotation=metadata, verbose=True)\n\neso.compute(verbose=True)\n```\n\n## Save result(s)\nOnly saves ESmu by default. The ESmu specificity scores may be used directly with **[CELLECT](https://github.com/perslab/CELLECT)**.\n\n```python\neso.save(verbose=True)\n```\n\n### Output format\nOutput consist of Expression Specificity Weights (float) for each **gene** and **cell-type**. ESmu values lie in the range [0,1].\n\n|               | type_A                | ... | type_C                 |\n|---------------|-----------------------|-----|------------------------|\n| gene_x        | 0.0                   | ... | 0.9                    |\n| ...           | ...                   | ... | ...                    |\n| gene_z        | 0.1                   | ... | 0.2                    |\n\nShape: *m* genes by *x* unique annotations. N.B. a number of genes may be removed during preprocessing.\n\n\n",
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        "home_page": "https://github.com/perslab/CELLEX",
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        "name": "cellex",
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        "summary": "Compute single-cell cell-type expression specificity",
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