EnsembleKQC

An Unsupervised Ensemble Learning Method for Quality Control of Single Cell RNA-seq Sequencing Data

Requirements

• Python 3
• Scikt-learn
• Numpy

Usage

Download all files and run following command to display help message

$ python runEnsembleKQC.py --help

usage: runEnsembleKQC.py [-h] [--input_path INPUT_PATH] [--lower_bound LOWER_BOUND]
                    [--upper_bound UPPER_BOUND] [--labeld LABELD]
                    [--output_path OUTPUT_PATH]

optional arguments:
  -h, --help            show this help message and exit
  --input_path INPUT_PATH
                        path of input data
  --lower_bound LOWER_BOUND
                        lower bound of estimated low-quality cell number
  --upper_bound UPPER_BOUND
                        upper bound of estimated low-quality cell number
  --labeld LABELD       whether the data has quality labels. If true,
                        evaluation information will be printed.
  --output_path OUTPUT_PATH
                        path of output data

Example

$ python runEnsembleKQC.py --input_path ./example_data/Kolodziejczyk.csv --lower_bound 96 --upper_bound 192 --labeled False --output_path ./result.csv

$ python runEnsembleKQC.py --input_path ./example_data/labeled_Kolodziejczyk.csv --lower_bound 96 --upper_bound 192 --labeled True --output_path ./result.csv