iforest: Detect outliers.

In charliex210/sctools: Tools for single cell RNAseq analysis

Description

The implement of iforest is dependent on numpy, scipy and sklearn in python, make sure that they have been installed in your enviroment.

Usage

iforest(mat, n_estimators = 1000, outliers_fraction = 0.1,
  random_state = 1, n_jobs = 1)

Arguments

mat	Gene expression matrix, columns are cells and rows are genes.
n_estimators	The number of base estimators in the ensemble.
outliers_fraction	float in (0, 0.5). The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the decision function.
random_state	RandomState instance.
n_jobs	The number of jobs to run in parallel for both 'fit' and 'predict'. If -1, then the number of jobs is set to the number of cores.
verbose	Controls the verbosity of the tree building process.

Details

Use Isolation Forest algorithm to detect outliers of cells.

Value

list of outlier score and indice.

References

Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest." Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.

Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation-based anomaly detection." ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.

charliex210/sctools documentation built on Dec. 29, 2021, 11:19 p.m.