I-Impute: a coherent strategy to impute singlecell RNA sequencing data
I-Impute: a self-consistent method to impute single cell RNA sequencing data
I-Impute is a “self-consistent” method method to impute scRNA-seq data. I-Impute leverages continuous similarities and dropout probabilities and refines the data iteratively to make the final output "self-consistent". I-Impute exhibits robust imputation ability and follows the “self-consistency” principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data.
pip install -r requirements.txt
Installation with pip
To install with pip, run the following from a terminal:
pip install i-impute
Installation from Github
To clone the repository and install manually, run the following from a terminal:
git clone https://github.com/xikanfeng2/I-Impute.git cd I-Impute python setup.py install
The following code runs MAGIC on simulation data located in the I-Impute repository.
import iimpute import pandas as pd # read your reads count or RPKM or TPM data data = pd.read_csv('simluation-data/sim-counts.csv', index_col=0) # create I-Impute object iimpute_operator = iimpute.IImpute(normalize=False) # impute imputed_data = iimpute_operator.impute(data) # store result to a file imputed_data.to_csv('your file name') # iterative mode iimpute_operator = iimpute.IImpute(normalize=False, iteration=True) # impute imputed_data = iimpute_operator.impute(data) # store result to a file imputed_data.to_csv('your file name')
IImpute(n=20, c_drop=0.5, p_pca=0.4, alpha=0.01, normalize=True, iteration=False, verbose=1)
n : int, optional, default: 20
The nth of nearest neighbors on which to build kernel when calculating affinity matrix.
c_drop : float, optional, default: 0.5
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.
p_pca : float, optional, default: 0.4
Percentage of variance explained by the selected components of PCA. It determines the nmumber of PCs used to calculate the distance between cells.
alpha : float, optional, default: 0.01
L1 penalty for Lasso regression.
normalize : boolean, optional, default: True
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.
iteration : boolean, optional, default: False
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.
boolean, optional, default: 1
> 0, print status messages
Feng, X., Chen, L., Wang, Z., & Li, S. C. (2019). I-Impute: a self-consistent method to impute single cell RNA sequencing data. bioRxiv, 772723. doi: https://doi.org/10.1101/772723.
If you have any questions or require assistance using I-Impute, please contact us with email@example.com.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.