The relevant machine configuration of the PC and HPC can be found in Supplementary Table 3. Missing data simulation, MICE, missForest and comparison were operated in R 3.5.1. GAIN was developed with Python 3.5. The level of significance for all statistical tests was set as 0.05....
MAGIC has been implemented in Python, Matlab, and R. To get started immediately, check out our tutorials: Python Epithelial-to-Mesenchymal Transition Tutorial Bone Marrow Tutorial R Epithelial-to-Mesenchymal Transition Tutorial Bone Marrow Tutorial Magic reveals the interaction between Vimentin (VIM), ...
Missing data simulation, MICE, missForest and comparison were operated in R 3.5.1. GAIN was developed with Python 3.5. The level of significance for all statistical tests was set as 0.05. Results Experiments on DM-data Table 1 presents the imputation errors (NRMSE and PFC for continuous and ...
git clone git://github.com/KrishnaswamyLab/MAGIC.git cd MAGIC/python python setup.py install --user Usage Quick Start The following code runs MAGIC on test data located in the MAGIC repository. import magic import pandas as pd import matplotlib.pyplot as plt X = pd.read_csv("MAGIC/data/...
missing values, is often conducted as the first step in pre-processing scRNA-seq data. In this paper, we propose a novel Zero-Inflated Negative Binomial (ZINB) model-based autoencoder for imputing discrete scRNA-seq data. The novelties of our method are twofold. First, in addition to ...
A higher score on these cell clustering metrics indicates better performance in cell population identification, consequently leading to better predictions in spatial transcriptomics. Implementation Details The stImpute was implemented using PyTorch and Python. To optimize the AE model, we used the Adam ...
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In R, run this command to install MAGIC and all dependencies: In a terminal, run the following command to install the Python repository. pip install --user magic-impute To clone the repository and install manually, run the following from a terminal: ...