Single cell RNA-seq data analysis using Markov Affinity-Based Graph Imputation

import magic
import pandas as pd
import matplotlib.pyplot as plt

X = pd.read_csv("test_data.csv")
X.shape

(500, 197)

magic_operator = magic.MAGIC()
X_magic = magic_operator.fit_transform(X, genes=['VIM', 'CDH1', 'ZEB1'])
X_magic.shape

Calculating MAGIC...
  Running MAGIC on 500 cells and 197 genes.
  Calculating graph and diffusion operator...
    Calculating PCA...
    Calculated PCA in 0.02 seconds.
    Calculating KNN search...
    Calculated KNN search in 0.04 seconds.
    Calculating affinities...
    Calculated affinities in 0.03 seconds.
  Calculated graph and diffusion operator in 0.09 seconds.
  Calculating imputation...
Calculated MAGIC in 0.10 seconds.






(500, 3)

magic_operator.set_params(t=7, a=15, k=5, knn_dist='euclidean', n_jobs=1, n_pca=100, random_state=None, verbose=1)
X_magic = magic_operator.transform(genes=['VIM', 'CDH1', 'ZEB1'])
X_magic.shape

Running MAGIC on 500 cells and 197 genes.
Calculating graph and diffusion operator...
  Calculating PCA...
  Calculated PCA in 0.01 seconds.
  Calculating KNN search...
  Calculated KNN search in 0.03 seconds.
  Calculating affinities...
  Calculated affinities in 0.03 seconds.
Calculated graph and diffusion operator in 0.08 seconds.
Calculating imputation...





(500, 3)

X_magic = magic_operator.transform(genes="all_genes")
X_magic.shape

Calculating imputation...





(500, 197)

plt.scatter(X_magic['VIM'], X_magic['CDH1'],c=X_magic['ZEB1'], s=1, cmap='inferno')

<matplotlib.collections.PathCollection at 0x7fc92b0eafa0>

png

References