UMAP for Data Integration¶
Here we will overlap graphs from single cell RNAseq and Proteomics data sets from the CITE-seq technology, and produce a consensus / integrative OMICs UMAP plot. The idea is to convert the two data types into graphs, i.e. non-parametric space where they forget their technological differences, and overlap the graphs. The resulting graph will be used for constructing the UMAP embeddings.
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from IPython.display import Image
Image('/home/nikolay/Documents/Medium/UMAP_DataIntegration/UMAP_DataIntegration.png', width=2000)
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We will start with reading and log-transforming the OMICs data.
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import os
import numpy as np
import pandas as pd
from keras.models import Model
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from keras.utils import plot_model
from keras.layers import Input, Dense
from keras.layers.merge import concatenate
os.chdir('/home/nikolay/WABI/Misc/SingleCell/CITEseq/')
scRNAseq = pd.read_csv('scRNAseq.txt',sep='\t')
scProteomics = pd.read_csv('scProteomics.txt',sep='\t')
X_scRNAseq = scRNAseq.values[:,0:(scRNAseq.shape[1]-1)]
Y_scRNAseq = scRNAseq.values[:,scRNAseq.shape[1]-1]
X_scProteomics = scProteomics.values[:,0:(scProteomics.shape[1]-1)]
Y_scProteomics = scProteomics.values[:,scProteomics.shape[1]-1]
X_scRNAseq = np.log(X_scRNAseq + 1)
X_scProteomics = np.log(X_scProteomics + 1)
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X_scRNAseq.shape
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X_scProteomics.shape
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Let us display the UMAP embeddings of individual OMICs data sets. We will start with the scRNAseq dataset. As an optimal number of nearest neighbors we will choose sqrt(N) = sqrt(8617) = 93.
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import warnings
warnings.filterwarnings("ignore")
from umap import UMAP
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
X_reduced = PCA(n_components = 20).fit_transform(X_scRNAseq)
model = UMAP(n_components = 2, min_dist = 1, n_neighbors = 93, init = X_reduced[:, 0:2], n_epochs = 1000,
verbose = 2)
umap = model.fit_transform(X_reduced)
plt.figure(figsize = (20,15))
plt.scatter(umap[:, 0], umap[:, 1], c = Y_scRNAseq, cmap = 'tab20', s = 20)
plt.title('UMAP: scRNAseq', fontsize = 25);
plt.xlabel("UMAP1", fontsize = 22); plt.ylabel("UMAP2", fontsize = 22)
plt.show()
Now let us display UMAP embeddings for the scProteomics dataset of the CITE-seq sequencing technology.
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import warnings
warnings.filterwarnings("ignore")
from umap import UMAP
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
X_reduced = PCA(n_components = 10).fit_transform(X_scProteomics)
model = UMAP(n_components = 2, min_dist = 0.8, n_neighbors = 93, init = X_reduced[:, 0:2], n_epochs = 1000,
verbose = 2)
umap = model.fit_transform(X_reduced)
plt.figure(figsize = (20,15))
plt.scatter(umap[:, 0], umap[:, 1], c = Y_scProteomics, cmap = 'tab20', s = 20)
plt.title('UMAP: scProteomics', fontsize = 25);
plt.xlabel("UMAP1", fontsize = 22); plt.ylabel("UMAP2", fontsize = 22)
plt.show()
Now we will construct the intersection of the two graphs / simplicial sets, and visualize the resulting embeddings.
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import umap
X_reduced_scRNAseq = PCA(n_components = 20).fit_transform(X_scRNAseq)
X_reduced_scProteomics = PCA(n_components = 10).fit_transform(X_scProteomics)
fit1 = umap.UMAP(n_components = 2, min_dist = 1, n_neighbors = 93, n_epochs = 1000,
init = X_reduced_scRNAseq[:, 0:2], verbose = 2).fit(X_reduced_scRNAseq)
fit2 = umap.UMAP(n_components = 2, min_dist = 0.8, n_neighbors = 93, n_epochs = 1000,
init = X_reduced_scProteomics[:, 0:2], verbose = 2).fit(X_reduced_scProteomics)
intersection = umap.umap_. general_simplicial_set_intersection(fit1.graph_, fit2.graph_, weight = 0.45)
intersection = umap.umap_.reset_local_connectivity(intersection)
embedding = umap.umap_.simplicial_set_embedding(fit1._raw_data, intersection, fit1.n_components,
fit1.learning_rate, fit1._a, fit1._b,
fit1.repulsion_strength, fit1.negative_sample_rate,
1000, 'random', np.random, fit1.metric,
fit1._metric_kwds, False, densmap_kwds=False, output_dens=False)
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plt.figure(figsize = (20,15))
plt.scatter(embedding[0][:, 0], embedding[0][:, 1], c = Y_scRNAseq, cmap = 'tab20', s = 20)
plt.title('UMAP: scRNAseq + scProteomics', fontsize = 25);
plt.xlabel("UMAP1", fontsize = 22); plt.ylabel("UMAP2", fontsize = 22)
plt.show()
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