<jupyter_start><jupyter_text>Table 7 and Figure 9: Visualizations of  the Hydra data using t-SNE with respect to different values of perplexity. Table summarized the neigboring cells in Figure 9.<jupyter_code>color_palette = sns.color_palette("husl", n_colors=38)
color_palette[15] = '#f77189'
unique_labels = np.unique(y)
fig, axes = plt.subplots(2, 3)
for ind in range(len(tsnes)):
    tsne = tsnes[ind]
    row, col = ind // 3, ind % 3
    ax = axes[row, col]
    for yi in unique_labels:
            ax.scatter(tsne[y==yi, 0], tsne[y==yi, 1], label=yi, color=color_palette[yi])
            if (yi + 1 == 19) or  (yi + 1 == 30):
                pass
            else:
                ax.text(np.mean(tsne[y==yi, 0]), np.mean(tsne[y==yi, 1]), yi + 1)
    ax.set_title("Perp={}".format(perplexities[ind]))
    if col == 0:
        ax.set_ylabel("tSNE2")
    if row == 1:
        ax.set_xlabel("tSNE1")
# plt.legend()
fig.set_size_inches(10, 6)
# plt.legend(ncol=5)
plt.tight_layout()<jupyter_output><empty_output><jupyter_text>Figure 10: Heatmaps of the first 40 principal components of the hydra single-cell RNA-seq data.<jupyter_code>_, counts = np.unique(y, return_counts=True)
comparsion = 15
tentacle = 18
tentnem = 29
X1 = X[y == tentacle, :]
X2 = X[y == tentnem, :]
X3 = X[y == comparsion, :]
X12 = np.concatenate([X1, X2])
X13 = np.concatenate([X1, X3])
row_colors12 = ['#35ae9c'] * X1.shape[0] + ['#af88f4'] * X2.shape[0]
row_colors13 = ['#35ae9c'] * X1.shape[0] + ['#f77189'] * X3.shape[0]
counts, X1.shape, X2.shape, X3.shape, counts[13]
g12 = sns.clustermap(X12, row_colors=row_colors12, col_cluster=False, cmap='coolwarm',
                    xticklabels=[], yticklabels=[], figsize=(2.75, 3.5))
g12.cax.set_visible(False)
g13 = sns.clustermap(X13, row_colors=row_colors13, col_cluster=False, cmap='coolwarm',
                    xticklabels=[], yticklabels=[],figsize=(2.75, 3.5))
g13.cax.set_visible(False)<jupyter_output><empty_output>
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