t-Distributed Stochastic Neighbor Embedding

Required packages

The required packages for this section are pandas, seaborn and scikit-learn. These can be installed with the following command in the command window (Windows) / terminal (Mac).

pip install pandas seaborn scikit-learn

Loading the data

For this section the following dataset will be used:

3MB

GOUT_CTRL_QC_Ales_data_31012025.xlsx

Load the dataset into a Pandas DataFrame named df as described in the basic plotting section:

import pandas as pd
df = pd.read_excel("GOUT_CTRL_QC_Ales_data_31012025.xlsx", decimal=",")
df.set_index("Sample Name", inplace=True)

PCA

Usually before running t-SNE, PCA is performed on the high-dimensional data to reduce the dimensions (e.g. 30, 50) and then t-SNE is applied on the derived Principal Components.

Data normalisation

The first step is to normalise the data such that the features (lipids) have zero mean and unit variance, this can easily be done with the StandardScaler from sklearn. By indexing the dataframe with df.iloc[:,1:] we'll select all the data in the dataframe, except for the first column, which contains the labels:

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
df_normalised = scaler.fit_transform(df.iloc[:,1:])

PCA

Next, we use the PCA sklearn from sklearn, we'll select the 50 first principal components and we'll apply the PCA algorithm to the normalised data:

from sklearn.decomposition import PCA

n_components = 50
pca = PCA(n_components=n_components)
pca_features = pca.fit_transform(df_normalised)

t-SNE

Then using t-SNE from sklearn we'll apply the t-SNE algrorithm on the 50 Principal Components.

from sklearn.manifold import TSNE

n_components = 2
tsne = TSNE(n_components=n_components)
tsne_features = tsne.fit_transform(pca_features)

The TSNE function from sklearn return the results as a numpy ndarray, let's put these results in a Pandas DataFrame:

tsne_features = pd.DataFrame(data=tsne_features,
                      columns=[f"t-SNE{i+1}" for i in range(n_components)],
                      index=df.index)
tsne_features["Label"] = df.Label

We can now visualise the projection of the samples to the new feature space with a scatterplot:

import seaborn as sns
import matplotlib.pyplot as plt

sns.scatterplot(x='t-SNE1',
       y='t-SNE2',
       data=tsne_features,
       hue="Label",
       palette=["red", "royalblue"]);
plt.show()