by python

'tsne_scores.csv' is single-cell gene expression dataset of Arabidopsis thaliana root cells processed by a 10x genomics Cell Ranger pipeline. The dataset is pre-processed t-SNE dimensionality reduction technique. You will use the t-SNE vectors to identify the clusters using DBSCAN. 1. Compute required parameters (MinPts, eps) for DBSCAN clustering as illustrated in lab4 notebook a. Since you have 2 dimensionality in the dataset, what is the value of MinPts should be? b. Based on your choice of MinPts. use the kth Nearest Neighbors to find the optimal value for eps (make the k value as ( MinPts +1) ). 2. Pass the calculated parameters to the DBSCAN to predict the clusters using the sklearn clusterDBSCAN class. 3. Visualize the cluster as a scatter plot and color the clusters using predicted class labels. Question 2: [PLO K1 / CLO 1/SO 1] [3 marks] 1. Using the same dataset questionl, apply kxosaos and hierarchal clustering (ward method) 2. Apply elbow method and show dendrogram to choose the best value for the number of clusters. 3. Compare the clustering results of kmeaus and hierarchal clustering to DBSCAN's results