Please complete in either Python or R Language. We will infer a gene regulatory network from gene expression data and make a ROC plot. The link to download the dataset is here: https://files.fm/u/bqqfevej

In HW2, we will infer a gene regulatory network from gene expression data and make a ROC plot. Download the gene expression data in the course web page, where there are 500 samples and each sample has 10 gene expression Gene regulatory networks inference based on the correlation-based approach. -Dataset: o Gene_expression.csv: contains gene expression data o Adj.csv: contains adjacency matrix of ground truth You can follow this procedure 1. Load th e gene expression data (Gene_expression.cs v) and the ground truth adjacency matrix (Adj.csv) 2. Compute pairwise correlation matrix, and show the matrix. E.g., see Fig.1 3. Given the range of threshold (e.g., 0, 0.1, 0.2, 0.3, ..., 0.9, 1), compare the adjacency 4. 5. 6. matrices between the network and the ground truth Compute a confusion matrix for each threshold Compute TPR and FPR for each threshold Make a ROC plot. E.g., see Fig. 2 [L] 0.0000000000 1.731708e-01 1.054191e-04 4.215201e-04 3.615903e-01 6.62842se-01 0.3238827683 6.998978e04 1.313074e 01 4.178738e-01 [2,10.17317083980.000000e 00 2 512879e-04 7.774737e-07 5.903489e-01 2.824813e-01 0.5282463659 1.042277e-03 6.778354-01 6.582586e-01 3,10.00010541912612879e-04 0.c00000e+0 2.448500e-04 3.C01712e-04 2.833031e-050.0001954879 3.948046-04 2.132722e-04 1453577e-05 [6,10.6628425202 2824813e-01 2.833031e-05 7.924904e-04 5.001508e-01 0000000000.5010528062 2.813857e-04 1.9405-01 6.554619e-01 [7.103238827683 5.282464e-01 1.954879e-04 2.505381e-04 5 220584e-01 5.010528e-01 0.0000c00000 2.508563e-04 3.773563e-01 5,754074e-01 441785e-03 0.0006998978 1.0422 77e-03 3.948016e 30.000000e+ 00 3.550426e-0