Difference between revisions of "Gene expression prediction"

https://github.com/SchulzLab/TEPIC/tree/master/MachineLearningPipelines/INVOKE

Input:

• TF-genes scores (calculated by TEPIC)
  • open chromatin data (DNaseI-seq, NOMe-seq)
  • PWM (Jaspar, HOCOMOCO, Uniprobe)
• expression data (RNA-seq)

Output:

• regression coefficients for TF
• model performance: Pearson correlation, Spearman correlation, and MSE
  • boxplot showing model performance
  • heatmap (top 10 positive and negative coefficients)
  • scatter plots for predicted versus the measured gene expression data

INVOKE offers linear regression with various regularisation techniques (Lasso, Ridge, Elastic net) to infer potentially important transcriptional regulators by predicting gene expression from TEPIC TF-gene scores.