Gene expression prediction

Method, code, references	Input data	Algorithm	Accuracy	Comment
INVOKE (R script)[1] 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.	HepG2 - r=0.68, K562 - r=0.68, GM12878 - r =0.58
PECA - paired expression and chromatin accessibility (MATLAB)[2] http://web.stanford.edu/~zduren/PECA/
2009 - an approach based on feature extraction of ChIP-Seq signals, principal component analysis, and regression-based component selection [3]	Input: ChIP-seq data expression data (RNA-seq) Output: log-linear regression model principal components with weights of corresponding TFs	for each TF, each gene - compute a TF association strength (TFAS) - the weighted sum of the corresponding ChIP-Seq signal strength, where the weights reflect the proximity of the signal to the gene. principal component analysis (PCA) to extract uncorrelated characteristic patterns in the TFAS vectors. centered and standardized the TFAS matrix A is decomposed by the singular value decomposition (SVD) regression-based component selection gene expression is expressed by the log-linear regression model	mouse ESCs, r=0.806, R2=0.65, CV-R2=0.64

References

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