Package: MultiSEp 4.1.3

1. Introduction | Citing MultiSEp | 2. Quickstart | Predicting Synthetic Lethality (SL) and other gene dependencies with mts_omics() | Predicting SL with CRISPR and gene expression (SynLeGG) | Induced dependency prediction with CRISPR and gene expression (SynLeGG) | Predicting SL with mutation and gene expression (SynLeGG) | 3. Prediction of Gene Dependency Relationships (GDRs) with Functional Genomics Data | Data partitioning to map gene function states | Unsupervised clustering to reveal gene states for continuous data | Threshold-based partitioning of continuous data | Partitioning categorical data | Analysing canonical dependency patterns for gene pairs | Mapping GDRs with sample enrichment: CRISPR data | Tissue-specific GDR detection | Predicting tissue-specific GDRs with transcriptome data | Predicting tissue-specific GDRs with transcriptome and CRISPR data | Tissue-specific GDR detection (SynLeGG) | Induced dependency prediction (t-test) | Mutation and expression GDRs (SynLeGG) | Analysis of mutational neighbourhoods in SL networks for therapeutic target prioritisation | 4. Running MultiSEp in a High-Performance Computing (HPC) Environment for Evaluation of Context-specific SL at Genome Scale | Step 1: Produce gene expression clusters | Step 2: Preparing the clustered genes for parallel processing | Step 3: Predicting SL with mts_omics() | Step 4: Identifying deleterious mutations to inform drug discovery | Step 5: Predicting the population coverage achieved by inhibition of a candidate target gene | Visualisation of predicted SL network for Multiple Myeloma | 5. References