Quick Start | Introduction | Data | Quality control | High level functions | Data Import | Assess input data quality | Apply quality control | Review of all quality control | Low level functions | Measure completeness | show_rownames is passed to pheatmap() through the ... parameter | Constraining a single variable | Merge variables | Imputing missing values | Encoding categorical data | Encoding ordinal data | Encoding genotype (SNP) data | Extract information from free text variables | Review quality control | value level modifications showing which exact missingness values | were removed | plot value level modifications | Semantic enrichment | Required inputs | Example data | High level functionality | Low level functionality | Convert edge table to ontology | Join ontology and data variable names | Compute meta-variable information | annotations are also considered a set. This isn't helpful for this visualisation | Therefore, the sets of non-meta-variables are removed below | Generate semantic aggregations | References

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