Bioinformatics Dictionary

• Definition: Computational methods used to separate mixed biological signals from heterogeneous samples into their constituent components.

• Applications:
- Cell type composition estimation from bulk tissue transcriptomics data
- Tumor microenvironment characterization from mixed tumor samples
- Immune cell profiling from complex tissue samples
- Epigenetic signal deconvolution from mixed cell populations

• Key methodologies:
- Reference-based deconvolution: Uses known cell type-specific signatures as reference
- Reference-free deconvolution: Identifies cell types without prior knowledge using statistical approaches
- Semi-supervised approaches: Combines reference data with unsupervised learning
- Spatial deconvolution: Incorporates spatial information to resolve cellular heterogeneity

• Algorithms and tools:
- CIBERSORT: Estimating immune cell fractions from gene expression profiles
- CellMix: R package for linear unmixing of heterogeneous tissue samples
- MuSiC: Multi-subject single cell deconvolution
- DSA (Digital Sorting Algorithm): Marker-free deconvolution for transcriptomics

• Challenges and considerations:
- Reference dataset quality and comprehensiveness
- Assumption of linear mixing in most algorithms
- Handling of technical and biological noise
- Validation of deconvolution results with orthogonal methods

• Definition: Identification of genetic variations present in only a subset of cells within an individual, resulting from post-zygotic mutations during development.

• Types of mosaicism:
- Somatic mosaicism: Mutations present in somatic cells but not germline
- Gonadal mosaicism: Mutations present in germ cells that can be transmitted to offspring
- Chromosomal mosaicism: Presence of cells with different chromosomal compositions
- Mitochondrial heteroplasmy: Varying proportions of mutant mitochondrial DNA

• Detection methods:
- Deep sequencing: High-depth targeted sequencing to detect low-frequency variants
- Single-cell sequencing: Analyzing genetic material from individual cells
- Digital PCR: Highly sensitive detection of rare variants
- SNP arrays: Detection of mosaic copy number variations and loss of heterozygosity

• Bioinformatic challenges:
- Distinguishing true mosaic variants from sequencing errors
- Determining variant allele frequency thresholds
- Computational efficiency for large-scale analyses
- Integration of multiple data types for comprehensive detection

• Clinical significance:
- Cancer: Tumor heterogeneity and clonal evolution
- Developmental disorders: Explaining variable phenotypes
- Aging: Accumulation of somatic mutations throughout life
- Precision medicine: Tailoring treatments based on subclonal genetic profiles