SureTypeSCR

SureTypeSCR performs quality control and rapid genotyping of single-cell SNP array data to reduce errors introduced by whole genome amplification (WGA).

Key Features:

• R package implementation: Implemented in R for analysis of single-cell SNP array data.
• SureTypeSC integration: Builds upon the SureTypeSC algorithm developed to address WGA-related challenges.
• Two-layered machine learning: Uses a two-layered machine learning approach to evaluate genotype quality.
• SNP-level quality scoring: Assigns quality scores to individual SNPs within an array.
• WGA noise reduction: Reduces noise and minimizes genotyping errors introduced by whole genome amplification (WGA).
• QC workflows with tidyverse: Implements quality-control strategies and data manipulation routines using tidyverse packages in R.
• Bulk genotyping compatibility: Workflow can be adapted for standard genotyping of bulk DNA and batch processing.

Scientific Applications:

• Single-cell genotyping: Quality control and genotyping of single-cell SNP arrays affected by WGA artifacts.
• Bulk DNA genotyping: Adaptation of the workflow for standard genotyping of bulk DNA samples.
• Batch processing in genomic studies: Facilitates batch processing of SNP array datasets for genomic research.

Methodology:

Implements the SureTypeSC algorithm and a two-layered machine learning approach that assigns quality scores to individual SNPs and applies QC routines using tidyverse packages.