Noise reduction software tools | Genomic array data analysis
Array-based comparative genomic hybridization (aCGH) is a powerful tool to detect genomic imbalances in the human genome. The analysis of aCGH data sets has revealed the existence of a widespread technical artifact termed as 'waves', characterized by an undulating data profile along the chromosome.
Detects copy number variations (CNVs) with high resolution. PennCNV is an integrated hidden Markov model (HMM) method that incorporates the population allele frequency for each single nucleotide polymorphism (SNP) and the distance between adjacent SNPs. This application was developed specifically for data generated on the Illumina Infinium platform, but it can be extended to other similar SNP genotyping platforms.
Provides a practical solution to a problem that is recognized by many researchers working with CGH arrays. NoWaves removes the wave bias from tumor profiles, thereby allowing for more accurate breakpoint detection in those profiles. We show that our algorithm, based on ridge regression, has several advantages: (i) it is robust against error-in-variables due to measurement noise in the covariates; (ii) the ridge penalty stabilizes the regression model in the presence of collinearity between the covariates; (iii) it is robust against the presence of CNAs in tumors.