Correlation Statistics for cDNA Microarray Image Analysis
Radhakrishnan Nagarajan, Meenakshi Upreti
Abstract
In this report, correlation of the pixels comprising a microarray spot is investigated. Subsequently, correlation statistics namely: Pearson correlation and Spearman rank correlation are used to segment the foreground and background intensity of microarray spots. The performance of correlation-based segmentation is compared to clustering-based (PAM, k-means) and seeded-region growing techniques (SPOT). It is shown that correlation-based segmentation is useful in flagging poorly hybridized spots, thus minimizes false-positives. The present study also raises the intriguing question of whether a change in correlation can be an indicator of differential gene expression.
Create a lesson
Related papers
Learning Interpretable Tumor Microenvironment Representations by Fitting Pan-Cancer Cell State-Niche Correlation
Xiao Xiao, Jiashu He, Shiyang Zhang et al.
Optimizing RNA yield using deep neural networks coupled to massively parallel screening
Dinghai Zheng, Justin Hong, Jun Wang et al.
A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1Ψ-Modified RNA 5' UTRs
Narges Zarnaghinaghsh, Ahmadreza Mofayezi, Byung-Jun Yoon
mLS-GKM: Efficient Multi-class Regulatory Sequence Classification with Gapped k-mer SVMs
Kieran Howard, Nathan Harmston
DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification
Jana T. Winterstein, Lukas Heinlein, Günter Raddatz et al.
Hepatitis C Virus Genotyping with a Transformer Neural Network
Ariella Aro, Taimá Furuyama, Marcelo R. S. Briones et al.