Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · q-bio/0511030

Correlation Statistics for cDNA Microarray Image Analysis

Radhakrishnan Nagarajan·Meenakshi Upreti
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

q-bio.GNq-bio.QM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Radhakrishnan Nagarajan, Meenakshi Upreti. 2005-11-16. Correlation Statistics for cDNA Microarray Image Analysis. https://arxiv.org/abs/q-bio/0511030

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Synchronized Optimal Transport Reconstructs Coupled Transcriptomic and Chromatin Dynamics

Single-cell RNA and ATAC data collected across developmental stages provide time-course snapshots of transcriptional and regulatory states. Since sequencing is destructive, trajectories between these snapshots must be inferred from independently sampled cell populations. Existing approaches typically infer continuous dynamics in a single modality or integrate RNA and ATAC in a shared representation. Here we present Cross-Omics Aligned Trajectory Inference (COATI), which extends unbalanced synchronized dynamical optimal transport to time-dependent cross-modal maps for reconstructing coupled trajectories in distinct RNA and ATAC state spaces. We evaluated COATI on synthetic benchmarks and four developmental datasets using held-out-stage reconstruction and lineage analyses. COATI improved reconstruction of cell populations at held-out stages, including recovery of chromatin-state heterogeneity within cell types, and better preserved developmental lineage relationships. The unbalanced formulation allows the inference of population mass changes, which were shown to associate with DNA-replication programs at early stages and lineage-maturation programs later in development. The coupled trajectories enabled analysis of lineage-specific transcriptional and chromatin changes and their temporal relationships during development. Lineage responses inferred by COATI agreed with independent CRISPR perturbation results, and the inferred regulatory patterns were consistent with independent epigenetic data.

q-bio.GN↗
arXiv2026

Genome-Factory: A Library for Tuning, Deploying, and Interpreting Genomic Foundation Models

We introduce Genome-Factory, the first integrated Python library for tuning, deploying, and interpreting genomic foundation models. Our core contribution is to simplify and unify the workflow for genomic model development: data collection, model tuning, inference, benchmarking, and interpretability. For data collection, Genome-Factory offers an automated pipeline to download genomic sequences and preprocess them. For model tuning, Genome-Factory supports both full and parameter-efficient fine-tuning across diverse genomic models. For inference, Genome-Factory enables both embedding extraction and DNA sequence generation. For benchmarking, we include two existing benchmarks and provide a flexible interface to incorporate additional benchmarks. For interpretability, Genome-Factory introduces an open-source biological interpreter based on a sparse auto-encoder. We validate the utility of Genome-Factory across three dimensions: (i) Compatibility with diverse models and fine-tuning methods; (ii) Benchmarking downstream performance using two open-source benchmarks; (iii) Biological interpretation of learned representations with DNABERT-2. These results highlight its practical value for real-world genomic analysis. GitHub: https://github.com/WeiminWu2000/Genome_Factory.

q-bio.GN↗
arXiv2026

One Method Does Not Fit All: A Large-Scale Benchmark of Imputation Methods for Single-Cell RNA Sequencing Data

Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at cellular resolution but is inherently affected by sparsity caused by dropout events, where expressed genes are recorded as zeros due to technical limitations. These dropout events distort gene expression distributions and compromise downstream analyses. To address this challenge, numerous traditional and deep learning (DL)-based imputation methods have been developed to detect dropout events and impute their true expression values. However, evaluations of these methods have largely been restricted to specific downstream tasks and limited datasets, and large-scale systematic comparisons under standardized benchmarking conditions are lacking. To fill this gap, we present a comprehensive framework that evaluates 16 imputation methods across 10 experimental protocols, 6 downstream tasks, and 30 benchmark datasets. Through extensive experimentation, we systematically investigate the strengths, limitations, and task-specific performance of these methods under consistent evaluation settings. Our results show that traditional methods perform relatively better across a wide range of downstream tasks, whereas DL-based methods are more variable. In addition, strong performance in numerical gene expression recovery does not necessarily translate into improved biological interpretability in downstream analyses, including cell clustering, differential expression analysis, marker gene analysis, trajectory analysis, and cell type annotation. Furthermore, performance varies substantially across datasets, protocols, and downstream analyses, with no single method consistently outperforming the others. Our findings provide practical guidance for selecting imputation methods tailored to specific analytical objectives and underscore the importance of task-specific evaluation in scRNA-seq data analysis.

q-bio.GN↗