Matthew N. McCall

The paper benchmarks normalization and differential expression methods for miRNA-seq using a realistic dataset, finding RPM normalization and edgeR perform best.

PARiS identifies and removes technical isomiR errors in sRNA-seq data, improving miRNA-level differential expression analysis.

This study shows that the tumor microenvironment alters macrophage circadian rhythms, affecting tumorigenesis and macrophage function.

This study introduces NBSR, a method for differential expression analysis of miRNA-seq data that outperforms existing approaches on simulated and experimental datasets.

The paper introduces a hierarchical Bayesian model for Sholl analysis to better handle hierarchical data structures in microglia morphology studies.

The paper introduces Brooklyn plots to detect co-expression dysregulation in scRNA-seq data, identifying altered chromatin patterns in dilated cardiomyopathy.

This study identifies biological and technical sources of expression variation across 49 human tissues using GTEx data, revealing 522 variable transcript clusters with varying confidence in their explanations.

The paper performs deconvolution on GTEx coronary artery data using CIBERSORT to assess challenges and limitations in estimating tissue composition from gene expression data.

The paper curates a comprehensive reference of miRNA expression across 196 human primary cell types using public datasets and provides access through a UCSC Genome Browser track and R/Bioconductor package.

The paper integrates multi-omic data to identify cellular pathways associated with RSV severity in infants.

This study uses in vivo imaging to show that microglia rapidly self-renew locally in the adult visual cortex without requiring a progenitor cell or developmental maturation.

PCRedux analyzes qPCR amplification curves to calculate interpretable features for machine learning, achieving high sensitivity and specificity in classification.

The paper analyzes matrisome gene expression patterns across tissues, age, sex, and disease using GTEx and TCGA data, finding that transcriptomics can recapitulate proteomic patterns and identify tissue-specific and sex/age-related expression variations.

The paper introduces a Smooth Transition Autoregressive model to improve qPCR data analysis by addressing autocorrelation in residuals caused by sequential dependence in fluorescence measurements.

The paper integrates proteomic and genomic data from skeletal muscle myocytes to identify gene and protein expression differences between slow and fast-twitch fiber types.

The paper identifies basal contamination in GTEx data, showing that highly expressed tissue-specific genes contaminate samples, affecting eQTL assignments and analysis accuracy.

This study examines how tissue composition affects gene co-expression estimates and shows how deconvolution methods can improve co-expression analysis.

The paper identifies a lack of airway interferon activation as a marker of severe infant RSV infection and shows that blocking IFN signaling increases viral infection in vitro.

The paper introduces xMD-miRNA-seq to obtain in vivo miRNA expression from colon epithelial cells using FFPE tissues, showing cell-specific miRNA profiles comparable to other epithelial sources.

The paper introduces statistical methods to handle non-detects in qPCR data by modeling missing data mechanisms and using multiple imputation to reduce bias in gene expression estimation.

The paper establishes the landscape of human cell-specific microRNA expression using small RNA-seq data from diverse cell types and tissues, identifying novel microRNA loci and patterns correlated with super-enhancer activity.