Andrew Erich Teschendorff

PLASTIC quantifies cellular plasticity and epigenetic reprogramming in preneoplastic lesions, revealing dedifferentiation as a key factor in cancer risk.

This paper introduces EpiDiff, a DNA methylation-based tool to predict cell differentiation states, revealing age-related epigenetic drift in mesenchymal lineages and cancer risk associations.

OmniAge compiles 413 aging biomarkers across 12 categories and reveals associations between mitotic clocks, clonal hematopoiesis, and cancer risk using blood datasets.

LDscore provides a cloud-based, Python 3 web platform for LDSC analysis, improving accessibility and scalability for heritability and genetic correlation studies.

The paper creates a DNA methylation aging atlas across 17 human tissues, identifying conserved and tissue-specific aging signatures and potential therapeutic targets.

The paper develops a DNAm reference panel called UniLIFE to estimate immune cell fractions in blood tissue across the lifespan, identifying biomarkers for type-1 diabetes in infants and children.

The paper shows that an inflammation clock fails to capture inflammaging by not accounting for cell-type shifts, and is outperformed by a cell-type deconvolution method.

This paper introduces a method to improve cancer prognosis by analyzing cell-type specific epigenetic alterations, finding better prognostic models in liver and kidney cancer.

The paper develops cell-type specific DNA methylation clocks to estimate biological age at cell-type resolution and finds they show age acceleration in disease states.

This study integrates multi-OMIC data from 12 cohorts to identify exercise-specific molecular signatures and gene regulatory patterns in human skeletal muscle.

FORGEdb integrates genomic annotations to prioritize functional variants and target genes for complex diseases.

This study identifies 48% differentially methylated positions and 37% variably methylated positions in blood methylomes across aging, revealing their roles in entropy and epigenetic clocks.

This paper uses DNA methylation data to analyze immune cell fractions across 25,629 blood samples, identifying associations with age, sex, health outcomes, and lifestyle factors.

ELVAR improves differential abundance testing in single-cell RNA-Seq by using cell attribute aware clustering to detect DA shifts in aging and disease.

dbDEMC 3.0 updates a database of differentially expressed miRNAs in human and model organism cancers, expanding data entries and integrating functional annotations.

The paper critiques a recent commentary on DNA methylation analysis, highlighting misconceptions in algorithm evaluation and reporting contradictory results on real EWAS datasets.

The paper introduces SCIRA, a method to detect tumor suppressor events in single-cell RNA-Seq data by inferring regulatory activity of tissue-specific transcription factors, improving sensitivity over differential expression.

The paper evaluates existing statistical algorithms for identifying cell-type-specific DNA methylation associations in EWAS and highlights biases in prior comparative studies.

The paper introduces LandSCENT, a computational method to identify bipotent-like cells in mammary epithelium, which are associated with breast cancer risk.

ebayGSEA improves Gene Set Enrichment Analysis for Epigenome-Wide-Association Studies by ranking genes based on differential methylation, enhancing sensitivity and avoiding probe representation bias.

The paper analyzes DNA methylation changes across multiple tissues and cell types to show that a substantial portion of age-related methylation changes is shared between different cell/tissue types, with some sites showing age-related alterations in over 10 different types.

This paper introduces CellDMC, a statistical algorithm that identifies differentially methylated cell-types in EWAS, achieving high sensitivity and specificity in detecting methylation changes in smoking-related lung cancer.

The paper compares single-cell potency measures using 9 experiments and 6,600 RNA-Seq profiles, finding that graph entropy of a Markov Chain on a signaling network better estimates potency than other methods.

This paper introduces tICA, a tensorial independent component analysis algorithm, which outperforms existing methods in decomposing multi-omic data with reduced computational cost and improved biological insight.

The paper develops classifiers using miRNA and DNA methylation data to predict tumor origin with high accuracy and provides a webserver for clinical use.

This paper introduces SCENT, a method to estimate single-cell differentiation potency using transcriptomic entropy, validated across 7,000 single-cell RNA-Seq profiles.

This paper critiques a study claiming ReFACTor improves EWAS by correcting cell-type heterogeneity, finding its claims lack rigorous statistical support.

This paper compares reference-based deconvolution algorithms for correcting cell-type heterogeneity in EWAS, finding that non-constrained methods like robust partial correlations outperform constrained approaches.