My list of publications and resources to read through
- Andreatta and Carmona et al 2021 UCell: Robust and scalable single-cell gene signature scoring
- Rich et al 2024 The impact of package selection and versioning on single-cell RNA-seq analysis
- Cuomo et al 2021 Optimizing expression quantitative trait locus mapping workflows for single-cell studies
- Luecken and Theis 2019 Current best practices in single-cell RNA-seq analysis: a tutorial
- Squair et al 2021 Confronting false discoveries in single-cell differential expression
- Zimmerman, Espeland & Langefeld 2021 A practical solution to pseudoreplication bias in single-cell studies
- Becht et al 2019 [Dimensionality reduction for visualizing single-cell data using UMAP(https://www.nature.com/articles/nbt.4314)
- Traag, Waltman & van Eck 2019 From Louvain to Leiden: guaranteeing well-connected communities
- Antonsson and Melsted 2024 Batch correction methods used in single cell RNA-sequencing analyses are often poorly calibrated
- Ma et al 2023 Principled and interpretable alignability testing and integration of single-cell data
- Chari and Pachter 2023 The specious art of single-cell genomics
- Parab and Bhalerao 2010 Choosing statistical test
- Finak et al 2015 MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
- Dong et al 2021 powerEQTL: an R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis
- Thiese, Ronna, and Ott 2016 P value interpretations and considerations
- Serdar et al 2020 Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies
- Szumilas 2020 Explaining Odds Ratios
- Burgess et al 2023 Guidelines for performing Mendelian randomization investigations: update for summer 2023
- Hartwig et al 2016 Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique
- Davies et al 2017 Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians
- Bland 2000 The odds ratio
- Li et al 2014 MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens
- Spahn et al 2017 PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens
- Goodspeed et al 2019 A Whole-genome CRISPR Screen Identifies a Role of MSH2 in Cisplatin-mediated Cell Death in Muscle-invasive Bladder Cancer
- Smith and Sheffield 2020 Analytic Approaches for ATAC-seq Data Analysis
- Yan, Powell, Curtis, and Wong, 2020 From reads to insight: a hitchhiker's guide to ATAC-seq data analysis
- Grandi, Modi, Kampman and Corces 2022 Chromatin accessibility profiling by ATAC-seq
- Reske, Wilson, and Chandler 2020 ATAC-seq normalization method can significantly affect differential accessibility analysis and interpretation
- Klemm, Shipony, and Greenleaf 2019 Chromatin accessibility and the regulatory epigenome
- Tarbell and Liu 2019 HMMRATAC: a Hidden Markov ModeleR for ATAC-seq
- Fang et al 2021 Comprehensive analysis of single cell ATAC-seq data with SnapATAC
- Granja et al 2021 [ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis](ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis)
- Molder et al 2021 Sustainable data analysis with Snakemake
- Amemiya, Kundaje, and Boyle 2019 The ENCODE Blacklist: Identification of Problematic Regions of the Genome