article Open AccessTop 1% cited
Linear models enable powerful differential activity analysis in massively parallel reporter assays
BMC Genomics · 2019 · Vol. 20(1) · pp. 209–209
Leslie Myint✉(Macalester College)Dimitrios Avramopoulos(Johns Hopkins University)Loyal A. Goff(Johns Hopkins University)Kasper D. Hansen(Johns Hopkins University)
Abstract
Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments.
Gene expression and cancer classificationAdvanced Biosensing Techniques and ApplicationsBioinformatics and Genomic NetworksAutomatic summarizationBioconductorPower analysisComputer scienceStatistical powerBarcodeMassively parallelData miningBiologyComputational biology
MeSH terms
HumansSoftwareGenome, HumanLinear ModelsSequence Analysis, DNASequence Analysis, RNAHigh-Throughput Nucleotide Sequencing
Funding
- National Institutes of Health
- National Cancer Institute
- National Institute of General Medical Sciences
Citations
463
FWCI
19.87
field-weighted impact
References
34
Percentile
100%
vs. same field & year
Citations per year
References
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Genome biology · 2014 · 6,605 citations
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
BMC Bioinformatics · 2010 · 1,770 citations
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Genome biology · 2014 · 97,164 citations
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