Power analysis and sample size estimation for RNA-Seq differential expression.
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Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream AnalysisRNA-Seq reveals 10 novel promising candidate genes affecting milk protein concentration in the Chinese Holstein population.Functional genomic analysis and neuroanatomical localization of miR-2954, a song-responsive sex-linked microRNA in the zebra finch.Differentially expressed genes from RNA-Seq and functional enrichment results are affected by the choice of single-end versus paired-end reads and stranded versus non-stranded protocolsrMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq dataEvaluation of methods for differential expression analysis on multi-group RNA-seq count data.Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancerTHE TRAINING OF NEXT GENERATION DATA SCIENTISTS IN BIOMEDICINERNA-seq mixology: designing realistic control experiments to compare protocols and analysis methodsGene dispersion is the key determinant of the read count bias in differential expression analysis of RNA-seq data.Comparative muscle transcriptome associated with carcass traits of Nellore cattle.RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice.Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experimentsOn the study of microbial transcriptomes using second- and third-generation sequencing technologies.Resolving host-pathogen interactions by dual RNA-seq.Power analysis for RNA-Seq differential expression studies.Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders.Prenatal Choline Supplementation Diminishes Early-Life Iron Deficiency-Induced Reprogramming of Molecular Networks Associated with Behavioral Abnormalities in the Adult Rat Hippocampus.Transcriptomic evidence for modulation of host inflammatory responses during febrile Plasmodium falciparum malaria.Dysregulation of long non-coding RNA profiles in human colorectal cancer and its association with overall survivalRNA-Seq analysis on chicken taste sensory organs: An ideal system to study organogenesis.The power and promise of RNA-seq in ecology and evolution.The Impact of Microenvironment on the Synovial Sarcoma Transcriptome.Power and sample size calculations for high-throughput sequencing-based experiments.Pan-Cancer Analyses Reveal Long Intergenic Non-Coding RNAs Relevant to Tumor Diagnosis, Subtyping and Prognosis.Feasibility of sample size calculation for RNA-seq studies.Promises and Challenges of Eco-Physiological Genomics in the Field: Tests of Drought Responses in Switchgrass.Tissue dual RNA-seq allows fast discovery of infection-specific functions and riboregulators shaping host-pathogen transcriptomes.Direct and indirect consequences on gene expression of a thyroid hormone receptor alpha 1 mutation restricted to Sertoli cells.Transcriptional signature of lymphoblastoid cell lines of BRCA1, BRCA2 and non-BRCA1/2 high risk breast cancer families.The draft genome of tropical fruit durian (Durio zibethinus).A broken promise: microbiome differential abundance methods do not control the false discovery rate.Peri-implant and periodontal microbiome diversity in aggressive periodontitis patients: a pilot study.Subcellular analysis of pigeon hair cells implicates vesicular trafficking in cuticulosome formation and maintenance.Thinking BIG rheumatology: how to make functional genomics data work for you.Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size.Maternal stress has divergent effects on gene expression patterns in the brains of male and female threespine stickleback.Metatranscriptome of the Oral Microbiome in Health and Disease.Coding and Non-coding RNAs, the Frontier Has Never Been So Blurred.Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results.
P2860
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P2860
Power analysis and sample size estimation for RNA-Seq differential expression.
description
2014 nî lūn-bûn
@nan
2014 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Power analysis and sample size estimation for RNA-Seq differential expression.
@ast
Power analysis and sample size estimation for RNA-Seq differential expression.
@en
Power analysis and sample size estimation for RNA-Seq differential expression.
@nl
type
label
Power analysis and sample size estimation for RNA-Seq differential expression.
@ast
Power analysis and sample size estimation for RNA-Seq differential expression.
@en
Power analysis and sample size estimation for RNA-Seq differential expression.
@nl
prefLabel
Power analysis and sample size estimation for RNA-Seq differential expression.
@ast
Power analysis and sample size estimation for RNA-Seq differential expression.
@en
Power analysis and sample size estimation for RNA-Seq differential expression.
@nl
P2093
P2860
P356
P1433
P1476
Power analysis and sample size estimation for RNA-Seq differential expression.
@en
P2093
Lana X Garmire
Sijia Huang
Travers Ching
P2860
P304
P356
10.1261/RNA.046011.114
P407
P577
2014-09-22T00:00:00Z