Papers: 10.1155/2013/203681

https://doi.org/10.1155/2013/203681

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

Cited by: 23
Author(s): Sangwon Oh, Seongho Song, Gregory A. Grabowski, Hongyu Zhao, James P. Noonan
Published: over 13 years ago

Software Mentions 6

bioconductor: baySeq
Empirical Bayesian analysis of patterns of differential expression in count data
Papers that mentioned: 139
Very Likely Science (100)
bioconductor: DEGseq
Identify Differentially Expressed Genes from RNA-seq data
Papers that mentioned: 697
Very Likely Science (100)
bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in R
Papers that mentioned: 6,568
Very Likely Science (100)
bioconductor: limma
Linear Models for Microarray Data
Papers that mentioned: 7,776
Very Likely Science (78)
cran: ebdbNet
Empirical Bayes Estimation of Dynamic Bayesian Networks
Papers that mentioned: 5
Very Likely Science (100)
cran: GeneNet
Modeling and Inferring Gene Networks
Papers that mentioned: 87
Very Likely Science (90)