Powerful differential expression analysis incorporating network topology for next-generation sequencing data.
BACKGROUND
RNA-seq has become the technology of choice for interrogating the transcriptome. However, most methods for RNA-seq differential expression (DE) analysis do not utilize prior knowledge of biological networks to detect DE genes. With the increased availability and quality of biological network databases, methods that can utilize this prior knowledge are needed and will offer biologists with a viable, more powerful alternative when analyzing RNA-seq data.


RESULTS
We propose a three-state Markov Random Field (MRF) method that utilizes known biological pathways and interaction to improve sensitivity and specificity and therefore reducing false discovery rates (FDRs) when detecting differentially expressed genes from RNA-seq data. The method requires normalized count data (e.g. in Fragments or Reads Per Kilobase of transcript per Million mapped reads (FPKM/RPKM) format) as its input and it is implemented in an R package pathDESeq available from Github. Simulation studies demonstrate that our method outperforms the two-state MRF model for various sample sizes. Furthermore, for a comparable FDR, it has better sensitivity than DESeq, EBSeq, edgeR and NOISeq. The proposed method also picks more top Gene Ontology terms and KEGG pathways terms when applied to real dataset from colorectal cancer and hepatocellular carcinoma studies, respectively. Overall, these findings clearly highlight the power of our method relative to the existing methods that do not utilize prior knowledge of biological network.


METHODS
As an R package at https://github.com/MalathiSIDona/pathDESeq.
install_github("MalathiSIDona/pathDESeq",build_vignettes = TRUE). After installation, type vignette("pathDESeq") to access the vignette.


BACKGROUND
a.salim@latrobe.edu.au.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.
Malathi S I
MSI
Dona
Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.
Luke A
LA
Prendergast
Department of Biochemistry and Genetics, La Trobe Institute of Molecular Science, La Trobe University, Melbourne, VIC, Australia.
Suresh
S
Mathivanan
Department of Biochemistry and Genetics, La Trobe Institute of Molecular Science, La Trobe University, Melbourne, VIC, Australia.
Shivakumar
S
Keerthikumar
Department of Mathematics and Statistics, La Trobe University, Melbourne, VIC, Australia.
Agus
A
Salim
D006528:Carcinoma, Hepatocellular / Q000235:genetics / Q000378:metabolism; D015179:Colorectal Neoplasms / Q000235:genetics / Q000378:metabolism; D015972:Gene Expression Regulation, Neoplastic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008113:Liver Neoplasms / Q000235:genetics / Q000378:metabolism; D018401:Sample Size; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome