RNA-seq co-expression

Related software

1. coseq: Co-expression analysis of sequencing data 🔗
2. ICAL: Model selection for model based clustering of annotated data 🔗
3. HTSCluster: Clustering high-throughput sequencing data with Poisson mixture models 🔗

Related publications

1. Godichon-Baggioni, A., Maugis-Rabusseau, C. and Rau, A. (2018) Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. Journal of Applied Statistics, 46(1):47-65. 🔗 📄 🔄 💻

2. Rau, A. and Maugis-Rabusseau, C. (2018) Transformation and model choice for RNA-seq co-expression analysis. Briefings in Bioinformatics, bbw128. 🔗 📄 🔄 💻

3. Mondet, F., Rau, A., Klopp, C., Rohmer, M. Severac, D., Le Conte, Y., and Alaux, C. (2018) Transcriptome profiling of the honeybee parasite Varroa destructor provides new biological insights into the mite adult life cycle. BMC Genomics, 19:328. 🔗 📄

4. Sauvage, C., Rau, A., Aichholz, C., Chadoeuf, J., Sarah, G., Ruiz, M., Santoni, S., Causse, M., David, J., Glémin, S. (2017) Domestication rewired gene expression and nucleotide diversity patterns in tomato. The Plant Journal, 91(4):631-645. 🔗 📄

5. Gallopin, M., Celeux, G., Jaffrézic, F., Rau, A. (2015) A model selection criterion for model-based clustering of annotated gene expression data. Statistical Applications in Genetics and Molecular Biology, 14(5): 413-428. 🔗 📄 🔢

6. Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M.-L., Celeux, G. (2015) Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31(9): 1420-1427. 🔗 📄 🔄 💻