The methods section starts with the following:
Downstream analysis and visualization were performed within the NIH Integrated Analysis Platform (NIDAP) using R programs developed by a team of NCI bioinformaticians on the Foundry platform (Palantir Technologies).
Followed by these lines. Please check specifics – default settings are listed in pink:
Briefly, RNA-Seq FASTQ files were aligned to the reference genome (GRCh38) using STAR (1) and raw counts data produced using RSEM (2). The gene counts matrix was imported into the NIDAP platform, where genes were filtered for low counts (<1 cpm) and normalized by quantile normalization using the limma package (3). Differentially expressed genes were calculated using limma-Voom (4).
Optional Steps:
- Batch removal was performed using ComBat (5)
- GSEA was performed using fgsea package (6)
- Pathway enrichment analysis was performed using Fisher’s Exact Test (7)
References:
- Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: Ultrafast Universal RNA-seq Aligner. Bioinformatics (Oxford, England), 29(1), 15–21.
- Li, B., & Dewey, C. N. (2011). RSEM: Accurate Transcript Quantification from RNA-Seq Data with or without a Reference Genome. BMC Bioinformatics, 12, 323.
- Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Research, 43(7), e47.
- Law, C. W., Alhamdoosh, M., Su, S., Smyth, G. K., & Ritchie, M. E. (2016). RNA-seq Analysis is Easy as 1-2-3 with limma, Glimma, and edgeR. F1000Research, 5, 1408.
- Leek, J. T., Johnson, W. E., Parker, H. S., Fertig, E. J., Jaffe, A. E., Storey, J. D., Zhang, Y., & Torres, L. C. (2019). sva: Surrogate Variable Analysis. R Package Version 3.30.1.
- Korotkevich, G., Sukhov, V., & Sergushichev, A. (2019). Fast Gene Set Enrichment Analysis. bioRxiv. doi:.
- L2P Package on GitHub: https://github.com/ccbr/l2p