This is the custom R code used for the NanoString data analysis in:
Szeitz, B.; Pipek, O.; Kulka, J.; Szundi, C.; Rusz, O.; Tőkés, T.; Szász, A.M.; Kovács, K.A.; Pesti, A.; Ben Arie, T.B.; Gángó, A.; Fülöp, Z.; Drágus, E.; Vári-Kakas, S.A.; Tőkés, A.M. Investigating the Prognostic Relevance of Tumor Immune Microenvironment and Immune Gene Assembly in Breast Carcinoma Subtypes. Cancers 2022, 14, 1942. https://doi.org/10.3390/cancers14081942
This repository consists of 2 scripts:
| Script name | Description |
|---|---|
| Customized_functions.R | This contains custom functions used in the data analysis script. |
| NanoString_Data_Analyis_Script.R | This contains the all data analysis steps. |
Data analysis steps:
- Technical QC of the samples [1]
- Selection of housekeeping genes for normalization [1]
- Normalization using RUVSEq method [1, 2, 3, 4]
- Assessment of normalization
- Differential expression analysis with DESeq2 [4]
- Overrepresentation analysis for significant genes
- Volcano plots for the differential expression results
- Unsupervised clustering of the gene expression matrix
- Overrepresentation analysis for gene clusters
- Export results
References:
[1] Bhattacharya A, Hamilton AM, Furberg H, Pietzak E, Purdue MP, Troester MA, Hoadley KA, Love MI: An approach for normalization and quality control for NanoString RNA expression data. Brief Bioinform 2021, 22(3).
[2] Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94.
[3] Risso D, Ngai J, Speed TP, Dudoit S: Normalization of RNA-seq data using factor analysis of control genes or samples. Nat Biotechnol 2014, 32(9):896-902.
[4] Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014, 15(12):550.