Immune_breast_carcinoma

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:

  1. Technical QC of the samples [1]
  2. Selection of housekeeping genes for normalization [1]
  3. Normalization using RUVSEq method [1, 2, 3, 4]
  4. Assessment of normalization
  5. Differential expression analysis with DESeq2 [4]
  6. Overrepresentation analysis for significant genes
  7. Volcano plots for the differential expression results
  8. Unsupervised clustering of the gene expression matrix
  9. Overrepresentation analysis for gene clusters
  10. 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.