systematicBNR

Identification of immune/stromal cell quantitative trait loci linked to cancer risk

This package contains the pipeline and tools developed for the study:

Palomero L, Galván-Femenía I, de Cid R, Espín R, Barnes DR, CIMBA, et al. Antoniou AC, Lázaro C, Pujana MA. Immune Cell Associations with Cancer Risk. iScience. 2020;23(7):101296. DOI: 10.1016/j.isci.2020.101296 · PMID: 32622267

This pipeline was developed and is maintained by Luis Palomero and Roderic Espín (MA Pujana's lab, Catalan Institute of Oncology, IDIBELL).

Note: This package provides the regression/normalization pre-processing step (Best Normalize Regression) used prior to QTL mapping. The published paper's Methods section cites systematicQTL for the QTL mapping step itself (LOD scoring, peak identification). Together, systematicBNR (pre-processing) and systematicQTL (QTL mapping) form the full analysis pipeline for this study.

Dependencies

The pipeline includes:

• ConsensusTME (Jiménez-Sánchez et al., 2019) — github.com/cansysbio/ConsensusTME
• GSVA (single-sample GSEA; Hänzelmann et al., 2013) — 10.18129/B9.bioc.GSVA
• R/qtl2 (Broman et al., 2019) — github.com/rqtl/qtl2
• bestNormalize — github.com/petersonR/bestNormalize

Installation

install.packages(c("devtools", "curl"))
library(devtools)
install_github("pujana-lab/systematicBNR", ref = "master")

Pipeline

Before running the regression analysis, three steps should be completed:

1. Delete cases with empty data and covariates without variability
2. Apply bestNormalize regression to the output variable
3. Apply a Stepwise AIC pipeline to remove non-informative covariates

The main function output (systematic_regression) returns results as a 1-row data.frame object, ideally called in a loop across variables.

Citation

If you use this code, please cite the publication above.

Authors

Luis Palomero and Roderic Espín