Chapter 1 Introduction

IOBRpy workflow

Figure 1.1: IOBRpy workflow

This tutorial presents a reproducible IOBRpy workflow that starts with public paired-end FASTQ files. The main runall command runs in STAR mode and produces quality-control files, alignments, a TPM matrix, signature scores, six default TME analyses, ligand-receptor scores, and TRUST4 results. A separate chapter covers BayesPrism, TME clustering, and HLA typing.

1.1 Output directories

The complete workflow creates:

results/PRJDB16684/
├── 01-qc/
├── 02-star/
├── 03-tpm/
├── 04-signatures/
├── 05-tme/
├── 06-LR_cal/
├── 07-TCRBCR/
├── 08-bayesprism/
├── 09-tme-cluster/
└── 10-hla/
Directory Contents
01-qc/ Cleaned FASTQ files and quality-control reports generated by fastp.
02-star/ STAR alignments, coordinate-sorted BAM files, gene-level read counts, and the merged count matrix.
03-tpm/ TPM converted from the count matrix and the result after log2 transformation.
04-signatures/ Sample-level scores for the included biological signatures.
05-tme/ Results from the six-method TME panel and their merged profile.
06-LR_cal/ Ligand-receptor activity scores calculated from the expression matrix.
07-TCRBCR/ TRUST4 T-cell and B-cell receptor repertoire outputs.
08-bayesprism/ BayesPrism immune deconvolution results.
09-tme-cluster/ Sample clusters derived from TME profiles.
10-hla/ Per-sample HLA typing results and the merged HLA table.

1.2 Example RNA-seq project

The example dataset comes from ENA study PRJDB16684.