The PredictioR Nextflow pipeline is designed to analyze immunotherapy responses and identify biomarkers across various cancers. It utilizes Nextflow for workflow management and Docker for reproducibility, focusing on handling SummarizedExperiment objects for in-depth biomarker analysis.
The main.nf
script integrates three key analysis steps:
- Gene Level Analysis
- Signature Level Analysis
- Meta Analysis
- Version: 24.04.2
- Installation and Resources:
- Setup Instructions: For detailed installation steps, please refer to the Nextflow Setup Guide.
- Documentation and Resources:
- Purpose: Ensures computational reproducibility by containerizing the environment.
- Installation Guide: Install Docker
- PredictioR Docker Image:
docker pull bhklab/nextflow-env
- Docker Hub: bhklab/nextflow-env
- GitHub Repository: PredictioR GitHub Repository
- Key Publication: PubMed Reference, PMID: 36055464
- Input Data Directory:
params.gene_data_dir = './ICB_data'
- Example Data Files: Includes files such as
ICB_small_Hugo.rda
,ICB_small_Mariathasan.rda
, which are SummarizedExperiment objects. These files are located within theICB_data
directory at the bhklab PredictioR data repository.- Output Data Directory:
params.out_dir = './output/main_output'
- Output Details: The results of the Gene Level Analysis are stored in the
main_output
directory, stratified by their study ID for clarity and ease of reference.
-
Input Data Directory:
params.signature_data_dir = './SIG_data'
-
Example Data Files: Files like
CYT_Rooney.rda
,EMT_Thompson.rda
,PredictIO_Bareche.rda
are data frames with columns like:signature_name
: Name of the signaturegene_name
: Name of the geneweight
: Weight assigned to each gene within the signature
To see other columns, these files are also sourced from the bhklab SignatureSets GitHub repository. The
.rda
files are stored in the objectsig
as data frames. Please follow the same format for consistency. -
Output Data Directory:
params.out_dir = './output/main_output'
-
Output Details: The results of the Signature Level Analysis are stored in the
main_output
directory, stratified by their study ID for clarity and ease of reference.
- Input Data Directory:
- The meta-analysis step uses the results from both gene-level and signature-level analyses.
- Input Directories:
- Gene level:
./output/main_output
- Signature level:
./output/main_output
- Gene level:
- Output Data Directory:
params.out_dir = './output/main_output'
-
This table summarizes each dataset by study and treatment type, along with cancer types, clinical and molecular data availability, and relevant PMID references. Required columns include 'treatment' and 'cancer type'.
Dataset Patients [#] Cancer type Treatment Clinical endpoints Molecular data PMID ICB_small_Hugo 27 Melanoma PD-1/PD-L1 OS RNA 26997480 ICB_small_Liu 121 Melanoma PD-1/PD-L1 PFS/OS RNA/DNA 31792460 ICB_small_Miao 33 Kidney PD-1/PD-L1 PFS/OS RNA/DNA 29301960 ICB_small_Nathanson 24 Melanoma CTLA4 OS RNA/DNA 27956380 ICB_small_Padron 45 Pancreas PD-1/PD-L1 PFS/OS RNA 35662283 ICB_small_Riaz 46 Melanoma PD-1/PD-L1 OS RNA/DNA 29033130 ICB_small_Van_Allen 42 Melanoma CTLA4 PFS/OS RNA/DNA 26359337 ICB_small_Mariathasan 195 Bladder PD-1/PD-L1 OS RNA/DNA 29443960 Ensure that clinical data is properly organized with all required and additional fields to ensure the integrity of the analysis.
-
Required Columns:
patientid
: Unique identifier for patientstreatmentid
: Details of the treatment regimenresponse
: Patient response to treatment (Responder 'R', Non-responder 'NR')tissueid
: Standardized cancer typesurvival_time_pfs
: Time to progression-free survival, Example: 2.6 monthssurvival_time_os
: Time to overall survivalsurvival_unit
: Measurement units for survival times, typically monthsevent_occurred_pfs
: Binary indicator of event occurrence during PFS (1,0)event_occurred_os
: Binary indicator of event occurrence during OS (1,0)
-
Additional Recommended Fields: Include sex, age, histo (histological type), stage of cancer, dna, and rna details among others as necessary.
-
-
This table summarizes each signature name by study and PMID references, the method for computing the signature score, and the corresponding score function.
Signature DNA/RNA RNA Type Method Cancer Type Score Function PMID ADO_Sidders RNA Count RNA-seq/TPM GSVA Multiple geneSigGSVA 31953314 APM_Thompson RNA log CPM GSVA Lung, melanoma geneSigGSVA 33028693 APM_Wang RNA Microarray GSVA Multiple geneSigGSVA 31767055 Bcell_Budczies RNA Microarray GSVA Lung geneSigGSVA 33520406 Bcell_Helmink RNA log FPKM GSVA Melanoma, kidney geneSigGSVA 31942075 Blood_Friedlander RNA Microarray GSVA Melanoma geneSigGSVA 28807052 C-ECM_Chakravarthy RNA Normalized counts ssGSEA Multiple geneSigssGSEA 30410077 CCL5-CXCL9_Dangaj RNA GSVA Multiple geneSigGSVA 31185212 CD39-CD8Tcell_Chow RNA RNA-seq count GSVA Lung geneSigGSVA 36574773 Required Columns:
signature
: Name of the signature, same names located in './SIG_data'method
: Used for signature score calculationscore function
: Specifying the function that should be used in the R script
For detailed information on the signatures used in the pipeline, refer to the signature(there are more than 50) information CSV available at: Signature Information CSV.
Run the pipeline with the configured parameters using Nextflow:
nextflow run main.nf
- Necessary R packages and dependencies are installed as specified in
load_libraries.R
and included in the BHK Docker. - Customize the nextflow.config file to specify any additional parameters or configurations required for your specific analysis needs