Get test data

test_dat.7z, Download one of the data sets below:

Module 1: RNA-seq processing

mkdir s1
cd s1
baseDir=/your/path/to/PipeOne/
nextflow run ${baseDir}/s1_RNAseq.nf  -resume -profile docker \
        --genome hg38 \
        --reads "../test_dat/s1_RNA-seq/*_{1,2}.fq.gz"

Results: Tables for different aspects of RNA-seq:

$ ls -1 results/tables 
APA_pau-distal-proximal.csv
circRNA_CPM.csv
lncR_gene.tpm.csv
merge_graphs_alt_3prime_C3.confirmed.psi.csv
merge_graphs_alt_5prime_C3.confirmed.psi.csv
merge_graphs_exon_skip_C3.confirmed.psi.csv
merge_graphs_intron_retention_C3.confirmed.psi.csv
merge_graphs_mult_exon_skip_C3.confirmed.psi.csv
merge_graphs_mutex_exons_C3.confirmed.psi.csv
prot_gene.tpm.csv
retro-FPKM-divide_totalMapReads.csv
snp.geneBase.csv

Module 2: Feature Prioritization

We use these downloaded test tables as input data. Note: In real applications, the result tables of the previous step should be used as input

mkdir s2
cd s2
nextflow run ${baseDir}/s2_RF.nf -profile docker \
    --rawdir ../test_dat/s2_tables/00_rawdata  \
    --sample_info ../test_dat/s2_tables/s1_sample_info-tumor-normal.csv \
    --gene_info ../test_dat/s2_tables/protein_coding_and_all_lncRNA.info.tsv

Results:

results/data/feature_importance.csv

results/data/feature_importance-addName.csv

results/data/discriminative_power_of_topk_feature.csv

Module 3: Subtype Analysis

mkdir s3
cd s3
nextflow run ${baseDir}/s3_Subtype.nf -profile docker \
    --rawdir   ../test_dat/s3_subtype/00_rawdata/ \
    --clinical ../test_dat/s3_subtype/KIRP_cli.OS.csv \
    --test

Note: The parameter --test is use for test running only, should not be use in real execution.

Results: results/record_log_rank_test_pvalue.csv

results/FeatureSelection

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