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update config
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config.yaml

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---
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metasheet: metasheet.csv
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ref: ref.yaml
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#########Fixed and user-defined parameters################
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metasheet: metasheet.csv # Meta info
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ref: ref.yaml # Reference config
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assembly: hg38
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cancer_type: GBM
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cancer_type: GBM #TCGA cancer type abbreviations
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rseqc_ref: house_keeping #Option: 'house_keeping' or 'false'.
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#By default, a subset of housekeeping genes is used by RSeQC to assess alignment quality.
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#This reduces the amount of time needed to run RSeQC.
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mate: [1,2] #paired-end fastq format, we recommend naming paired-end reads with _1.fq.gz and _2.fq.gz
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#########User defined parameters needed to run the modules################
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#------- preprocess module--------#
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rseqc_ref: house_keeping #rseqc ref model
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mate: [1,2] #paired-end([1,2]) or single-end([0])
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#########Cohort level analysis parameters################
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design: Group # Condition on which to do comparsion (as set up in metasheet.csv)
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Treatment: R # Treatment use in DESeq2, corresponding to positive log fold change
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Control: NR # Control use in DESeq2, corresponding to negative log fold change
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batch: syb_batch # Options: 'false' or a column name from the metasheet.csv.
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# If set to a column name in the metasheet.csv, the column name will be used for batch effect analysis (limma).
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# It will also be used as a covariate for differential analysis (DESeq2) to account for batch effect.
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###deseq2
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batch: syn_batch #False or batch colname in metasheet
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design: Group #condition to do comparsion
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Treatment: R
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Control: NR
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pre_treated: false # Option: true or false.
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# If set to false, patients are treatment naive.
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# If set to true, patients have received some form of therapy prior to the current study.
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