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df_clinical.tsv: clinical dataframe in tab seperated text format.
Each row is a patient and each column a descriptor.
Details about descriptor are as follows:
* ID the unique ID of each patient/sample
* SEX, AGE demographics
* Mds type
* WHO 2016 classification
* Blood counts and Blast counts
* Cytogenetics
* IPSS-R risk category and score
* IPSS-M risk category and score
* NGS derived deletions, gains and regions copy-neutral loss of heterozygosity as a list of chr. arms (CNACS)
* Annotation of complex karyotype
* Status of chr.17 at the TP53 locus
* Treatment status regarding HMA, Lenalidomid, Chemo, HSCT
* Overall survival, Leukemia free surival, and AML transformation information
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df_mut.tsv: binary matrix of mutated genes. Patients in lines and genes in columns. The file is tab seperated. -
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df_cna.tsv: binary matrix of cytogenetics alterations. Patients in lines and alterations in columns. The file is tab seperated. -
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maf.tsv: mutation file.
Each row corresponds to a given mutation in a given patient. Those are curated likely oncogenic or oncogenic mutations.
Details about mutation fields are as follows:
* ID the unique ID of each patient/sample
* CHR START END REF ALT the genomic position and descriptor of the mutation
* GENE
* cDNA_CHANGE
* PROTEIN_CHANGE
* VT variant type (substitution, indels)
* EFFECT classification of mutation consequence, ie missense nonsense frameshift ...
* VAF DEPTH variant allele frequency and coverage depth