Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients.
Petr V NazarovAnke K Wienecke-BaldacchinoAndrei ZinovyevUrszula CzerwińskaArnaud MullerDorothée NashanGunnar DittmarFrancisco AzuajeStephanie KreisPublished in: BMC medical genomics (2019)
We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.
Keyphrases
- single cell
- end stage renal disease
- case report
- rna seq
- newly diagnosed
- ejection fraction
- chronic kidney disease
- papillary thyroid
- peritoneal dialysis
- small molecule
- prognostic factors
- high throughput
- big data
- patient reported outcomes
- stem cells
- squamous cell carcinoma
- machine learning
- mesenchymal stem cells
- free survival
- lymph node metastasis
- artificial intelligence
- bone marrow