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Using singscore to predict mutations in AML from transcriptomic signatures
Below are video tutorials for this GTN material, created for various (past) events.
Tutorial Video ()
Advances in RNA sequencing (RNA-seq) technologies that measure the
transcriptome of biological samples have revolutionised our ability to
understand transcriptional regulatory programs that underpin diseases
such as cancer. We recently published singscore - a single-sample, rank-based
gene set scoring method which quantifies how concordant the transcriptional
profile of individual samples are relative to specific gene sets of interest.
Here we demonstrate the application of singscore to investigate
transcriptional profiles associated with specific mutations or genetic lesions
in acute myeloid leukemia. Using matched genomic and transcriptomic data
available through The Cancer Genome Atlas we show that scoring of appropriate
signatures can distinguish samples with corresponding mutations, reflecting
the ability of these mutations to drive aberrant transcriptional programs
involved in leukemogenesis. We believe the singscore method is particularly
useful for studying heterogeneity within specific subsets of cancers, and as
demonstrated, singscore has the ability to identify samples where alternative
mutations/genetic lesions appear to drive transcriptional programs.
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