12,538 research outputs found
Learning the optimal scale for GWAS through hierarchical SNP aggregation
Motivation: Genome-Wide Association Studies (GWAS) seek to identify causal
genomic variants associated with rare human diseases. The classical statistical
approach for detecting these variants is based on univariate hypothesis
testing, with healthy individuals being tested against affected individuals at
each locus. Given that an individual's genotype is characterized by up to one
million SNPs, this approach lacks precision, since it may yield a large number
of false positives that can lead to erroneous conclusions about genetic
associations with the disease. One way to improve the detection of true genetic
associations is to reduce the number of hypotheses to be tested by grouping
SNPs. Results: We propose a dimension-reduction approach which can be applied
in the context of GWAS by making use of the haplotype structure of the human
genome. We compare our method with standard univariate and multivariate
approaches on both synthetic and real GWAS data, and we show that reducing the
dimension of the predictor matrix by aggregating SNPs gives a greater precision
in the detection of associations between the phenotype and genomic regions
Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data
We present the extention and application of a new unsupervised statistical
learning technique--the Partition Decoupling Method--to gene expression data.
Because it has the ability to reveal non-linear and non-convex geometries
present in the data, the PDM is an improvement over typical gene expression
analysis algorithms, permitting a multi-gene analysis that can reveal
phenotypic differences even when the individual genes do not exhibit
differential expression. Here, we apply the PDM to publicly-available gene
expression data sets, and demonstrate that we are able to identify cell types
and treatments with higher accuracy than is obtained through other approaches.
By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may
be used to find sets of mechanistically-related genes that discriminate
phenotypes.Comment: Revise
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Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.
Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity
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