| Title |
MixHMM: Inferring Copy Number Variation and Allelic Imbalance Using SNP Arrays and Tumor Samples Mixed with Stromal Cells
|
|---|---|
| Published in |
PLOS ONE, June 2010
|
| DOI | 10.1371/journal.pone.0010909 |
| Pubmed ID | |
| Authors |
Zongzhi Liu, Ao Li, Vincent Schulz, Min Chen, David Tuck |
| Abstract |
Genotyping platforms such as single nucleotide polymorphism (SNP) arrays are powerful tools to study genomic aberrations in cancer samples. Allele specific information from SNP arrays provides valuable information for interpreting copy number variation (CNV) and allelic imbalance including loss-of-heterozygosity (LOH) beyond that obtained from the total DNA signal available from array comparative genomic hybridization (aCGH) platforms. Several algorithms based on hidden Markov models (HMMs) have been designed to detect copy number changes and copy-neutral LOH making use of the allele information on SNP arrays. However heterogeneity in clinical samples, due to stromal contamination and somatic alterations, complicates analysis and interpretation of these data. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 2% |
| Italy | 1 | 2% |
| Sweden | 1 | 2% |
| United Kingdom | 1 | 2% |
| Belgium | 1 | 2% |
| United States | 1 | 2% |
| Poland | 1 | 2% |
| Unknown | 49 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 38% |
| Student > Ph. D. Student | 13 | 23% |
| Professor > Associate Professor | 7 | 13% |
| Student > Doctoral Student | 5 | 9% |
| Student > Master | 4 | 7% |
| Other | 2 | 4% |
| Unknown | 4 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 31 | 55% |
| Biochemistry, Genetics and Molecular Biology | 7 | 13% |
| Computer Science | 6 | 11% |
| Medicine and Dentistry | 4 | 7% |
| Mathematics | 3 | 5% |
| Other | 2 | 4% |
| Unknown | 3 | 5% |