33 research outputs found
Breast and ovarian cancer genotype specific risks for each tSNP by study
<p>1 odds ratio, 2 confidence interval, * compared with common homozygote. Confidence intervals that do not reach or cross 1.00 and P- values<0.05 are in bold type</p
Linkage disequilibrium between the 92 common variants (MAF>0.05) in HapMap CEPH trios.
<p>Each square represents the correlation (r<sup>2</sup>) between each pair of SNPs with darker shades representing stronger LD. Tag SNPs are indicated with those SNPs that failed assay design being shown in grey font.</p
Serous type ovarian cancer genotype specific risks for each tSNP
*<p>compared with common homozygote. Confidence intervals that do not reach or cross 1.00 and P- values<0.05 are in bold type</p
Single-Patient Molecular Testing with NanoString nCounter Data Using a Reference-Based Strategy for Batch Effect Correction
<div><p>A major weakness in many high-throughput genomic studies is the lack of consideration of a clinical environment where one patient at a time must be evaluated. We examined generalizable and platform-specific sources of variation from NanoString gene expression data on both ovarian cancer and Hodgkin lymphoma patients. A reference-based strategy, applicable to single-patient molecular testing is proposed for batch effect correction. The proposed protocol improved performance in an established Hodgkin lymphoma classifier, reducing batch-to-batch misclassification while retaining accuracy and precision. We suggest this strategy may facilitate development of NanoString and similar molecular assays by accelerating prospective validation and clinical uptake of relevant diagnostics.</p></div
PVCA and PCA plots of the Hodgkin Lymphoma clinical samples.
<p>We considered the PVCA plot (A) of the HL clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 49% of the variability in the data. HL1, HL2, and HL3 label each of unique CodeSets corresponding to the HL gene list.</p
Percentage of genes detected as a function of Signal to Noise Ratio by cohort.
<p>Each point on the plot represents a NanoString nCounter unique run (duplicates and triplicates included where available). The zoomed in section illustrates how the selected cut-off excludes samples that have low signal to noise and low % genes detected. HL: Hodgkin lymphoma clinical samples, OC: ovarian cancer clinical samples, OVCL: ovarian cancer cell lines, HLO: oligonucleotides corresponding to the HL CodeSet, OVO: oligonucleotides corresponding to the OC CodeSet.</p
PVCA and PCA plots of the ovarian cancer clinical samples.
<p>We considered the PVCA plot (A) of the OC clinical samples run in different batches. The percentages represent the variability explained by each factor and first order interaction between factors. The PCA plot (B) provides a two-dimensional summary of the pairwise plot of the first three principal components, which represent 40% of the variability in the data. CS1, CS2, and CS3 label each of unique CodeSets corresponding to the OC gene list.</p
PVCA of the HL clinical samples after adjusting batch effect using different methods.
<p>We consider the PVCA plot of the HL clinical samples run in different batches after adjusting BE with different methods. In each plot, percentages represent the variability explained by each factor and first order interaction between factors.</p
Impact of BE on downstream analysis, illustrated using a HL prognostic model.
<p>The x and y axes correspond to risk scores obtained in HL1 and HL2 respectively. The dashed line represents the identity line, and the solid line represents the best linear fit. The horizontal line indicates the threshold used for prediction. The results in (A) correspond to scores not corrected for BE, and in (B) scores are corrected using 3 reference samples that were run in both CodeSets.</p