Biomedical subjects

K R Coombes

Publications and source records attributed to K R Coombes.

5 recordsLinked to original sources

PubMed2001

Sources of nonlinearity in cDNA microarray expression measurements.

BACKGROUND: A key assumption in the analysis of microarray data is that the quantified signal intensities are linearly related to the expression levels of the corresponding genes. To test this assumption, we experimentally examined the relationship between signal and expression for the two types of microarrays we most commonly encounter: radioactively labeled cDNAs on nylon membranes and fluorescently labeled cDNAs on glass slides. RESULTS: We uncovered two sources of nonlinearity. The first, which led to discrepancies in analysis affecting the fluorescent signals, was signal quenching associated with excessive dye concentrations. The second, affecting the radioactive signals, was a nonlinear transformation of the raw data introduced by the scanner. Correction for this transformation was made by some, but not all, image-quantification software packages. CONCLUSIONS: The second type of nonlinearity is more troublesome, because it could not have been predicted a priori. Both types of nonlinearities were detected by simple dilution series, which we recommend as a quality-control step.

Calibration↗

PubMed2001

Microarrays: handling the deluge of data and extracting reliable information.

Application of powerful, high-throughput genomics technologies is becoming more common and these technologies are evolving at a rapid pace. Genomics facilities are being established in major research institutions to produce inexpensive, customized cDNA microarrays that are accessible to researchers in a broad range of fields. These high-throughput platforms have generated a massive onslaught of data, which threatens to overwhelm researchers. Although microarrays show great promise, the technology has not matured to the point of consistently generating robust and reliable data when used in the average laboratory. This article addresses several aspects related to the handling of the deluge of microarray data and extracting reliable information from these data. We review the essential elements of data acquisition, data processing and data analysis, and briefly discuss issues related to the quality, validation and storage of data. Our goal is to point out some of the problems that must be overcome before this promising technology can achieve its full potential.

DNA, Complementary↗

PubMed2001

Identifying differentially expressed genes in cDNA microarray experiments.

A major goal of microarray experiments is to determine which genes are differentially expressed between samples. Differential expression has been assessed by taking ratios of expression levels of different samples at a spot on the array and flagging spots (genes) where the magnitude of the fold difference exceeds some threshold. More recent work has attempted to incorporate the fact that the variability of these ratios is not constant. Most methods are variants of Student's t-test. These variants standardize the ratios by dividing by an estimate of the standard deviation of that ratio; spots with large standardized values are flagged. Estimating these standard deviations requires replication of the measurements, either within a slide or between slides, or the use of a model describing what the standard deviation should be. Starting from considerations of the kinetics driving microarray hybridization, we derive models for the intensity of a replicated spot, when replication is performed within and between arrays. Replication within slides leads to a beta-binomial model, and replication between slides leads to a gamma-Poisson model. These models predict how the variance of a log ratio changes with the total intensity of the signal at the spot, independent of the identity of the gene. Ratios for genes with a small amount of total signal are highly variable, whereas ratios for genes with a large amount of total signal are fairly stable. Log ratios are scaled by the standard deviations given by these functions, giving model-based versions of Studentization. An example is given.

Analysis of Variance↗

PubMed2001

Cancer genomics: promises and complexities.

The impending final deciphering of the complete human genome, coupled with the advancement of high-throughput technologies, is positioned to bring about a fundamental transformation in cancer research. The era of molecular biology is transforming into the era of genomic biology, with an unprecedented promise of understanding multifactorial diseases and of identifying specific targets that can be used to develop patient-tailored therapies. Although the genomic approach is in an early phase of its development and its tools need to be honed, the application of genomic technologies to cancer research has already generated exciting results both in target identification and in disease classification. In this article, we review some of the developments pertinent to cancer research, discuss potentially problematic areas associated with them, and comment on future trends and issues.

DNA, Neoplasm↗

PubMed1990

Blood use during extracorporeal membrane oxygenation.

An analysis of the transfusion records of 91 neonatal patients subjected to extracorporeal membrane oxygenation (ECMO) is reported. Mean daily blood usage was 250 mL of red cells (RBCs), 80 mL of fresh-frozen plasma, and 2 units of platelets. Average time on ECMO was 4.6 days. Group O or ABO type-specific RBCs and group AB or ABO type-specific plasma products and platelets were transfused. RBCs were not washed, and neither RBCs nor other components were tested for anticytomegalovirus (CMV) or irradiated. No cases of posttransfusion CMV infection or graft-versus-host disease were observed. Hemolysis in eight patients was traced to occlusions in the ECMO circuit. All but three patients survived ECMO. Contrary to a previous report, an active ECMO program for neonatal patients imposes a minimal burden on the hospital transfusion service.

Bilirubin↗