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Li and Wong introduced a method, where a large number of genes are selected ad-hoc as references, rather than using a standard set of 'housekeeping genes'. Their method assumes that there is a subset of unchanged genes, between any two samples.

Ideally one would like a common invariant set of reference genes across all chips, but in practice, only a very few probes are in common rank order, or even close to that, across all chips. Terry Speed's group introduced a non-parametric procedure normalizing to a synthetic chip. Their method assumes that the distribution of gene abundances is nearly the same in all samples.

For convenience they take the pooled distribution of probes on all chips. Then to normalize each chip they compute for each value, the quantile of that value in the distribution of probe intensities; they then transform the original value to that quantile's value on the reference chip. In a formula, the transform is. Figure 3. If F i and F ref are fairly similar in shape, then in practice this transform is not too different from a straight line, which is what a scaling transform looks like; see Figure 4.

However the transform is strong enough to cope with the non-linear ratio-intensity relationships revealed in figure 5A; see figure 5B after quantile transformation.

Figure 4. The effects of quantile normalization on raw probe values in three chips. Raw values are on x-axis, normalized values on y-axis. Often this transform looks very much like a scaling transform nearly linear , but sometimes it is quite non-linear. Figure A. Ratio Intensity Plot of all probes for four pairs of chips from GeneLogic spike-in experiment.

Figure B. As in A, after normalization by matching quantiles. Both figures courtesy of Terry Speed. This form of normalisation also reduces noise among replicate measures of the same samples, compared to normalization by scaling, as show below in figure 6.

Figure 6. Each dot represents one probe on an Affymetrix U95A chip. On the y-axis is the ratio of variance across a set of replicates after quantile normalisation, divided by the variance of the scale-normalized values.

On the x-axis are the mean levels. Both axes on log scale. The main drawback of this approach to normalization is the strong assumption that the distributions of probe intensities are identical even if individual probes differ in their positions in the distribution. This is true for low abundance genes, and to a fairly good approximation for genes of moderate abundance, but certainly not true for the few high-abundance genes, whose typical levels vary noticeably from sample to sample.

There has been considerable discussion over the appropriate algorithm for constructing single expression estimates based on multiple-probe hybridization data. To date, over a dozen different methods have been published, which aim to synthesize the different readings from the various probes for a gene, into a single estimate of transcript abundance.

MAS 4. First the intensities are transformed to a logarithmic scale before the average is taken; this equalizes the contribution of different probes. The idea of averaging different probe intensities for the same gene is seems quite wrong. The individual probes in a probe set have very different hybridization kinetics.

Taking an average of their intensities, is like averaging the readings from scans taken at very different settings. A good algorithm should compare information about probe characteristics, based on the performance of each probe across chips, and use this to make a better estimate. These principles are the basis of the multi-chip models. Affymetrix has seen the evidence, and they are planning to release their own multi-chip model in A chemical motivation for multi-chip models comes from reasoning that the amount of signal from one probe in a gene's probe set, should depend both on the amount of that gene in the sample, and on the specific affinity of the probe for that gene's mRNA.

The statistical motivation for multi-chip models is that the signals from individual probes move in parallel across a set of chips; the signals have roughly the same pattern across the different samples, as shown in Figure 3. The animations of probe sets in dChip show this quite compellingly. Probe signals from a spike-in experiment. The concentrations are plotted along the horizontal axis log scale , and the probe signals are plotted on the vertical axis log scale. This program visualizes pathways.

The user can display any kind of quantitative data from gene and protein experiments directly within the pathways colours represent the value. The user has the possibility to display any kind of quantitative data from gene and protein experiments directly within the pathways colours represent the value. The linking between the pathway items and the experiment data is done over the gene or protein names and their accession numbers. Furthermore the user has a Link to us Submit Software.

I need it for my work. FlexiHub Simin To make best use of computer resources FlexiHub is a must have software for mid to large scale It provides matched molecular and drug activity profiling data.

This data may be used to 1 assess molecular and drug data reproducibility, 2 determine repositioning opportunities for FDA-approved compounds, 3 identify potential drug response and gene regulatory determinants, and 4 identify and validate novel genes associated with phenotypic processes.

This data is an important precision medicine resource. Rajapakse, Mirit I. Reinhold, Nucleic Acids Research, October Rajapakse, Robin Sebastian, Kurt W. Kohn, Julia Krushkal, Mirit I. Aladjem, Beverly A.

Teicher, Paul S. Meltzer, William C. Reinhold, John D. A database and query tool designed for the cancer research community to facilitate integration of the molecular datasets generated by the Genomic and Pharmacology Facility and its collaborators on the NCI Reinhold, Cancer Research, July Generates color-coded Clustered Image Maps CIMs "heat maps" to represent high-dimensional data sets such as gene expression profiles.

Clustering of the axes brings like together with like to create patterns of color.



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