An analysis of the needs assessment techniques and finding

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An analysis of the needs assessment techniques and finding

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Introduction[ edit ] Microarray data analysis is the final step in reading and processing data produced by a microarray chip. Samples undergo various processes including purification and scanning using the microchip, which then produces a large amount of data that requires processing via computer software.

It involves several distinct steps, as outlined in the image below. Changing any one of the steps will change the outcome of the analysis, so the MAQC Project [1] was created to identify a set of standard strategies.

Companies exist that use the MAQC protocols to perform a complete analysis. There are also open source options that utilize a variety of methods for analyzing microarray data. Aggregation and normalization[ edit ] Comparing two different arrays or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects.

Dye normalization for two color arrays is often achieved by local regression. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.

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Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence.

These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average RMA [5] is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through median polish.

The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise.

According to the Affycomp benchmark [10] FARMS outperformed all other summarizations methods with respect to sensitivity and specificity. Identification of significant differential expression[ edit ] Many strategies exist to identify array probes that show an unusual level of over-expression or under-expression.

The simplest one is to call "significant" any probe that differs by an average of at least twofold between treatment groups.

An analysis of the needs assessment techniques and finding

More sophisticated approaches are often related to t-tests or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.

The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.

Pattern recognition[ edit ] Commercial systems for gene network analysis such as Ingenuity [13] and Pathway studio [14] create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as FunRich, [15] GenMAPP and Moksiskaan also aid in organizing and visualizing gene network data procured from one or several microarray experiments.

A wide variety of microarray analysis tools are available through Bioconductor written in the R programming language. The frequently cited SAM module and other microarray tools [16] are available through Stanford University. Another set is available from Harvard and MIT.An IEP team's introduction to functional behavioral assessment and behavior intervention plans.

The object of the IDEA is not to arbitrarily mandate change, but to provide an environment conducive to the education of all students, including those with disabilities.

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Microarray analysis techniques - Wikipedia