ArrayStat's approach to outliers is fine-tuned to whether the user selects a small-sample or a pooled-error approach. To test for outliers in the small-sample approach, each set of replicates within a condition is considered as a separate sample. A spread/deviation statistic (if the number of replicates per condition is 3 or 4) or the sample kurtosis (if the number of replicates per condition is 5 or greater) is used to determine which data points depart significantly from a normal distribution.

ArrayStat provides thorough statistical output: For example, for each gene it provides the mean, standard deviation, and number of observations for each condition, the difference between the conditions, the confidence interval and standard error for the difference, a test statistic (t, z, or F, depending on the model selected), a p-value, and whether the difference should be regarded as statistically significant (given the adjustment selected for false-positive error control).

ArrayStat is a software package that is optimized for statistical analysis of array gene expression data.

It can calculate variation across replicates on an array element by array element basis. In the absence of poorly reproduced spots (outliers), this method is straightforward and is available in common statistics textbooks. However, with the very small number of replicates, this method tends to produce imprecise and potentially invalid estimates of variation. We call this procedure the "small sample" method. ArrayStat also offers two pooled-error methods.

Answer: For continuous variables, you identify outliers by using a univariate analysis to check for normality. If the distribution is highly skewed, you can do a data transformation to make the distribution of the data closer to normal. After checking the distribution and normality of the data, plot the survey weight against the variable to determine which of the extreme values identified in the univariate analysis are outliers.

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Other commercially available systems emphasize either visualization or "pattern finding" algorithms like cluster analysis. Although visualization and pattern finding are sometimes regarded as the first step in array data analysis, we believe that quality control and statistical evaluation of differential expression on a gene-by-gene basis are the first steps towards data analysis of arrays. A crucial difference from other commercially available systems is how ArrayStat makes use of replicates.

ArrayStat needs a minimum of two replicates, although we recommend using at least three replicates. This is not just our own opinion, but an emerging consensus in the field (for a thoughtful discussion on this issue, see Lee, M-L., Kuo, F. C., Whitmmore, G. A., & Sklar, J. (2000)). Importance of replication in microarray gene expression studies: Statistical methods and evidence form repetitive cDNA hybridizations. Proceedings of the National Association of Science, 97, 9834-9839.

Bases error estimates on the entire array, markedly increasing sensitivity for detecting effects (statistical power)

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Yes. ArrayStat offers a range of normalization options that are specifically designed and tested for application to array data. ArrayStat also offers iterative procedures in which potentially differentially expressed genes are omitted from normalization calculations and error propagation in which the error associated with reference values is included in the final error estimate.

No. ArrayStat is designed specifically for use with expression arrays with the purpose that one does not need to be an expert in statistics to get meaningful data. One advantage of this approach is that the software can be set up with appropriate configuration settings that determine which default statistical models and procedures are applied to data.

You cannot apply your own algorithms within ArrayStat; however, you can import and export results at any point during data analysis. For example, you may wish to apply your own normalization procedure to the data before importing them into ArrayStat. Or you may wish to use some of ArrayStat's outlier detection methods and then export the cleaned-up data for additional analysis.

ArrayStat analyzes expression data from Radio-isotopic or Single and double fluorescent Images quantified with our own ArrayVision™ or with third party software. The data can be normalized before importing into ArrayStat or ArrayStat can normalize the data for you. If background subtraction is necessary, however, it must be done before importing the data into ArrayStat.