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High throughput screening technologies have been developed to measure all the molecules of interest in a sample in a single experiment e. It is hoped this data will give researchers a better understanding of what they are studying. Each of these behaviors wreak havoc on statistical analyses.

Using automatic algorithms for data reduction has problems. Consider a dataset with variables with PCA being used to reduce the dimensions to a handful for analysis. On close examination it is seen that PCA produces linear combinations of the variables — — which is uninterpretable. Understanding the meaning and interactions of coefficients is not possible.

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Each PCA projection is uninterpretable so learning is not possible. As we are still left with a Large P Small N problem, direct hypothesis testing is not possible. Instead, the goal at this stage of the Analyzing the Science is interpretation and learning. It is important to treat this analysis as exploratory. This exploratory analysis is not conducted by analyzing all the data at once to see what stands out.

Instead of using algorithms to reduce the data, pick subsets of variables that are biologically meaningful and can be interpreted. Anything that is discovered in subset analyses should be viewed as hypothesis generating and potentially testable in follow-up designed experiments.

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Two analyses done by BioRankings are presented here as examples of this approach. Both involve high throughput screening data and hundreds or thousands of variables from a hundred or so subjects in a pre-diabetes study as part of the Integrative Human Microbiome Project. In the second analysis a subset of the genes from gut microbioal taxa were selected to test if gene copy number was associated with conversion from insulin sensitivity good outcome to resistance bad outcome. Collaborators were interested Analyzing the Science seeing if cytokines might be associated with microbiome composition.

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Cytokines are small peptides associated with many biological outcomes such as inflammation, sepsis, development, and other outcomes. Hundreds of small peptides and cytokines were measured so a subset of 12 thought to be associated with development Analyzing the Science diabetes were identified to avoid the problems associated with Large P Small N, and to have a focused subset where biological interpretation is possible. The 12 are listed in Table 1. Since Large P Small N data is exploratory the analysis of https://amazonia.fiocruz.br/scdp/blog/story-in-italian/the-cotton-mill-was-extremely-hot-today.php 12 peptides does not preclude us from analyzing other subsets of peptides.]