Using **SIGHTS** R-package

Elika Garg, Carl Murie and Robert Nadon

Abstract

Identifying rare biological events in high-throughput screens requires using the best available normalization and statistical inference procedures. It is not always clear, however, which algorithms are best suited for a particular screen. The Statistics and dIagnostics Graphs for High Throughput Screening (**SIGHTS**) R package is designed for statistical analysis and visualization of HTS assays. It provides graphical diagnostic tools to guide researchers in choosing the most appropriate normalization algorithm and statistical test for identifying active constructs.

The `sights`

package provides numerous normalization methods that correct the three types of bias that affect High-Throughput Screening (HTS) measurements: overall plate bias, within-plate spatial bias, and across-plate bias. Commonly-used normalization methods such as Z-scores (or methods such as percent inhibition/activation which use within-plate controls to normalize) correct only overall plate bias. Methods included in this package attempt to correct all three sources of bias and typically give better results.

Two statistical tests are also provided: the standard one-sample t-test and the recommended one-sample Random Variance Model (RVM) t-test, which has greater statistical power for the typically small number of replicates in HTS. Correction for the multiple statistical testing of the large number of constructs in HTS data is provided by False Discovery Rate (FDR) correction. The FDR can be described as the proportion of false positives among the statistical tests called significant.

Included graphical and statistical methods provide the means for evaluating data analysis choices for HTS assays on a screen-by-screen basis. These graphs can be used to check fundamental assumptions of both raw and normalized data at every step of the analysis process.

Citing Methods

Please cite the `sights`

package and specific methods as appropriate.

References for the methods can be found in this vignette, on their specific help pages, and in the manual. They can also be accessed by `help(sights_method_name)`

in R. For example:

The package citation can be accessed in R by: