1 Introduction

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:

2 Getting Started

2.1 Installation and loading

  1. Please install the package directly from Bioconductor and load it. Note that SIGHTS requires a minimum R version of 3.3.
  1. This should also install and load the packages that SIGHTS imports: ggplot2 (Wickham, 2009), reshape2 (Wickham, 2007), qvalue (Storey, 2015), MASS (Venables and Ripley, 2002), and lattice (Sarkar, 2008).
    Otherwise, you can install/update these packages manually.

2.2 Importing and exporting data

All SIGHTS normalization functions require that the data be arranged such that each plate is a column and each row is a well. The arrangement within each plate should be by-row first, then by-column. For more details and example, see help("ex_dataMatrix"). This required arrangement can be done in Microsoft Excel before importing the data into R, although advanced users may prefer to do so in R as needed.

  1. The datasets within SIGHTS can be loaded by:
  1. Your own data can be imported by giving the path of your file:
  • If it is a .csv or .txt file, run
  • If it is a Microsoft Excel file, you can import it directly by installing another package:
  1. Similarly any object saved in R (e.g. normalized results) can be exported as .csv or .xlsx files:

2.3 Information about data

  1. There are two datasets provided within SIGHTS:
  • CMBA data (Murie et al., 2015), see help("ex_dataMatrix")
  • Inglese et. al. data (Inglese et al., 2006), see help("inglese")
  1. Some basic information about data (including your own data after importing) can be accessed by various functions. For example, information about the Inglese et al. data set can be obtained as follows:

2.4 Information about methods

  1. There are several methods provided within SIGHTS:
  • Normalization:
    • Z, Robust Z (see Malo et al. (2006)),
    • Loess (Baryshnikova et al., 2010),
    • Median Filter (Bushway et al., 2011),
    • R (Wu et al., 2008), and
    • SPAWN (Murie et al., 2015).
  • Statistical testing:
    • one-sample t-test,
    • one-sample RVM t-test (Malo et al., 2006; Wright and Simon, 2003), and
    • FDR correction (Storey, 2002).
  • Plotting:
    • 3d plot,
    • heatmap,
    • auto-correlation plot,
    • scatter plot,
    • boxplot,
    • inverse-gamma fit plot, and
    • histograms.

See help("normSights"), help("statSights"), help("plotSights"), and the help pages of individual methods for more information.

  1. Information about the package functions can be accessed by:

2.5 Quick reference

  1. Normalization - All normalization functions are accessible either via normSights() or their individual function names (e.g. normSPAWN()).

  2. Statistical tests - All statistical testing functions are accessible either via statSights() or their individual function names (e.g. statRVM()).

  3. Plots - All plotting functions are accessible either via plotSights() or their individual function names (e.g. plotAutoco()).

The results of these functions can be saved as objects and called by their assigned names. For example: