Quality Control

Click on a button corresponding to an expression quality control process. Refer to the table below for guidance.

Process	Choose this process for...
Distribution Analysis	Displaying univariate distribution results for variables, with the option of computing and overlaying density estimates
Correlation and Principal Variance Component Analysis	Computing correlations between numeric variables, principal components of the correlation matrix, an outlier analysis, and a variance components decomposition
Correlation and Grouped Scatterplots	Computing correlations and scatterplot matrices for expression measurements across groups of arrays
Filter Intensities	Replacing measurements that lie outside a typical range of values in a data set, or filtering out rows with consistent poor performance across many conditions in an experiment
Feature Flagger	Screening array data and flagging features that have unusually low signals, as judged by a sufficiently large deviation from a specified group median
Missing Value Imputation	Replacing missing values in a data matrix with values computed from nonmissing values in the same row, performing a rowwise imputation
Pseudo Image	Generating an interactive pseudo image of an array by plotting a color-coded intensity variable using X- and Y-coordinates
Ratio Analysis	Converting two-channel expression data between log intensities and log ratios
Surface Summary	Visually examining a hybridization image pattern created from specified Z variables plotted over common X and Y variables, or performing spatial background correction for microarray data.
Variable Gene Selection	Selecting a subset of genes that exhibit high cell-to-cell variation in a Single-Cell RNA-Seq data set.

See Expression for other subcategories.