Microarray analysis exercises 1

WIBR Microarray Analysis Course - 2006

Starting Data     Processed Data


You'll be using a sample of expression data from a study using Affymetrix (one color) U95A arrays that were hybridized to tissues from fetal and human liver and brain tissue. Each hybridization was performed in duplicate. Many other tissues were also profiled but won't be used for these exercises.

What we'll be doing to analyze these data:

You'll be using Excel to do most of the mathematical analyses, since this will show the exact formulas used to perform every step of the analysis pipeline. As a result, you'll need to use Excel functions and be familiar with some Excel conventions. See the Excel help for the details.

Preliminary information: Image analysis and calculation of expression value

  1. As described in Su et al., 2002, human tissue samples were hybridized on Affymetrix (one-color) arrays and chips were scanned. For each tissue, at least two independent samples were hybridized to separate chips.
  2. Scanned images were quantified (including measurement of background) using standard software.
  3. Data from a probeset (a series of oligos designed to a specific gene target) were used to calculate an expression values for that probeset using standard Affymetrix algorithms.
  4. See the manuals from Affymetrix for more information about these processes, and the Statistical Algorithms Description Document for the actual equations used.
  5. Note that these analysis protocols are generally specific to the chip type and its manufacturer.

Class 1 exercises

Part I. Normalization of expression data

  1. Why normalize? Chips may have been hybridized to different amounts of RNA, for different amounts of time, with different batches of solutions, etc. Normalization should remove systematic biases and make any comparisons between chips more meaningful.
  2. Download the starting data for the exercises.
  3. Look at the expression data for a selected series of experiments in the "raw" sheet.
  4. Calculate the trimmed mean of all expression values from each chip.
  5. To perform global normalization by trimmed means, on a new sheet ("norm") of the same file, scale each column of data so that each trimmed mean is 100.

Part II. Calculating ratios

  1. On a new sheet ("means"), calculate the mean of each pair of replicated experiments (converting 8 chips to 4 means).
  2. On a new sheet ("ratios"), calculate the ratios (for both brain and liver) of fetal tissue / adult tissue (converting 4 experiments to 2 ratios).

Part III. Log transformations

  1. Why use logarithms? Log-transformed ratios are helpful so up-regulated and down-regulated genes change by the same amplitudes. Log-transformed chip intensities are recommended for differential expression testing, since these statistical tests assume normal distributions -- which is true for log-transformed intensities but not untransformed intensities.
  2. Logarithms can use any base, but base 2 is easiest when transforming ratios, since transformed 2-fold ratios up or down will be +1 or -1. As a result, we'll do all logs with base 2 to keep thing simplest.
  3. On a new sheet ("logs"), calculate the logs (base 2) of all expression values, means, and ratios.

WIBR Microarray Analysis Course - 2006