Show Menu
Cheatography
  1. Home >
  2. Education >

Microarray Cheat Sheet by

Microarray Bioinformatics - RA / IBAB / 2023

Introd­uction

Principle
Amount of hybrid­isation detected is propor­tional to no. of fragments in sample
Microarray
DNA probes bound to glass slide, to which sample DNA fragments can be hybridised
Probes
Oligon­ucl­eot­ides, ink-jet printed onto slides (Agilent) or synthe­sised in-situ (Affym­etrix)
Sample
labelled ssDNA or antisense RNA

Replicates

Technical replicate
Repeated measur­ements or procedures using the same biological sample to evaluate precision and reprod­uci­bility
Biological replicate
Use of multiple indepe­ndent biological samples to account for biological variab­ility

One and Two Color Arrays

One Color Array
Each sample loaded into a separate microarray
Two Color Array
Two samples, labelled differ­ently, loaded onto same microarray in same amounts.
Competitive hybrid­isa­tion.

Data Prepro­cessing

Background Correction
- Adjust for non-sp­ecific hybrid­isation
- mismatch probes (Affym­etrix)
- exogenous negative control spots
- remove features with lower intensity than background
Log Transf­orm­ation
Improves data distri­bution for classical statis­tical analysis
Normal­isation
Removes systematic effects due to technical differ­ences, which aren't due to biological differ­ences

Normal­isation

Within­­Array Normal­­is­ation
Two-color microa­­rrays;
align two channels for each array
Betwee­­nArray Normal­­is­ation
One-color microa­­rrays;
Single channel platforms;
Quantile normal­­is­a­tion, Cyclic Loess
Sources of Bias
Dye bias, Array bias, Spatial bias
Dye Swap Design
pair of samples compared twice with reversed dye
Reference Design
Each sample hybridised against a common reference sample
 

Within Array Normal­isation

Sources of Bias
- Differ­ential dye incorp­oration
- Diff. emission response to excitation
- Non-un­iform focusing across the array
Correction of diff responses of Cy3 and Cy5 channels
1. LR of Cy3 vs Cy5 intensity
2. LR of log ratio against avg. intensity (MA plot)
3. Non-linear (Loess) regression of log ratio against avg. intensity

MA plots

MA plot - Interp­ret­ation

Vertical axis
Log ratio (R/G) = M i.e. log fold change;
M = log2 (condA exp / condB exp)
Horiontal axis
average log-in­tensity between two cond;
mean(condA exp + condB exp)
Non-zero intercept
One channel is consis­tently brighter than the other
Slope not equal to 1
One channel responds more strongly at high intens­ities than other
Slope not straight line
Non-linear relation b/w intens­ities of two channels

Loess Regression

Working
- local reg in overla­pping windows of data
- join the regression to form a smooth curve

Between Array Normal­isation

Types
Scaling, centering, distri­bution normal­isation
Scaling
Ensure mean/m­edian of all distri­butions are equal; Subtract overall mean log intensity from each log intensity
Centering
Ensure that all the distri­but­ion’s mean and SD are equal
Quantile normal­isation
- sort each array in order
- average across rows
- sort avg values in original order
Explan­atory variables
Covariates (quant­itative measur­ements) and factors (categ­orical variables)
Levels
Unique values within a factor
 

Matrices

Design or Model matrix
Describes the experi­mental design of the microarray experiment
Contrast matrix
Defines specific compar­isons of interest b/w different experi­mental conditions i.e. defines the hypotheses to be tested
Types of design matrices
1. Mean Reference Model (with intercept)
2. Means model (wihout intercept)
Means model
Mean gene expression levels compared indepe­ndently for each sample group
Mean reference model
One sample group set as baseline or reference; gene exp in other groups compared relative to reference

Design Matrix

Contrast Matrix

Microarray Overview

Applic­ations
- find expressed genes in a sample­/cond
- find diff exp across sample­s/cond
- exp signatures of sample­/cond
- exp signature of set of genes
Limita­tions
- rely on prev knowledge of genome seq
- high BG levels due to cross-hyb
- complex normal­isa­tions needed
- limited range of detection due to BG and saturation signals
Not good for
- determ­ining exp or diff exp of single or small set of genes
- accurately studying protein levels and functional activity (measure steady­-state levels of RNA transc­ripts)
- absolute level of exp of a gene (true conc of mRNA)
 

Metadata

  • Languages:
  • Published: 13th September, 2023
  • Last Updated: 16th September, 2023

Comments

No comments yet. Add yours below!

Add a Comment

Your Comment

Please enter your name.

    Please enter your email address

      Please enter your Comment.

          Related Cheat Sheets

          seqkit Cheat Sheet