Introduction to mRNA Microarray

About terminology

• target: DNA hybridized to the array, mobile substrate
• probe: DNA spotted on the array (spot) ; one of these 25-mer oligonucleutides
• probe set: a collection of probes (e.g. 11) targeting the same transcript
• print-tip-group: collection of spots printed using the same print-tip (or pin), aka. grid.
• reporter: the sequence
• feature: a physical patch on the array with molecules intended to have the same reporter sequence (one reporter can be represented by multiple features)
• accuracy: how close are the estimated values to the truth
• precision: how variable are the estimates
• calibration/ normalization: adjust for systematic drifts associated with dye, array
• background correction: adjust fot the non-linearity at the lower end of the dynamic range
• PM: Perfect Match; MM: MisMatch

About microarray data

• Extracted from the images with artifacts (e.g. cross-talk) removed
• Final store in a matrix: row for probes, column for samples
• For each sample, each probe has one number from one-color arrays and two numbers for tow-color arrays
• Expression levels for the same genes from different arrays can be compared, after proper normalization
• Only calculate relative expressions

About history

• Evolved from Southern blotting, which is a procedure to detect and quantify a specific DNA sequence. Microarray can be thought as parallelized Southern blotting.
• First influential paper: Schena et al. (1995) Science [study the expression of 45 Arabidopsis genes]
• mRNA microarray got 50,000+ hits on pubmed for the past 20 years
• Main pros: lower costs, easier experimental procedure and more established analysis methods
• Brief timeline
  • Late 1980s: Lennon, Lehrach: cDNAs spotted on nylon membranes
  • 1990s: Affymetrix adapts oligonucleotide synthesis technology to build microchip with patent (So, the Genechip cannot be used by others)
  • 1990s: Brown lab in Stanford develops two-colour spotted array tech (open and free)
  • 1998: Yeast cell cycle expression profiling on spotted arrays (Spellmann) and Affymetrix (Cho)
  • 1999: Tumor type discrimination based on mRNA profiles (Golub)
  • 2000 - 2004: Affymetrix dominates the microarray market
  • Since 2003: Nimblegen, Illumina, Agilent
  • Since 2000: CGH, CNVs, SNPs, ChIP, tiling arrays
  • Since 2007: NGS (454, Solexa, ABI Solid, …)

About technology and design

• Collection of DNA spot on a solid surface
• Each spot contains many copies of the same DNA sequence (“probes”) [probes are designed to target specific genes]
• Part of genes sequence are complementary to a probe (“hybridize” or “stick to”)
• The amount of mRNA for target gene is measured with the amount of hybridization

Platforms

• Affymetrix
• Agilent
• Nimblegene
• Illumina
• ABI
• Spotted cDNA

One-color vs. two-color arrays

color means channel

• One-color arrays hybridize one sample per array ex. Affymetrix, Illumina Easier but need twice arrays
• Two-color arrays hybridize two samples on the same array ex. Agilent, Nimblegen Each spot produce two numbers

Most famous - Affymetrix

U133 chip

• HG U133A genechip represents more than 22,000 full length genes and EST clusters
• Around 20 probes (11 probe pairs) in a probe set to target the same transcript
• Not necessarily evenly spaced: sequence property matters
• Probes are randomly located on the chip to avergae out the effects of the array surface
• High signal intensity and lower noise

Probe set naming

• …_at: (anti-sense target) detects antisense strand of given gene, these are unique probes

• _a_at: (gene family probeset) recognize multiple transcripts of same gene

  _s_at: (identical probeset) recognize multiple transcripts from different genes

  _x_at: (mixed probe set) cross-hybrization with other sequences used for design

  a, s, x derived from gene cluster + gene family info

Analysis Tasks

• IMPORT : reformating and setup/curation of the metadata
• NORMALIZATION
• QUALITY ASSESSMENT & CONTROL
• DIFFERENTIAL EXPRESSION
• Annotation
• Gene set enrichment
• Clustering and classification
• Integration of other datasets

Analysis challenges

• Data normalization: remove systematic technical artifacts
  • Within array: variations of probe intensities
    • cross-hybridization: probe capture the “wrong " target
    • probe sequences: some probes bind too tight like “sticker”
    • chip factors: spot sizes, smoothness of array surface
  • Between array: variations of sample processing, image reader etc.
• Calculation of gene expressions: standard of summarizing multiple probes belonging to the same gene into one number
• Differential expression detection: find different genes between different conditions, e.g. case vs. control

Data normalization

Why?

Artifacts are introduced:

• Sample preparation: PCR effects
• Array itself: array surface effects, printing-tip effects
• Hybridization: non-specific binding, GC effects
• Scanning: scanner effects

So, the normalization is mean to ensure differences in intensities due to differential expressions, not artifacts

Types

• Within-array: remove array-specific artifacts individually and obtain true signals
• Between-array: put different arrays at the same baseline to make sure that numbers are comparable

First: Within array normalization (two-color)

Most common problem: intensity dependent effect

Most popular: loess normalization

What is MA plot?

Widely used diagnostic plot fot microarray and sequencing data.

M measures relative expression; A measures total expression

What is loess normalization?

ASSUMPTIONS : 1. most genes are not DE (M=0); 2. M and A are independent (MA plot should be flat and centered at 0)

Loess (lowess): locally weighted scatterplot smoothing [to fit a smooth curve between two variables]

Second: Within array normalization (one-color)

Background error models: RMA( Robust Multi-array Average) Model fitting: Median Polish

Also, the MA plot and background error models (e.g. RMA) are popular in other microarrays, ChIP-seq, RNA-seq

Third: Between array

Artifacts reasons could be:

• Total amount of mRNA used
• Properties of the agents used
• Array properties
• Settings of laser scanners

Lineat scaling method

Affymetrix software MAS: use a number of “housekeeping” genes and assume their expressions are identical, then rescale all data [ based on one-step Tukey Biweight (TBW) ]

Non-linear smoothing based

used in dChip: based on PM-MM

PROCEDURE: find a set of genes invariant across arrays => find a “baseline” array => ohter arrays fit a smooth curve on expressions of invariant genes => normalize based on the fitted curve (Q-Q plot)

Quantile normalization

Force the distribution of all data from all arrays to be the same, but keep the ranks of the genes

Simple, Fast, Easy and can correct for quite nasty non-linearities (saturation, background); no matter how the data is good/bad

PROCEDURE: find SMALLEST value for each sample => AVERAGE them => replace each value by the average => find the NEXT SMALLEST, then AVERAGE => replace

RESULT: 1. The values taken in each column are exactly the same; 2. The ranks of genes in each column are the same as before normalization

CAUTION: QN is too strong and often remove the true signals

Microarray with bioconductor

200+ packages for microarray

Platform-specific data import

http://www.bioconductor.org/docs/workflows/oligoarrays/

• Affymetrix 3’ IVT (e.g. Human U133 Plus 2.0, Mouse 430 2.0) => affy [ one of the earliest packages ]
• Affymetrix Exon (e.g. Human Exon 1.0 ST) => oligo, exonmap, xps
• Affymetrix SNP arrays => oligo [ designed to replace affy package ]
• Nimblegen tiling arrays (e.g. for ChIP-chip)=> Ringo
• Affymetrix tiling arrays (e.g. for ChIP-chip) => Starr
• Illumina bead arrays => beadarray, lumi