Analysis of Proteomics Spectra from Serum Samplesjmorris/talks_files/tamu2c.pdf12886 2.547 0:005 11...

The Analysis of ProteomicsSpectra from Serum Samples

Jeffrey S. MorrisDepartment of Biostatistics

MD Anderson Cancer Center

TAMU: PROTEOMICS SPECTRA 1

What Are Proteomics Spectra?

DNA makes RNA makes Protein

Microarrays allow us to measure the mRNA complement of a setof cells

Mass spectrometry allows us to measure the proteincomplement (or subset thereof) of a set of cells

Proteomics spectra are mass spectrometry traces of biologicalspecimens

Why Are We Excited?

Profiles can be assessed using less invasive samples (serum,urine, nipple aspirate fluid) rather than tissue biopsies

Why Are We Excited?

Spectra are cheaper to run on a per unit basis than microarrays

Why Are We Excited?

Can run samples on large numbers of patients

Why Are We Excited?

Can run samples on large numbers of patients

How Does Mass Spec Work?

What Do the Data Look Like?

Learning: Spotting the Samples

What the Guts Look Like

Taking Data

Some Other Common Steps

Fractionating the Samples

Changing the Laser Intensity

Working with Different Matrix Substrates

SELDI: A Special Case

www.ciphergen.com

Precoated surface performs some preselection of the proteins foryou.

Machines are nominally easier to use.

A Tale of Two Examples

Example 1 : Learning from the literature

Example 2 : Testing out our understanding

A story in pictures

Example 1: Feb 16 ’02 Lancet

• 100 ovarian cancer patients

• 100 normal controls

• 16 patients with ’benign disease’

Example 1: Feb 16 ’02 Lancet

• 100 ovarian cancer patients

• 100 normal controls

• 16 patients with ’benign disease’

Use 50 cancer and 50 normal spectra to train a classificationmethod; test the algorithm on the remaining samples.

Their Results

• Correctly classified 50/50 of the ovarian cancer cases.

• Correctly classified 46/50 of the normal cases.

• Correctly classified 16/16 of the benign disease as ’other’.

Data at http://clinicalproteomics.steem.com

Large sample sizes, using serum

The Data Sets

3 data sets on ovarian cancer

Data Set 1 : The initial experiment. 216 samples, baselinesubtracted, H4 chip

The Data Sets

Data Set 2 : Followup: the same 216 samples, baselinesubtracted, WCX2 chip

The Data Sets

Data Set 3 : New experiment: 162 cancers, 91 normals, baselineNOT subtracted, WCX2 chip

The Data Sets

Data Set 3 : New experiment: 162 cancers, 91 normals, baselineNOT subtracted, WCX2 chip

A set of 5-7 separating peaks is supplied for each data set.

We tried to (a) replicate their results, and (b) check consistencyof the proteins found

We Can’t Replicate their Results (DS1 & DS2)

Some Structure is Visible in DS1

Or is it? Not in DS2

Processing Can Trump Biology (DS1 & DS2)

We Can Analyze Data Set 3!

Do the DS2 Peaks Work for DS3?

Do the DS3 Peaks Work for DS2?

Which Peaks are Best? T-statistics

Note the magnitudes: t-values in excess of 20 (absolute value)!

One Bivariate Plot: M/Z = (435.46,465.57)

Perfect Separation. These are the first 2 peaks in their list, andones we checked against DS2.

Another Bivariate Plot: M/Z = (2.79,245.2)

Perfect Separation, using a completely different pair. Further,look at the masses: this is the noise region.

Perfect Classification with Noise?

This is a problem, in that it suggests a qualitative difference inhow the samples were processed, not just a difference in thebiology.

This type of separation reminds us of what we saw with benigndisease.

Mass Accuracy is Poor?

A tale of 5 masses...

Feb ’02 Apr ’02 Jun ’02DS1 DS2 DS3-7.86E-05 -7.86E-05 -7.86E-052.18E-07 2.18E-07 2.18E-079.60E-05 9.60E-05 9.60E-050.000366014 0.000366014 0.0003660140.000810195 0.000810195 0.000810195

How are masses determined?

Calibrating known proteins

Calibration is the Same?

M/Z vectors the same for all three data sets.

Machine calibration the same for 4+ months?

What is the Calibration Equation?

The Ciphergen equation

U= a(t− t0)2 + b, U = 20K, t = (0, 1, ...) ∗ 0.004

Fitting it here

a = 0.2721697 ∗ 10−3, b = 0, t0 = 0.0038

U= a(t− t0)2 + b, U = 20K, t = (0, 1, ...) ∗ 0.004

Fitting it here

a = 0.2721697 ∗ 10−3, b = 0, t0 = 0.0038

These are the default settings that ship with the software!

Other issues

Q-star data different

clinical trials?

Example 2: Proteomics Data Mining

41 samples, 24 with lung cancer∗, 17 controls.

20 fractions per sample.

Goal: distinguish the two groups;

Data used to be at

http://www.radweb.mc.duke.edu/cme/proteomics/explain.htm

but the site has been retired. Send email to Ned Patz or MikeCampa at Duke if interested.

Raw Spectra Have Different Baselines

Note the need for baseline correction.

Oscillatory Behavior...

Roughly half the spectra have sinusoidal noise.

Oscillatory Behavior...

Roughly half the spectra have sinusoidal noise. We’re seeing theA/C power cord.

Baseline Adj: Fraction Agreement, Before & After

Fractionation is Unstable

Unfractionating the Data

The Overall Average Shows Spikes. Difference It.

Computer Buffer?

Spike spacing has a wavelength of 4096 = 212.

Are We Done Cleaning Yet?

Give the problem a chance to be easy, try some simpleclustering.

PCA Splits off Half the Normals

Peaks at Integer Multiples of M/Z 180.6!

This suggests a polymer. No Amino Acid dimers fit.

Cleaning Redux

• Baseline Correction and Normalization

• Inconsistent Fractionation

• Computer Buffers

• Polymers in some Normal Spectra

• Peak Finding (Use Theirs)

Data reduced to 1 spectrum/patient, with 506 peaks perspectrum.

Find the Best Separators

Peaks MD P-Value Wrong LOOCV12886 2.547 ≤ 0.005 11 118840, 12886 5.679 ≤ 0.01 5 63077, 12886 9.019 ≤ 0.01 3 4742635863, 8143 12.585 ≤ 0.01 3 38840, 128864125, 7000 23.108 ≤ 0.01 1 19010, 1288674263

There are 9 values that recur frequently, at masses of 3077,4069, 5825, 6955, 8840, 12886, 17318, 61000, and 74263.

P-values are not from table lookups!

Testing Reality (Significance)

Generate a bunch of ’random noise’ data matrices, each 41× 506in size.

For each matrix, split the 41 noise ’samples’ into groups of 24and 17.

Repeat our search procedure on the random data, and see howwell we can separate things.

The Eyeball Test

We applied one last filtering step and actually looked at theregions identified. All 9 peaks listed above passed the eye test.

Blue lines = Cancers

Red lines = Controls

Punchlines

• There is no magic bullet here. (Too bad)

Punchlines

• Data preprocessing is extremely important with this type ofdata, and there is still much room for improvement.

Punchlines

• Dimension reduction is critical; both to avoid spurious structureand to focus our attention on peaks.

Punchlines

• There is structure in this data (some peaks have beenconfirmed, and the writeup is in progress) and it can be found!

Punchlines

• There is structure in this data (some peaks have beenconfirmed, and the writeup is in progress) and it can be found!

Other Stuff

We were the only ones to notice the sinusoidal noise.

Other Stuff

and the clock tick.

Other Stuff

and the clock tick.

and we also won the competition...

Important Lessons

• Experimental Design Issues are Crucial

Important Lessons

? Randomization? Uniform handling of samples? Blinding

Important Lessons

• Careful Pre-Processing of Data is Essential

Important Lessons

? Calibration? Baseline Correction? Normalization

Important Lessons

• Exploratory Data Analysis is Important: Look at the Data!

Important Lessons

• Exploratory Data Analysis is Important: Look at the Data!

? Search for anomalies? Confirm numerical results

References

On the Lancet data:

Baggerly, Morris and Coombes (2003), accepted byBioinformatics pending revisions.

On the Proteomics Data Mining Conference data:

Baggerly, Morris, Wang, Gold, Xiao and Coombes (2003),Proteomics, 3(9):1677-1682.

pdf preprints are available.

The Deluge

Bladder Cancer

Pancreatic Cancer

Leukemia

Colorectal Cancer

Brain Cancer

Several show real structure, several show processing effects.

’If you’re not working on a proteomics project, you will be soon!’Kevin Coombes to Bioinf section, 3/25/03

Collaborators

Keith Baggerly

Kevin Coombes

Jing Wang

David Gold

Lian-Chun Xiao

*********************

Ryuji Kobayashi

David Hawke

John Koomen

Analysis of Proteomics Spectra from Serum Samplesjmorris/talks_files/tamu2c.pdf12886 2.547 0:005 11...

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