TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein
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1 The Analysis of Proteomics Spectra from Serum Samples Jeffrey S. Morris Department of Biostatistics MD Anderson Cancer Center
2 TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein
3 TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein Microarrays allow us to measure the mrna complement of a set of cells
4 TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein Microarrays allow us to measure the mrna complement of a set of cells Mass spectrometry allows us to measure the protein complement (or subset thereof) of a set of cells
5 TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein Microarrays allow us to measure the mrna complement of a set of cells Mass spectrometry allows us to measure the protein complement (or subset thereof) of a set of cells Proteomics spectra are mass spectrometry traces of biological specimens
6 TAMU: PROTEOMICS SPECTRA 1 What Are Proteomics Spectra? DNA makes RNA makes Protein Microarrays allow us to measure the mrna complement of a set of cells Mass spectrometry allows us to measure the protein complement (or subset thereof) of a set of cells Proteomics spectra are mass spectrometry traces of biological specimens
7 TAMU: PROTEOMICS SPECTRA 2 Why Are We Excited? Profiles can be assessed using less invasive samples (serum, urine, nipple aspirate fluid) rather than tissue biopsies
8 TAMU: PROTEOMICS SPECTRA 2 Why Are We Excited? Profiles can be assessed using less invasive samples (serum, urine, nipple aspirate fluid) rather than tissue biopsies Spectra are cheaper to run on a per unit basis than microarrays
9 TAMU: PROTEOMICS SPECTRA 2 Why Are We Excited? Profiles can be assessed using less invasive samples (serum, urine, nipple aspirate fluid) rather than tissue biopsies Spectra are cheaper to run on a per unit basis than microarrays Can run samples on large numbers of patients
10 TAMU: PROTEOMICS SPECTRA 2 Why Are We Excited? Profiles can be assessed using less invasive samples (serum, urine, nipple aspirate fluid) rather than tissue biopsies Spectra are cheaper to run on a per unit basis than microarrays Can run samples on large numbers of patients
11 TAMU: PROTEOMICS SPECTRA 3 How Does Mass Spec Work?
12 TAMU: PROTEOMICS SPECTRA 4 What Do the Data Look Like?
13 TAMU: P ROTEOMICS S PECTRA Learning: Spotting the Samples 5
14 TAMU: P ROTEOMICS S PECTRA 6 What the Guts Look Like
15 TAMU: P ROTEOMICS S PECTRA 7 Taking Data
16 TAMU: PROTEOMICS SPECTRA 8 Some Other Common Steps Fractionating the Samples Changing the Laser Intensity Working with Different Matrix Substrates
17 TAMU: PROTEOMICS SPECTRA 9 SELDI: A Special Case Precoated surface performs some preselection of the proteins for you. Machines are nominally easier to use.
18 TAMU: PROTEOMICS SPECTRA 10 A Tale of Two Examples Example 1 : Learning from the literature Example 2 : Testing out our understanding A story in pictures
19 TAMU: PROTEOMICS SPECTRA 11 Example 1: Feb Lancet 100 ovarian cancer patients 100 normal controls 16 patients with benign disease
20 TAMU: PROTEOMICS SPECTRA 11 Example 1: Feb Lancet 100 ovarian cancer patients 100 normal controls 16 patients with benign disease Use 50 cancer and 50 normal spectra to train a classification method; test the algorithm on the remaining samples.
21 TAMU: PROTEOMICS SPECTRA 12 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 Large sample sizes, using serum
22 TAMU: PROTEOMICS SPECTRA 13 The Data Sets 3 data sets on ovarian cancer Data Set 1 : The initial experiment. 216 samples, baseline subtracted, H4 chip
23 TAMU: PROTEOMICS SPECTRA 13 The Data Sets 3 data sets on ovarian cancer Data Set 1 : The initial experiment. 216 samples, baseline subtracted, H4 chip Data Set 2 : Followup: the same 216 samples, baseline subtracted, WCX2 chip
24 TAMU: PROTEOMICS SPECTRA 13 The Data Sets 3 data sets on ovarian cancer Data Set 1 : The initial experiment. 216 samples, baseline subtracted, H4 chip Data Set 2 : Followup: the same 216 samples, baseline subtracted, WCX2 chip Data Set 3 : New experiment: 162 cancers, 91 normals, baseline NOT subtracted, WCX2 chip
25 TAMU: PROTEOMICS SPECTRA 13 The Data Sets 3 data sets on ovarian cancer Data Set 1 : The initial experiment. 216 samples, baseline subtracted, H4 chip Data Set 2 : Followup: the same 216 samples, baseline subtracted, WCX2 chip Data Set 3 : New experiment: 162 cancers, 91 normals, baseline NOT 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 consistency of the proteins found
26 TAMU: PROTEOMICS SPECTRA 14 We Can t Replicate their Results (DS1 & DS2)
27 TAMU: PROTEOMICS SPECTRA 15 Some Structure is Visible in DS1
28 TAMU: PROTEOMICS SPECTRA 16 Or is it? Not in DS2
29 TAMU: PROTEOMICS SPECTRA 17 Processing Can Trump Biology (DS1 & DS2)
30 TAMU: PROTEOMICS SPECTRA 18 We Can Analyze Data Set 3!
31 TAMU: PROTEOMICS SPECTRA 19 Do the DS2 Peaks Work for DS3?
32 TAMU: PROTEOMICS SPECTRA 20 Do the DS3 Peaks Work for DS2?
33 TAMU: PROTEOMICS SPECTRA 21 Which Peaks are Best? T-statistics Note the magnitudes: t-values in excess of 20 (absolute value)!
34 TAMU: PROTEOMICS SPECTRA 22 One Bivariate Plot: M/Z = (435.46,465.57) Perfect Separation. These are the first 2 peaks in their list, and ones we checked against DS2.
35 TAMU: PROTEOMICS SPECTRA 23 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.
36 TAMU: PROTEOMICS SPECTRA 24 Perfect Classification with Noise? This is a problem, in that it suggests a qualitative difference in how the samples were processed, not just a difference in the biology. This type of separation reminds us of what we saw with benign disease.
37 TAMU: PROTEOMICS SPECTRA 25 Mass Accuracy is Poor? A tale of 5 masses... Feb 02 Apr 02 Jun 02 DS1 DS2 DS3-7.86E E E E E E E E E
38 TAMU: PROTEOMICS SPECTRA 26 How are masses determined? Calibrating known proteins
39 TAMU: PROTEOMICS SPECTRA 27 Calibration is the Same? M/Z vectors the same for all three data sets. Machine calibration the same for 4+ months?
40 TAMU: PROTEOMICS SPECTRA 28 What is the Calibration Equation? The Ciphergen equation m/z U = a(t t 0) 2 + b, U = 20K, t = (0, 1,...) 0.004
41 TAMU: PROTEOMICS SPECTRA 28 What is the Calibration Equation? The Ciphergen equation m/z U = a(t t 0) 2 + b, U = 20K, t = (0, 1,...) Fitting it here a = , b = 0, t 0 =
42 TAMU: PROTEOMICS SPECTRA 28 What is the Calibration Equation? The Ciphergen equation m/z U = a(t t 0) 2 + b, U = 20K, t = (0, 1,...) Fitting it here a = , b = 0, t 0 = These are the default settings that ship with the software!
43 TAMU: PROTEOMICS SPECTRA 29 Other issues Q-star data different clinical trials?
44 TAMU: PROTEOMICS SPECTRA 30 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 but the site has been retired. Send to Ned Patz or Mike Campa at Duke if interested.
45 TAMU: PROTEOMICS SPECTRA 31 Raw Spectra Have Different Baselines Note the need for baseline correction.
46 TAMU: PROTEOMICS SPECTRA 32 Oscillatory Behavior... Roughly half the spectra have sinusoidal noise.
47 TAMU: PROTEOMICS SPECTRA 32 Oscillatory Behavior... Roughly half the spectra have sinusoidal noise. We re seeing the A/C power cord.
48 TAMU: PROTEOMICS SPECTRA 33 Baseline Adj: Fraction Agreement, Before & After
49 TAMU: PROTEOMICS SPECTRA 34 Fractionation is Unstable
50 TAMU: PROTEOMICS SPECTRA 35 Unfractionating the Data
51 TAMU: PROTEOMICS SPECTRA 36 The Overall Average Shows Spikes. Difference It.
52 TAMU: PROTEOMICS SPECTRA 37 Computer Buffer? Spike spacing has a wavelength of 4096 = 2 12.
53 TAMU: PROTEOMICS SPECTRA 38 Are We Done Cleaning Yet? Give the problem a chance to be easy, try some simple clustering.
54 TAMU: PROTEOMICS SPECTRA 39 PCA Splits off Half the Normals
55 TAMU: PROTEOMICS SPECTRA 40 Peaks at Integer Multiples of M/Z 180.6! This suggests a polymer. No Amino Acid dimers fit.
56 TAMU: PROTEOMICS SPECTRA 41 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 per spectrum.
57 TAMU: PROTEOMICS SPECTRA 42 Find the Best Separators Peaks MD P-Value Wrong LOOCV , , , , , , There are 9 values that recur frequently, at masses of 3077, 4069, 5825, 6955, 8840, 12886, 17318, 61000, and P-values are not from table lookups!
58 TAMU: PROTEOMICS SPECTRA 43 Testing Reality (Significance) Generate a bunch of random noise data matrices, each in size. For each matrix, split the 41 noise samples into groups of 24 and 17. Repeat our search procedure on the random data, and see how well we can separate things.
59 TAMU: PROTEOMICS SPECTRA 44 The Eyeball Test We applied one last filtering step and actually looked at the regions identified. All 9 peaks listed above passed the eye test. Blue lines = Cancers Red lines = Controls
60 TAMU: PROTEOMICS SPECTRA 45 Punchlines There is no magic bullet here. (Too bad)
61 TAMU: PROTEOMICS SPECTRA 45 Punchlines There is no magic bullet here. (Too bad) Data preprocessing is extremely important with this type of data, and there is still much room for improvement.
62 TAMU: PROTEOMICS SPECTRA 45 Punchlines There is no magic bullet here. (Too bad) Data preprocessing is extremely important with this type of data, and there is still much room for improvement. Dimension reduction is critical; both to avoid spurious structure and to focus our attention on peaks.
63 TAMU: PROTEOMICS SPECTRA 45 Punchlines There is no magic bullet here. (Too bad) Data preprocessing is extremely important with this type of data, and there is still much room for improvement. Dimension reduction is critical; both to avoid spurious structure and to focus our attention on peaks. There is structure in this data (some peaks have been confirmed, and the writeup is in progress) and it can be found!
64 TAMU: PROTEOMICS SPECTRA 45 Punchlines There is no magic bullet here. (Too bad) Data preprocessing is extremely important with this type of data, and there is still much room for improvement. Dimension reduction is critical; both to avoid spurious structure and to focus our attention on peaks. There is structure in this data (some peaks have been confirmed, and the writeup is in progress) and it can be found!
65 TAMU: PROTEOMICS SPECTRA 46 Other Stuff We were the only ones to notice the sinusoidal noise.
66 TAMU: PROTEOMICS SPECTRA 46 Other Stuff We were the only ones to notice the sinusoidal noise. and the clock tick.
67 TAMU: PROTEOMICS SPECTRA 46 Other Stuff We were the only ones to notice the sinusoidal noise. and the clock tick. and we also won the competition...
68 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial
69 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial Randomization Uniform handling of samples Blinding
70 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial Randomization Uniform handling of samples Blinding Careful Pre-Processing of Data is Essential
71 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial Randomization Uniform handling of samples Blinding Careful Pre-Processing of Data is Essential Calibration Baseline Correction Normalization
72 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial Randomization Uniform handling of samples Blinding Careful Pre-Processing of Data is Essential Calibration Baseline Correction Normalization Exploratory Data Analysis is Important: Look at the Data!
73 TAMU: PROTEOMICS SPECTRA 47 Important Lessons Experimental Design Issues are Crucial Randomization Uniform handling of samples Blinding Careful Pre-Processing of Data is Essential Calibration Baseline Correction Normalization Exploratory Data Analysis is Important: Look at the Data! Search for anomalies Confirm numerical results
74 TAMU: PROTEOMICS SPECTRA 48 References On the Lancet data: Baggerly, Morris and Coombes (2003), accepted by Bioinformatics pending revisions. On the Proteomics Data Mining Conference data: Baggerly, Morris, Wang, Gold, Xiao and Coombes (2003), Proteomics, 3(9): pdf preprints are available.
75 TAMU: PROTEOMICS SPECTRA 49 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
76 TAMU: PROTEOMICS SPECTRA 50 Collaborators Keith Baggerly Kevin Coombes Jing Wang David Gold Lian-Chun Xiao ********************* Ryuji Kobayashi David Hawke John Koomen
Short Communication. ª 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Proteomics 2003, 3, DOI /pmic
Proteomics 2003, 3, 1667 1672 DOI 10.1002/pmic.200300522 1667 Short Communication Keith A. Baggerly Jeffrey S. Morris Jing Wang David Gold Lian-Chun Xiao Kevin R. Coombes Department of Biostatistics, UT
Keywords: calibration, data preprocessing, follow-up studies, proteomic mass spectrometry, randomised run order, validation
Jianhua Hu is Assistant Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center (UTMDACC). Her current research is focused on the development of biomarker panels from mass spectrometry
Keywords: calibration, data preprocessing, follow-up studies, proteomic mass spectrometry, randomised run order, validation
Jianhua Hu is Assistant Professor of Biostatistics at the University of Texas M. D. Anderson Cancer Center (UTMDACC). Her current research is focused on the development of biomarker panels from mass spectrometry
Received on July 14, 2003; revised on October 14, 2003; accepted on October 16, 2003 Advance Access publication January 29, 2004
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