SELDI-TOF Mass Spectrometry Protein Data By Huong Thi Dieu La

Transcription

1 SELDI-TOF Mass Spectrometry Protein Data By Huong Thi Dieu La

2 References Alejandro Cruz-Marcelo, Rudy Guerra, Marina Vannucci, Yiting Li, Ching C. Lau, and Tsz-Kwong Man. Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics, 24(19): , Robert Gentleman, Vincent Carey, Wolfgang Huber, Rafael Irizarry, and Sandrine Dudoit. Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health). Springer Science and Business Media, Inc, New York, first edition edition, Haleem J. Issaq, Timothy D. Veenstra, Thomas P. Conrads,, and Donna Felschow. The SELDI-TOF MS approach to proteomics: Protein profiling and biomarker identification. Biochemical and Biophysical Research Communications, 292: , 2002.

3 SELDI-TOF-MS Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry Used to profile protein markers from tissue or bodily fluids and thus identify biomarkers that can aid in diagnosis, prognosis or treatment. Application: psychiatric disease, renal function, cancer (pancreatic, prostate, ovarian, and breast)

4 SELDI-TOF-MS Components ProteinChip array Retain specific proteins from the sample Reader Measures the molecular weights of the retained proteins and generates a trace showing the relative abundance vs. the molecular weights of these proteins Software Identify differences in protein abundances between two samples

5 Source: re_issaq.pdf

6 Preparation Biological samples are processed via fractionation. Fractionation: the process of splitting the original sample into subsamples which contain proteins that are more homogeneous

7 EAM: Energy Absorbing Molecule Source:

8 Preprocessing of MS data Alignment of the spectra Filtering (Denoising) Baseline subtraction Normalization Peak Detection Clustering of peaks Peak quantification

9 SELDI-TOF-MS softwares ProteinChip Software 3.1 SpecAlign Cromwell PROcess MassSpecWAvelet

10 PROcess package Process a single spectrum Process a set of spectra

11 Process a single spectrum Baseline subtraction Peak detection

12 Baseline subtraction Purpose: To level off the elevated, non-constant baseline caused by the chemical noise in the EAM and by ion overload, thus, make different spectra compatible. Solution: Using local regression to estimate the bottom of a spectrum and then subtracting that estimate from a spectrum Two approaches: Fitting local regression to: The points below a certain quantile Local minima: yields better results when estimating the baseline.

13 Baseline Subtraction

14 Baseline subtraction: algorithm For each spectrum, find local minima by segmenting the m/z range. Fit a local regression to local minima for each spectrum Subtract the estimated baseline from each spectrum

15 ### Load libraries library(survival) library(icens) library(process) ### Read in the raw spectrum fdat <- system.file("test", package="process") fs <- list.files(fdat, pattern = "\\.*csv\\.*", full.names=true) f1 <- read.files(fs[1]) ### Plot the raw spectrum jpeg("f1.jpeg", width=480, height=480) plot(f1, type="l", xlab="m/z") title(basename(fs[1])) dev.off() ### Remove the baseline jpeg("f2.jpeg", width=480, height=480) bseoff <- bslnoff(f1, method="loess", bw=0.1, xlab="m/z", plot=true) title(basename(fs[1])) dev.off()

16 Peak detection Purpose: To detect peaks that represent the set of proteins that are differentially expressed between different samples.

17 Peak Detection: algorithm Smooth the spectrum using moving averages of k s nearest neighbors Compute local variability as the median of the absolute deviations of k v nearest neighbors. Identify local maxima of the smoothed spectrum using three thresholds: The signal to noise ratio: local smooth/local variability The detection threshold for the whole spectrum The shape ratio: the area under the curve within a small distance of a peak candidate/ maximum of all such peak areas of a spectrum

18 ### Peak detection jpeg("f3.jpeg", width=480, height=480) pkgobj <- ispeak(bseoff, span=81, sm.span=11, plot=true, zerothrsh=2, area.w=0.003, ratio=0.2) dev.off() ### Inspect peaks in a particular range of m/z values jpeg("f4.jpeg", width=480, height=480) speczoom(pkgobj, xlim=c(5000,10000)) dev.off()

19 Peak detection

20 Processing a set of calibration spectra Apply baseline subtraction Normalize spectra Cutoff selection Identify peaks Quality assessment Get proto-biomarkers

21 Example Data Set A set of 8 spectra from a calibration data set Same 5 proteins are present in the sample: 1084, 1638, 3496, 5807, 7034 amu

22 ### Read in the 8 spectra amu.cali <- c(1084,1638,3496,5807,7034) ### Plot 8 spectra and mark the protein positions by red vertical lines for each of them jpeg("f5.jpeg", width=1080, height=560) par(mfrow=c(2,4)) plotcali <- function(f, main, lab.cali){ x <- read.files(f) plot(x,main=main, ylim=c(0,max(x[,2])), type="n") abline(h=0, col="gray") abline(v=amu.cali, col="salmon") if(lab.cali) axis(3, at=amu.cali, labels=amu.cali, las=3, tick=false, col="salmon", cex.axis=0.94) lines(x) return(invisible(x)) } dir.cali <- system.file("calibration", package="process") files <- dir(dir.cali, full.names=true) i <- seq(along=files) mapply(plotcali, files, LETTERS[i], i <=2) dev.off()

23

24 Baseline subtraction Similar to baseline subtraction for a single spectra R code: Mcal <- rmbaseline(dir.cali, plot=true) head(mcal) peptidecalib_1_128.csv peptidecalib_1_16.csv

25 Normalize Spectra Purpose: reduce variation due to experimental noise Total ion normalization: Calculate each spectrum's area under the curve (AUC) for m/z values greater than the selected cutoff Scale all spectra to the median AUC Assumptions: The number of proteins being over-expressed is approximately equal to the number of proteins being under-expressed. The number of proteins whose expression levels change is small relative to the total number of proteins bound to the protein array surface

26 Cutoff selection Choose a cutoff point such that the magnitude of the noise is relatively stable above that point. Algorithm for a single cutoff point: Baseline-subtracted spectra within the group are normalized to the median of the sums of intensities of spectra The standard deviation of intensities at each m/z value is calculated The mean of those standard deviations is computed. Repeat for different cutoff points and Plot average standard deviations vs. cutoff points.

27 ### Cutoff selection cts <- round(10^(seq(2,4,length=14))) sdsfirst <- sapply(cts, avesd, Ma=Mcal) jpeg("f6.jpeg", width=480, height=480) par(mfrow=c(1,1)) plot(cts, sdsfirst, xlab="cutpoint", pch=21, bg="red", log="x", ylab="average sd") dev.off() ### Normalize spectra- cutoff point m/z=400 M.r <- renorm(mcal, cutoff=400)

28 Identify Peaks Similar to peak detection for a single baselineadjusted spectrum R Code ### Identify peaks peakfile <- "calipeak.csv" getpeaks(m.r, peakfile, ratio=0.1)

29 Quality Assessment Purpose: Identify and eliminate spectra of poor quality Based on 3 parameters: Quality: measure of separation of signal from noise Retain: the number of high peaks in a single spectrum Peak: the number of peaks in a spectrum relative to the average number of peaks of the whole set of spectra being considered Poor quality spectra: Quality < 0.4, Retain < 0.1, Peak <0.5.

30 Quality assessment: algorithm Estimate the noise by subtracting from each spectrum its moving average with a window size of 5 points. Calculate the noise envelope as 3 times the standard deviation of the noise in a 250 point window. Calculate the area under each spectrum A 0 Calculate the area after subtracting the noise envelope from the spectrum A 1 Obtain Quality, Retain, and Peak

31 Quality assessment: algorithm Quality: A1/A0 Retain: the number of points with height greater than 5 times noise envelope/ the total numbrer of points in the spectrum Peak: the number of peaks in each spectrum detected/ the average number of peaks for all spectra in a run

32 qualres <- quality(m.r, peakfile, cutoff=400) QualRes Quality Retain peak peptidecalib_1_128.csv peptidecalib_1_16.csv peptidecalib_1_2.csv peptidecalib_1_256.csv peptidecalib_1_32.csv peptidecalib_1_4.csv peptidecalib_1_64.csv peptidecalib_1_8.csv

33 Get Proto-biomarkers Peak alignment: peaks across spectra that are likely to represent the same protein. Proto-biomarkers: peaks aligned across spectra To obtain a proto-biomarker: Generate an interval around each peak that is centered at the m/z value for the peak (0.3%) Determine which actual peaks are represented by a proto-biomarker Use the maximum value as the height of that proto-biomarker

34 ### Get proto-biomarkers bmkfile <- "calibmk.csv" bmk1 <- pk2bmkr(peakfile, M.r, bmkfile, p.fltr=0.5) mk1 <- round(as.numeric(gsub("m", "", names(bmk1)))) mk1 ### [1] jpeg("f7.jpeg", width=1080, height=560) par(mfrow=c(2,4)) plotcali2 <- function(...){ x <- plotcali(...) lines(x[,1]*2, x[,2]+25, col="blue") } mapply(plotcali2, files, LETTERS[i], i <=2) dev.off()

35

36 Analyze the result 5 known proteins: 1084, 1638, 3496, 5807, 7034 Obtained 4 proto-biomarkers: 2906, 3498, 5812, and 7036 Within 0.3% of m/z values of known proteins: 3498, 5812, and 7036 Result of larger proteins with two charges: 2x2906 (5807) and 2x3496 (7034) Failed to detect peaks at m/z=1084 and 1638

37 Summary PROcess package: Process SELDI-TOF-MS data Advantage: produce more producible results regarding peak quantification Limitation: The results were not homogeneous across laser intensities