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1. SD Depending upon your data and your interpretation of the results you may decide that some samples should be discarded in which case you can use removeSample as previously described then repeat the PCA analysis The next step for most people is to determine the number of PCs needed to describe the data This is usually done with a scree plot as shown in Figure 15 As of v 1 51 there is an alternate style scree plot which actually think is much more informative 2 A SAMPLE EXPLORATION S repens IR Spectra PCA Score Plot centered noscale classical robust ellipses by group PC2 score 3 3 PC1 score 93 Figure 11 Classical PCA SV_EPO 22 2 A SAMPLE EXPLORATION S repens IR Spectra PCA Score Plot l1median noscale robust robust ellipses by group PC2 score 19 PC1 score 68 Figure 12 Robust PCA 23 2 A SAMPLE EXPLORATION Possible PCA Outliers based on Orthogonal Distance orthogonal distance 5 10 15 Serenoa repens IR quality study centered noscale classical 2 PCs Figure 13 Diagnostics Orthogonal Distances 24 2 A SAMPLE EXPLORATION Possible PCA Outliers based on Score Distance score distance esv EPO 5 10 15 Serenoa repens IR quality study centered noscale classical 2 PCs Figure 14 Diagnostics Score Distances 25 2 A SAMPLE EXPLORATION 26 S repens IR Spectra Scree Plot o g sed zsa D E lees2. o d o ME Q le La I le e e e i 704 eo o eeeee i 1702704 30 centered noscale classical T T T 0 03 0 02 0 01 0 00 0 01 0 02 covariance Figure 21 s Plot to Identify Influential Frequencies However a potentially more useful approach is to use an s plot to determine which variables have the greatest influence A standard loadings plot plotLoadings shows you which frequency ranges contribute to which principal components but the plot allows the vertical axis to be free Unless you look at the y axis scale you get the impression that the loadings for principal component 1 etc all contribute equally The function sPlotSpectra plots the correlation of each frequency variable with a particular score against the covariance of that frequency variable with the same score The result is an s shaped plot with the most influential frequency variables in the upper right hand and lower left quadrants An example is shown in Figures 21 and 22 In the latter figure you can clearly see the influence of the carbonyl peaks This method was reported in Wiklund et a 12 spt lt sPlotSpectra SrE3 IR class title 3 pc 1 tol 0 001 spt lt sPlotSpectra SrE3 IR class title 5 pc 1 tol 0 05 xlim c 0 04 0 01 ylim c 1 05 0 9 Finally you can blend the ideas of PCA and HCA Since PCA eliminates the noise in a data set after you have selected the important PCs you can carry out HCA on t
3. classical PCA no scaling autoscaling Pareto scaling classPCA robust PCA no scaling median absolute deviation robPCA Diagnostics OD plots pcaDiag SD plots pcaDiag Choosing the correct no of PCs scree plot plotScree alternate style scree plot plotScree2 bootstrap analysis classical PCA only pcaBoot Score plots plotting options 2D plots robust or classical confidence ellipses plotScores 3D plots static 3D plots plotScores3D interactive 3D plots plotScoresRGL interactive multivariate plots plotScoresG Loading plots loadings vs frequencies plotLoadings loadings vs other loadings plot2Loadings s plot correlation vs covariance sPlotSpectra Other HCA of PCA scores hcaScores ANOVA PCA aovPCA 2 5 Principal Components Analysis Principal components analysis PCA from now on is the real workhorse of exploratory data analysis It makes no assumptions about group membership but clustering possibly in high dimensions of the resulting sample scores can be very helpful in understanding your data The theory and practice of PCA is covered well elsewhere Chapter 3 of 2 is an excellent choice Here we ll concentrate on using the PCA methods in ChemoSpec Briefly however you can think of PCA as determining the minimum number of components necessary to describe a data set in effect removing noise Think of a typical spectrum some regions are clearly just noise Further a typical spectroscopi
4. Analytical Chemistry vol 80 no 1 pp 115 122 2008 PMID 18027910 P Harrington N Vieira J Espinoza J Nien R Romero and A Yergey Analysis of variance principal component analysis A soft tool for proteomic discovery ANALYTICA CHIMICA ACTA vol 544 no 1 2 pp 118 127 2005 R C Pinto V Bosc H Nocairi A S Barros and D N Rutledge Using ANOVA PCA for discriminant analysis Application to the study ofmid infrared spectra of carraghenan gels as a function of concentration and temperature ANALYTICA CHIMICA ACTA vol 629 no 1 2 pp 47 55 2008 C Fraley and A Raftery mclust Model Based Clustering Normal Mixture Modeling 2009 R package version 3 4 C Fraley and A E Raftery Model based clustering discriminant analysis and density estimation Journal of the American Statistical Association vol 97 no 458 pp 611 631 2002 M V Bravington mvbutils Workspace organization code and documentation editing package prep and editing etc 2011 R package version 2 5 101 C T Butts sna Tools for Social Network Analysis 2010 R package version 2 2 0 T Krastev spectrino software Spectra visualization and preparation for R Journal of Statistical Software vol 18 no 10 pp 1 16 2007 K M Mullen and I H M van Stokkum TIMP an R package for modeling multi way spectroscopic measurements Journal of Statistical Software vol 18 no 3 2007 J L Izqu
5. Set median iqr 0 05 L 0 00 gt L T T T 1000 2000 3000 4000 wavenumber Figure 5 Checking for Regions of No Interest Finding these regions might be pretty simple a matter of inspection coupled with your knowledge of spectroscopy Another approach is to use the function specSurvey to examine the entire set of spectra This function computes a summary statistic your choice of the intensities at a particular frequency across the data set and plots this against frequency Regions where there is not much variation in the intensity will show up as unvarying baseline and these regions are candidates for removal Figure 5 demonstrates the process specSurvey SrE2 IR method igr title repens Extrac Spectra by gr FALSE In Figure 5 we kept all the groups together by using argument by gr FALSE We also looked at the entire spectral range In Figure 6 we can look just at the carbonyl region The black line is the median value of intensity across the entire set of spectra The red lines are the upper and lower interquartile ranges which makes it pretty clear that the carbonyl region of this data set varies a lot specSurvey SrE2 IR method oe eE E S opens Detai f Carbor F om by gr FALSE xlim c 1650 1800 Finally specSurvey allows us to view the data set by group which is really more useful Let s look at the carbonyl region by group Figure 7 Note that we get warnings because two of the
6. are the necessary commands 2 A SAMPLE EXPLORATION 27 S repens IR Spectra Scores by PC cumulative percent variance shown to right of PC o o 93 96 98 99 o 99 100 100 100 100 100 o 8 i i b i i ees 8 scores o centered noscale classical component Figure 16 Alternate Style Scree Plot 2 A SAMPLE EXPLORATION Bootstrap Analysis of Number of PCs Explained variance 0 90 0 95 0 85 0 80 Serenoa repens IR quality study centered noscale classical 1 2 3 4 5 Number of components Figure 17 Bootstrap Analysis for No of PCs 28 2 A SAMPLE EXPLORATION 29 S repens IR Spectra PCA Score Plot PC1 93 PC3 1 8 PC2 3 3 adSrE EPO 00 psrE Serenoa repens IR quality study Figure 18 Plotting Scores in 3D using plotScores3D plotScoresRGL SrE3 IR class title S R Spectra leg pos t pos B not run it s interactive For full details of course take a look at the manual page plotScoresRGL If you want a similar and probably more publication worthy plot you can use plotScores3D as shown in Figure 18 In this example we ve set ellipse FALSE because the adSrE data points form a very elongated ellipse which sets the plotting limits in such a way that other points become very hard to see plotScores3D SrE3 IR class title S neS g ellipse FALSE Finally you can install the program GGobi and the package rggobi and use plo
7. case This is readily fixed by supplying the xlim argument in the desired order e g xlim c 1800 1650 in the previous example Second the vertical scale in these examples is absorbance When using IR for structural elucidation the vertical axis is typically T with the peaks pointing downward You don t have that choice in ChemoSpec because the absorbance mode is the appropriate one for chemometrics Record your original spectra that way and get used to it The argument which in plotSpectra takes a numerical list of the spectra you wish to plot you can think of this as the row number if you imagine each spectra to be a row in a matrix with intensities in the columns with each column corresponding to a particular frequency value You may be wondering how to determine which particular sample is in each row This is best accomplished with a grep command For instance if you wanted to know what row sample the olive oil was in the following methods would locate it for you SrE IR names suitable if there are not many spectra 1 CVS_adSrE ET_pSrE GNC_adSrE 4 LF_adSrE MDB_pSrE NA_pSrE T Nat_adSrE NP_adSrE NR_pSrE 10 NSI_adSrE NW_adSrE SN_adSrE 13 Sol_adSrE SV_EPO UiDwad SEN 16 TJ 00 grep 00 SrE IR names use if there are more spectra 1 16 See the discussion in the next section for more details on using grep effectively 2 2 2 Identifying amp Removing Problema
8. color red3 and sspB csv will be assigned dodgerblue4 see the help file for some thinking ahead about colors colorSymbol at the console After running this command a new file called subspecies RData will be in your directory and you can access the data set and give it whatever name you like as follows SubspeciesNMR lt loadObject ta Now it is ready to use Working with Chromatograms While all the language in this vignette and in the package are geared toward analysis of spectra ChemoSpec can also work with chromatograms as the raw data In this case time replaces frequency of course but other than that the analysis is virtually the same So the only real difference is when you issue the command getManyCsv you will give the frequency unit along these lines freq unit time minutes Built in Data Sets ChemoSpec ships with several built in data sets SrE IR is the set used for this vignette It is composed of a collection of 14 IR spectra of essential oil extracted from the palm Serenoa repens or Saw Palmetto which is commonly used to treat BPH in men The 14 spectra are of different retail samples and are divided into two categories based upon the label description adSrE adulterated extract and pSrE pure extract The adulterated samples typically have olive oil added to them which is inactive towards BPH There are two additional spectra included as references outliers evening primrose oil labeled EPO in the data s
9. into submatrices Each submatrix corresponds to a particular factor and the rows of the submatrix have been replaced by the average spectrum of each level of the factor The original data set is thought of as a sum of these submatrices plus residual error The residual error is added back to each submatrix and then PCA is performed This is conceptually illustrated in Figures 23 and ANOVA PCA has been implemented in ChemoSpec via the functions aovPCA aovPCAscores and aovPCAloadings The idea here is that if a factor is significant there will be separation along PC1 in a plot of PC1 vs PC2 Unfortunately there are not enough groups and levels within the SrE IR data set to carry out ANOVA PCA However the help page for aovPCA contains an example using the CuticleIR data set which illustrates how to carry out the analysis It also demonstrates another useful function splitSpectraGroups which allows you to take an existing group designation and split into new designations See gt aovPCA 3 FUNCTIONS THAT ARE NOT DISCUSSED HERE 35 2 7 Model Based Clustering Using mclust PCA and HCA are techniques which are unsupervised and assume no underlying model HCA computes distances between pairs of spectra and groups these in an iterative fashion until the dendrogram is complete PCA seeks out components that maximize the variance While in PCA one often and ChemoSpec does displays the samples coded by their group membership this information is not ac
10. set The y axis unit is absorbance HH The frequency scale runs from 399 2 to 4002 wavenumber There are 1869 frequency x axis data points The frequency resolution is 1 929 wavenumber point HH HH The spectra are divided into 4 groups HH group no color symbol alt sym 1 adSrE 10 984EA3 15 d 2 EPO 1 377EB8 2 b 3 00 1 4DAF4A 3 G 4 pSrE 4 E41A1C 1 a HH Note this data is an S3 object of class Spectra sumSpectra provides several pieces of information and we ll discuss some of them as we go along 2 2 1 Plotting the Spectra Assuming that everything looks good so far it s time to plot the spectra and inspect them A good practice would be to check every spectrum for artifacts and other potential problems As of v 1 50 there is a function LoopThruSpectra which will take the pain out of inspecting quite a few spectra See the help page for details However for plotting just a few spectra you should use plotSpectra A basic plot is shown in Figure 2 In this case we have chosen to plot one spectrum from each category Note that the carbonyl and CspoTH regions are clearly different in these samples We ll make a fancy proper title just this once myt lt expression paste italic S phantom 0 italic repens Extract IR Spectra plotSpectra SrE IR title myt which c 1 2 14 16 yrange c 0 1 6 offset 0 4 lab pos 2200 Depending upon the intensity rang
11. while was on sabbatical and was aided greatly by an award of a Fisher Fellowship These programs are coordinated by the Faculty Development Committee at DePauw and am very grateful to them as well as the individuals who originally created these programs One of my student researchers Kelly Summers took the data included in CuticleIR in the summer of 2009 as part of a preliminary study Prof Dan Raftery at Purdue University provided an NMR data set not included here which was very helpful in troubleshooting functions am also grateful to Prof Peter Filzmoser who answered a number of my questions related to the algorithms in his chemometrics package Finally Roberto Canteri of the Fundazione Bruno Kessler Italy brought some small bugs to my attention and made some good suggestions for improving the underlying code am grateful to Roberto for assistance 6 The Competition Several other packages exist which do some of the same tasks as ChemoSpec and do other things as well spectrino is a GUI interface that runs only under the Windows OS 19 It runs as a separate program in communication with R It is oriented mainly toward processing and organizing spectral data prior to statistical analysis hyperspec is a very nice package that appeared while was developing ChemoSpec and it does many of the same things as ChemoSpec and a few more It is written using S4 classes which proves that Claudia Beleites is a better programmer than me TIMP i
12. working perfectly You are on your own If you plan to use rggobi and GGobi to view the data later keep in mind that GGobi only uses certain color schemes al though there are many options and in interactive operation plots on a black background In the case of ChemoSpec two particular options have been hard coded into the function plotScoresG for simplicity If you plan to use plotScoresG you may wish to choose from one of these two color schemes before you begin if you want all your graphics to use the same scheme Keep in mind that these colors must be used in order though you can use the order of argument gr crit to associate a particular group with a particular color primary scheme c red3 dodgerblue4 forestgreen purple4 orangered yellow orangered4 violetred2 pastel scheme c seagreen brown2 skyblue2 hotpink3 chartreuse3 darkgoldenrod2 light salmon3 gray48 2 A SAMPLE EXPLORATION 6 2 2 Preliminary Inspection of Data One of the first things you should do and this is very important is to make sure your data are in good shape First you can summarize the data set you created and verify that the data ranges etc look like you expect them to data SrE IR makes the data available sumSpectra SrE IR No gaps were found by check4Gaps No plot will be made HH Serenoa repens IR quality study HH There are 16 spectra in this
13. ChemoSpec An R Package for Chemometric Analysis of Spectroscopic Data and Chromatograms Package Version 1 51 0 Bryan A Hanson e mail hanson depauw edu with contributions from Matt J Keinsley DePauw University Department of Chemistry amp Biochemistry Greencastle Indiana USA github com bryanhanson ChemoSpec CRAN R project org package ChemoSpec May 23 2012 Abstract ChemoSpec I is a collection of functions for plotting spectra NMR IR etc as well as chromatograms and carrying out various forms of top down exploratory data analysis such as hierarchical cluster analysis HCA principal components analysis PCA and model based clustering Robust methods appropriate for this type of high dimensional data are employed ChemoSpec is designed to facilitate comparison of samples from treatment and control groups It is designed to be user friendly and suitable for people with limited background in R This vignette gives some background on ChemoSpec and takes the reader through a typical workflow The development of ChemoSpec has been generously supported by DePauw University in the form of sabbatical funding and a Fisher Fellowship Thanks CONTENTS 2 Contents 2 3 oe epost ee eo eee 3 ban ee a e Ue 6 6 2 2 2 Identifying amp Removing Problematic Samples 2 eee a 9 2 2 3 Removing Groups gt o sa ko A RRR Re RE Re a 10 2 2 4 Correcting Baseline Drift 0 00 10 2 2 5 Identifying amp
14. E3 IR freq SrE3 IR data 1 plot TRUE beg freq end freq size beg indx end indx 1 399 2 1799 1400 al 727 2 2501 3 4002 1500 728 1506 2 3 Data Pre Processing Options There are a number of data pre processing options available for your consideration The main choices are whether to normalize the data whether to bin the data and whether to scale the data Data scaling is handled by the PCA routines see Section 2 5 Normalization is handled by the normSpectra function Usually one normalizes data in which the sample preparation procedure may lead to differences in concentration such as body fluids that might have been diluted during handling or that vary due to the physiological state of the organism studied The SrE IR data set is taken by placing the oil extract directly on an ATR device and no dilution is possible so normalization probably isn t really appropriate Currently there is only one means of normalizing and that is to divide each point frequency in a spectrum by the sum of all points in that spectrum Other means of normalizing can be readily added if they affect all points in the same way Normalization is accomplished by the following code which is not run SrE3 IR lt normSpectra SrE3 IR But remember this doesn t make sense for this data set The literature contains a number of useful discussions about normalization issues 2 Another type of pre processing that you may wish to consider is binning or buck
15. Removing Regions of No Interest 10 2 3 Data Pre Processing Options oa aaa 16 2 4 Hierarchical Cluster Analysis 2 2 0020 18 2 5 Principal Components Analysis 0 0 20 2 6 ANOVA PCA 2 0 2 ee 34 2 7 Model Based Clustering Using mclust LL 35 3 Functions That Are Not Discussed Here 35 Technical Background 39 Acknowledgements 40 The Competition 40 References 40 1 Introduction Chemometrics as defined by Varmuza and Filzmoser 2 is the extraction of relevant information from chemical data by mathematical and statistical tools This is an appropriately broad definition considering the wealth of questions and tasks that can be treated by chemometric approaches In our case the focus is on spectral data sets which typically have many variables frequencies and relatively few samples Such multivariate high p low n data sets present some algorithmic challenges but these have been addressed by knowledgeable folks In particular for both the practical and theoretical background to multivariate chemometric analysis strongly recommend the Varmuza Filzmoser book Some of the functions described here are not much more than wrappers for the functions they and others have made available to the R community in their packages ChemoSpec was developed for the chemometric analysis of spectroscopic data such as UV Vis NMR or IR data it also works with chromatographic data see below The approach is entir
16. a i plotLoadings aovPCAloadings plotSpectra baselineSpec plotScoresCor sumSpectra hcaSpectra hoh isWholeNo pcaBoot hmapSpectra rowDist An hypTestScores avgFacLvis Figure 28 Map of Functions in ChemoSpec 4 Technical Background ChemoSpec is written entirely in R there is no compiled code Hence it should be platform independent please let me know if you discover otherwise ChemoSpec uses S3 classes under the hood because frankly they were much faster to write For the pros and cons of classes and object oriented programming in R see the help archives search my name for one thread and some really interesting replies from the big dogs ChemoSpec employs several different graphics packages the choice was one of practicality In general tried to make all the graphics output look similar for consistency In understanding the operation of a package it is useful to know how the functions relate to each other i e which functions call each other These relationships are readily visualized with a diagram like Figure 28 This was generated by first using the foodweb function in package mvbutils 17 then removing the links to function chkSpectra which generates a lot of clutter since nearly all functions in ChemoSpec call it Finally the resulting adjacency matrix was converted into a graph by gplot in package sna 18 5 ACKNOWLEDGEMENTS 40 5 Acknowledgements The development of ChemoSpec began
17. c peak spans quite a few frequency units as the peak goes up tops out and then returns to baseline Any one of the points in a particular peak describe much the same thing namely the intensity of the peak Plus each frequency within a given peak envelope is correlated to every other frequency in the envelope they rise and fall in unison as the peak changes size from sample to sample PCA can look past all the noise and underlying correlation in the data set and boil the entire data set down to essentials Unfortunately the principal components that are uncovered in the process don t correspond to anything concrete usually Again you may wish to consult a more detailed treatment Table 1 gives an overview of the options available in ChemoSpec and the relevant functions There s quite a bit of choice here let s work through an example and illustrate or at least mention the options as we go Keep in mind that it s up to you to decide how to analyze your data Most people try various options and follow the ones that lead to the most insight But the decision is yours The first step is to carry out the PCA You have two main options either classical methods or robust methods Classical methods use all the data you provide to compute the scores and loadings Robust methods focus on the core or heart of the data which means that some samples may be downweighted This difference is important and the results from the two methods may be
18. choose colors for you automatically In addition to colors Spectra objects also contain a list of symbols and alternative symbols These are useful for plotting in black and white or when color blind individuals will be viewing the plots The alternative symbols are simply lower case letters as these are needed for plotScoresRGL and other rgl graphics driven functions which cannot plot traditional symbols An issue to keep in mind is that R plots are generally on a white background so pale colors should be avoided while GGobi which is used by function plotScoresG plots on a black background interactively so dark colors should be avoided Hence some compromise is necessary Two recommended color schemes are shown in Figure 1 By name these are 2 A SAMPLE EXPLORATION 5 ChemoSpec Primary Scheme ChemoSpec Pastel Scheme Figure 1 Recommended Color Sets in ChemoSpec primary scheme c red3 dodgerblue4 forestgreen purple4 orangered yellow orangered4 violetred2 pastel scheme c seagreen brown2 skyblue2 hotpink3 chartreuse3 darkgoldenrod2 light salmon3 gray48 Finally the current color scheme of a Spectra object may be determined using sumSpectra or changed using conColScheme NOTE about the next section as of December 2010 GGobi is on life support and the communication of color information between ChemoSpec and GGobi is not
19. cture these are also used in evolution and systematics where they are called cladograms The details behind HCA can be readily found elsewhere Chapter 6 of 2 is a good choice With ChemoSpec you have access to any of the methods available for computing distances between samples and any of the methods for identifying clusters A typical example is shown in Figure 10 hcaSpectra SrE3 IR title S rey IR Spectra The result is a dendrogram The vertical scale represents the numerical distance between samples Not unexpectedly the two reference samples which are known to be chemically different cluster together separately from all other sam ples Perhaps surprisingly the various pure and adulterated oil extracts do not group together precisely The function hcaScores does the same kind of analysis using the results of PCA rather than the raw spectra It is discussed in the next section 2 A SAMPLE EXPLORATION 1 5 1 0 0 5 0 0 SV_EPO S repens IR Spectra HCA Analysis 19 Key adSrE EPO pSrE H j TJ_OO NP_adSrE NR_pSrE NW_adSrE GNC_adSrE NA_pSrE ET _pSrE MDB_pSrE LF_adSrE NSI_adSrE TD_adSrE SN_adSrE Sol adSrE Nat adSrE CVS adSrE clustering method complete distance method euclidean Figure 10 Hierarchical Cluster Analysis 2 A SAMPLE EXPLORATION 20 Table 1 Principal Components Analysis Options amp Functions PCA options scaling options function
20. e is a function removeGroup which will remove samples belonging to a particular group in Spectra groups 2 2 4 Correcting Baseline Drift As of v 1 50 ChemoSpec contains a function to correct wandering baselines The function baselineSpec can operate interactively or not Figure 4 shows a typical usage Method rfbaseline works well for IR spectra retC TRUE puts the corrected spectra into the new Spectra object so we can use it going forward and we will SrE2 IR lt baselineSpec SrE IR int FALSE method rfbaseline retC TRUE 2 2 5 Identifying amp Removing Regions of No Interest Many spectra will have regions that should be removed lt may be an uninformative interfering peak like the water peak in HNMR or the CO peak in IR Or there may be regions of the spectra that simply don t have much information they contribute a noisy baseline and not much else An example would be the region from about 1 800 or 1 900 cm to about 2 500 cm in IR a region where there are typically no peaks except for the CO stretch and rarely be careful alkyne stretches 2 A SAMPLE EXPLORATION 11 0 10 0 20 0 30 0 00 0 10 0 20 0 30 0 00 Original spectrum I I I 0 500 1000 1500 Baseline corrected spectrum 0 500 1000 1500 Figure 4 Correcting baseline drift 2 A SAMPLE EXPLORATION 12 S repens Extract IR Spectra I 0 30 L 0 25 L 0 20 gt L 0 15 L 0 10 gt L Full Data
21. e of your data set and the number of spectra to be plotted you have to manually adjust the arguments yrange offset and amplify but this usually only takes a few iterations Keep in mind that offset and amplify are multiplied in the function so if you increase one you may need to decrease the other Suppose that you wanted to focus just on the carbonyl region of these spectra you can add an argument called xlim To demonstrate let s look at fewer spectra and at higher amplitude so we can see details as shown in Figure 3 plotSpectra SrE IR title S repens IR Spectra Detail of Carbonyl Regi which c 1 2 14 16 xlim c 1650 1800 yrange c 0 0 6 offset 0 1 lab pos 1775 These sample plots display the IR spectra in two ways that may be upsetting to some readers First the x axis is 2 A SAMPLE EXPLORATION absorbance 1 5 1 0 0 5 0 0 S repens Extract IR Spectra TJ_OO SV_EPO ET_pSrE MICA CVS_adSrE IA 1000 2000 3000 4000 wavenumber Figure 2 Plotting Spectra 2 A SAMPLE EXPLORATION S repens IR Spectra Detail of Carbonyl Region 0 6 0 5 0 4 TJ 00 L SV EPO absorbance 0 3 N ET_pSrE CVS_adSrE o DI I 1650 1700 1750 1800 wavenumber Figure 3 Zooming in on a Spectral Region 2 A SAMPLE EXPLORATION 9 backwards because the underlying spectra were originally saved with an ascending frequency axis which is not always the
22. ect on PC 1 PC 2 on the other hand is driven by a number of peaks with some interesting opposing peaks in the hydrocarbon region While the actual analysis of the data is not our goal here it would appear that PC 1 is sensitive to the ester vs acid carbonyl group and PC 2 is detecting the saturated vs unsaturated fatty acid chains the latter having Csp2 H peaks plotLoadings SrE3 IR class title 5 loads c i 2 ref 1 You can also plot one loading against another using function plot2Loadings Figure 20 This is typically not too useful for spectroscopic data since many of the variables are correlated as they are parts of the same peak hence the serpentine lines in the figure The most extreme points on the plot however can give you an idea of which peaks frequencies serve to differentiate a pair of PCs and hence drive your data clustering plot2Loadings SrE3 IR class title pany loads c 1 2 tol 0 002 31 2 A SAMPLE EXPLORATION PC2 3 3 loadings 0 10 0 05 0 00 0 05 0 10 0 15 S repens IR Spectra Covariance of Loadings 1704 8 1706 8 e 1741 5 1708 7 1743 4 1745 3 1710 6 017473 e L00000 Vere Bee ne e 2916 0 029778 centered noscale classical ones 0 2 0 1 0 0 0 1 PC1 93 loadings Figure 20 Plotting One Loading vs Another 2 A SAMPLE EXPLORATION 32 S repens IR Spectra s Plot eo oo 00 La c 2
23. ely exploratory and unsupervised in other words top down 3 developed it while beginning a new research focus on plant metabolomics and needed software to analyze the data was collecting My research involves ecological experiments on plant stress so ChemoSpec was designed to accommodate samples that have different histories i e they fall into different classes categories or groups Examples would be treatment and control groups or simply different specimens red flowers vs blue flowers Since my research is done with undergraduates who are true novices with R ChemoSpec is designed to be as user friendly as possible with plenty of error checking helpful warnings and a consistent interface It also produces graphics that are consistent in style and annotation and are suitable for use in publications and posters Careful attention was given to writing the documentation for the functions but this vignette serves as the best starting point for learning data analysis with ChemoSpec The centerpiece of ChemoSpec is the Spectra object This is the place where your data is stored and made available to R Once your data in stored this way and checked all analyses are easily carried out ChemoSpec currently ships with several built in data sets we ll use one called SrE IR for our demonstrations You will see in just a moment how to access it and inspect it 2 A SAMPLE EXPLORATION 3 assume you have at least a bare bones knowledge
24. eo E A E EEE E EE A i 3 cumulative percent a OD 2 D ceal g individual percent 40 ot 20 pesa centered noscale classical I I I I I I 1 2 3 4 5 6 7 8 9 10 factor Figure 15 Scree Plot Figure 16 If you are using classical PCA you can also get a sense of the number of PCs needed via a bootstrap method as shown in Figure 17 Note that this method is iterative and takes a bit of time Comparing these results to the scree plots you ll see that the bootstrap method suggests that 4 or 5 PCs would not always be enough to reach the 95 level while the scree plots suggest that 2 PC are sufficient plotScree class title A plotScree2 class title S out lt pcaBoot SrE3 IR pcs 5 choice Now let s turn to viewing scores in 3D There are currently 3 options in ChemoSpec plotting using lattice graphics which produces a static plot that you have to adjust manually and two interactive plots one based on package rgl and one based upon package rggobi which in turn uses the program GGobi Probably the best place to start is with plotScoresRGL It it well suited to exploring data and can be printed out in high quality However the nature of the open GL graphics device means that the title and the legend move with the data so this may not give a hardcopy suitable for publications This interactive plot cannot be invoked in this document but here
25. ep for the sample name to verify that it is not found The first argument in grep is the pattern you are searching for if that pattern matches more than one name they will all be caught For example if you used SrE as your pattern you would remove all the samples except the two reference samples since SrE occurs in adSrE and pSrE You can check this in advance with the grep function itself SrE lt grep SrE SrE IR names SrE IR names SrE gives the name s that contain SrE 1 CVS_adSrE ET_pSrE GNC_adSrE 4 LF adSrE MDB_pSrE NA pSrE T Nat_adSrE NP_adSrE NR_pSrE 10 NSI_adSrE NW_adSrE SN adSrE 13 Sol_adSrE TD_adSrE SrE gives the corresponding indicies de il a 2 Ss 4 6 8 7 Ao wil iy ae 16 This is what is meant by grep is greedy In this situation you have three choices 1 You could manually remove the problem samples gt str SrE IR would give you an idea of how to do that see also below under Hierarchical Cluster Analysis 2 removeSample also accepts indices of samples so you could grep as above note the index of the sample you actually want to remove and use that in rem sam 3 If you know a bit about grep you can pass a more sophisticated search pattern to rem sam 2 2 3 Removing Groups removeSample uses the names of the samples in Spectra names to identify and remove individual samples from the Spectra object As of v 1 51 ther
26. es rob for robustly computed ellipses or both if you want to directly compare the two Note that the use of classical and robust here has nothing to do with the PCA algorithm it s the same idea however but applied to the 2D array of scores produced by PCA Points outside the ellipses are more likely candidates for outlier status class lt classPCA SrE3 IR choice nosc plotScores SrE3 IR title S I Spectra class pcs c 1 2 ellipse tol 0 01 Warning message Group EPO has only 1 member no ellipse possible Warning message Group 00 has only 1 member no ellipse possible robust lt robPCA SrE3 IR choice noscale plotScores SrE3 IR title S re I QU robust pcs c 1 2 ellipse tol 0 01 Warning message Group EPO has only 1 member no ellipse possible Warning message Group 00 has only 1 member no ellipse possible Plots such as shown in Figures 11 and 12 can give you an idea of potential outliers but ChemoSpec includes more sophisticated approaches The function pcaDiag can produce two types of plots that can be helpful Figures 13 and 14 The meaning and interpretation of these plots is discussed in more detail in Varmuza and Filzmoser Chapter 3 2 NOTE There is some sort of problem with the OD plot as you can plainly see l Il be working on it diagnostics lt pcaDiag SrE3 IR class pcs 2 plot 0D diagnostics lt pcaDiag SrE3 IR class pcs 2 plot
27. et and olive oil labeled OO These latter two oils are mixtures of triglycerides for the most part while the SrE samples are largely fatty acids As a result the spectra of these two groups differ the glycerides have ester carbonyl stretches and no O H stretch while the fatty acids have acid carbonyl stretches and an O H stretch consistent with a carboxylic acid OH Also included is SrE NMR which is a corresponding set of NMR spectra and CuticleIR The latter is a series of IR spectra of the cuticle leaf surface of the plant Portulaca oleracea The data were taken by gently pinning the leaf against an ATR sampling device The plants were grown at two different temperatures and two different genotypes varieties were used a classic G x E genotype by environment experiment The SrE IR data set is used as the example in this vignette as the sample spectra are fairly different and give good separation by most chemometric methods The CuticleIR spectra differ in much more subtle ways and as as result are more of a challenge to analyze For more details about these data sets type gt data_set_name at the console Color and Symbol Options Skip this material if this is your first time looking through ChemoSpec In ChemoSpec the user may use any color name format known to R For ease of comparison it would be nice to plan ahead and use the same color scheme for all your plots However if you are just doing preliminary work ChemoSpec will
28. eting in which groups of frequencies are collapsed into one frequency value and the corresponding intensities are summed There are two reasons for doing this One is to compact the data but the algorithms in R are quite fast and data sets of the size of SrE IR don t slow it down much The other reason is to compensate for shifts in very narrow peaks from sample to sample This is typically done in H NMR because changes in dilution ionic strength or pH can cause slight shifts Spectra with broad rolling peaks won t have this problem UV Vis for example The function binBuck is your friend tmp lt binBuck SrE3 IR bin ratio 4 To preserve the requested bin ratio 3 data point s has have been removed from the beginning of the data chunk 1 2 A SAMPLE EXPLORATION Gaps in Frequency Data 0 20 0 25 0 15 0 10 0 05 0 00 1000 2000 3000 marked regions are skipped in data set Figure 9 Procedure to Find Gaps in a Data Set 4000 17 2 A SAMPLE EXPLORATION 18 HH To preserve the requested bin ratio 3 data point s has have been removed from the beginning of the data chunk 2 HH A total of 6 data points were removed to preserve the requested bin ratio sumSpectra tmp HH Serenoa repens IR quality study HH There are 16 spectra in this set The y axis unit is absorbance HH The frequency scale runs from 407 9 to 3999 wavenumber There are 375 fre
29. groups have too few members to compute the interquartile range and these are not shown s a result some panels are empty specSurvey SrE2 IR method title 5 ins Deta f Carbor DO by gr TRUE xlim c 1650 1800 Warning message Group EPO has 3 or fewer members so your stats are not very useful This group has been dropped for display purposes Warning message Group 00 has 3 or fewer members so your stats are not very useful This group has been dropped for display purposes Warning message Invalid or ambiguous component names 2 A SAMPLE EXPLORATION 13 S repens Detail of Carbonyl Region Full Data Set median igr 1700 1750 wavenumber Figure 6 Detail of Carbonyl Region 2 A SAMPLE EXPLORATION 14 S repens Detail of Carbonyl Region median igr 1700 1750 wavenumber Figure 7 Detail of Carbonyl Region by Group 2 A SAMPLE EXPLORATION 15 S repens Detail of Empty Region 0 30 0 25 0 20 0 15 0 10 Full Data Set median igr 0 05 0 00 T T T T 1900 2000 2100 2200 2300 2400 wavenumber Figure 8 Inspection of an Uninteresting Spectral Region For reasons that will become evident in a moment let s look at the region between 1800 and 2500 cm Figure 8 specSurvey SrE2 IR method title b f VaR r by gr FALSE xlim c 1800 2500 From a theoretical perspective we expec
30. he PCA scores since the scores represent the cleaned up data The result using the SrE IR data set are not different than doing HCA on the raw spectra so we won t illustrate it but the 2 A SAMPLE EXPLORATION 33 Detail of S repens IR Spectra s Plot 0 90 1720 3 0 95 1718 3 1716 4 1695 2 1697 1 correlation 1699 1 1693 3 1714 5 16 17126 1701 0 1702 9 1 00 centered noscale classical 1 05 0 040 0 035 0 030 0 025 0 020 0 015 0 010 covariance Figure 22 s Plot Detail 2 A SAMPLE EXPLORATION Raw Data Grand Mean Means for each level of factor 1 34 Means for Means for each level each level of of factor 2 factor 1 x factor 2 Residual Error Figure 23 aovPCA breaks the data into a series of submatrices original data JAE JAE JAE JAE JAE FAI factor matrix ZARA ZAI THETA HHHH JHE THETA TE JAE ET t group membership Figure 24 Submatrices are composed of rows which are averages of each factor level command would be hcaScores SrE3 IR class scores c 1 5 title S 2 6 ANOVA PCA Harrington et a 13 and a few others 14 have demonstrated a method which combines traditional ANOVA with PCA Standard PCA is blind to class membership though one generally colors the points in a score plot using the known class membership ANOVA PCA uses the class membership to divide the original centered data matrix
31. hoose not to scale or you can scale according to the median absolute deviation Median absolute deviation is a means of downweighting more extreme peaks The literature has plenty of recommendations about scaling options appropriate for the type of measurement instrument as well as the nature of the biological data set 2 5H8 LI There is not enough space here to illustrate all possible combinations of options Figure 11 and Figure 12 show the use and results of classical and robust PCA without scaling followed by plotting of the first two PCs we ll discuss plotting options momentarily You can see from these plots that the robust and classical methods have produced rather different results not only in the overall appearance of the plots but in the amount of variance explained by each PC Since we ve plotted the scores to see the results let s mention a few features of plotScores which produces a 2D plot of the results we Il deal with 3D options later Note that an annotation is provided in the upper left corner of the plot that describes the history of this analysis so you don t lose track of what you are viewing The tol argument controls what fraction of points are labeled with the sample name This is a means of identifying potential outliers The ellipse argument determines if and how the ellipses are drawn the 95 confidence interval is used You can choose none for no ellipses cls for classically computed confidence ellips
32. ierdo Metabonomic GUI for Metabonomic Analysis 2010 R package version 3 5
33. of R as you begin to learn ChemoSpec and have a good workflow set up For detailed help on any function discussed here type function_name at the console Finally some conventions for this document names of R objects such as packages functions function arguments and data sets are in typewriter font The commands you issue at the console and the output are shown with a light grey background and are colored according to use and purpose courtesy of the excellent knitr package 4 By the way if you try ChemoSpec and find it useful have questions have opinions or have suggestions please do let me know The current version has already been improved by users 2 A Sample Exploration This sample exploration is designed to illustrate a typical ChemoSpec workflow The point is to illustrate how to carry out the commands what options are available and typically used and the order in which one might do the analysis The SrE IR data set will be used for illustration only we are not trying to analyze it to reach useful conclusions You may wish to put your versions of these commands into a script file that you can source as you go along This way you can easily make changes and it will all be reproducible To do this open a blank R document and type in your commands Save it as something like My_First_ChemoSpec R Then you can either cut and paste portions of it to the console for execution or you can source the entire thing source irs
34. ormation content of metabolomics data BMC Genomics vol 7 p 15 2006 8 S C Zhang C Zheng I R Lanza K S Nair D Raftery and O Vitek Interdependence of signal processing and analysis of urine H 1 NMR spectra for metabolic profiling Analytical Chemistry vol 81 no 15 pp 6080 6088 2009 9 P E Anderson N V Reo N J DelRaso T E Doom and M L Raymer Gaussian binning a new kernel based method for processing NMR spectroscopic data for metabolomics Metabolomics vol 4 no 3 pp 261 272 2008 10 T De Meyer D Sinnaeve B Van Gasse E Tsiporkova E R Rietzschel M L De Buyzere T C Gillebert S Bekaert J C Martins and W Van Criekinge NMR based characterization of metabolic alterations in hypertension using an adaptive intelligent binning algorithm Analytical Chemistry vol 80 no 10 pp 3783 3790 2008 REFERENCES 41 11 12 13 14 15 16 17 18 19 20 21 T K Karakach P D Wentzell and J A Walter Characterization of the measurement error structure in 1D H 1 NMR data for metabolomics studies Analytica Chimica Acta vol 636 no 2 pp 163 174 2009 S Wiklund E Johansson L Sjostrom E J Mellerowicz U Edlund J P Shockcor J Gottfries T Moritz and J Trygg Visualization of gc tof ms based metabolomics data for identification of biochemically interesting compounds using opls class models
35. quency x axis data points The frequency resolution is 7 714 wavenumber point HH This data set is not continuous along the frequency axis Here are the data chunks HH beg freq end freq size beg indx end indx HH 1 407 9 1796 1389 1 181 2 2510 0 3999 1489 182 375 HH The spectra are divided into 4 groups HH group no color symbol alt sym 1 adSrE 10 984EA3 15 d 2 EPO 1 377EB8 2 b 3 00 1 4DAF4A 3 Cc 4 pSrE 4 E41A1C 1 a HH Note this data is an S3 object of class Spectra Compare the results here with the sumSpectra of the full data set Section 2 2 In particular note that the frequency resolution has gone down due to the binning process ChemoSpec uses the simplest of binning algorithms after perhaps dropping a few points with a warning to make your data set divisible by the specified bin ratio data points are replaced by the average frequency and the sum of the grouped intensities Depending upon the fine structure in your data and the bin ratio this might cause important peaks to be split between different bins There are more sophisticated binning algorithms in the literature that try to address this but none are currently implemented in ChemoSpec 9 2 4 Hierarchical Cluster Analysis Hierarchical cluster analysis HCA from now on is a clustering method no surprise in which distances between samples are calculated and displayed in a dendrogram a tree like stru
36. quite different depending upon your the nature of your data The differences arise because PCA methods both classical and robust attempt to find the components that explain as much of the variance in the data set 2 A SAMPLE EXPLORATION 21 as possible If you have a sample that is genuinely corrupted for instance due to sample handling its spectral profile may be very different from all other samples and it can legitimately be called an outlier In classical PCA this one sample will contribute strongly to the variance of the entire data set and the PCA scores will reflect that it is sometimes said that scores and loadings follow the outliers With robust PCA samples with rather different characteristics do not have as great an influence because robust measures of variance such as the median absolute deviation are used Note that as of ChemoSpec 1 46 neither classPCA nor robPCA carry out any normalization by samples You need to decide if you want to normalize the samples and if so use normSpectra Besides choosing to use classical or robust methods you also need to choose a scaling method For classical PCA your choices are no scaling autoscaling or Pareto scaling In classical analysis if you don t scale the data large peaks contribute more strongly to the results If you autoscale then each peak contributes equally to the results including noise peaks Pareto scaling is a compromise between these two For robust PCA you can c
37. s geared toward more sophisticated modeling of time dependent spectral data sets 20 Finally the package Metabonomic 2 provides a GUI interface to spectral processing such as baseline correction as well as a range of exploratory and supervised statistical methods Note my comments here are based on the latest versions have explored newer versions may have considerably more features Check em out for yourself References 1 B A Hanson ChemoSpec Exploratory Chemometrics for Spectroscopy 2012 R package version 1 51 0 2 K Varmuza and P Filzmoser Introduction to Multivariate Statistical Analysis in Chemometrics CRC Press 2009 3 D S Wishart Current progress in computational metabolomics Briefings in Bioinformatics vol 8 no 5 pp 279 293 2007 4 Y Xie knitr A general purpose package for dynamic report generation in R 2012 R package version 0 5 5 A Craig O Cloareo E Holmes J K Nicholson and J C Lindon Scaling and normalization effects in NMR spectroscopic metabonomic data sets Analytical Chemistry vol 78 no 7 pp 2262 2267 2006 6 R Romano M T Santini and P L Indovina A time domain algorithm for NMR spectral normalization Journal of Magnetic Resonance vol 146 no 1 pp 89 99 2000 7 R A van den Berg H C J Hoefsloot J A Westerhuis A K Smilde and M J van der Werf Centering scaling and transformations improving the biological inf
38. s of some interest to visually compare the score plot in Figure with the mclust results in Figure 26 Next mclust will map the true groups onto the groups it has found Points in error are X ed out These results can be seen in Figure 27 From this plot you can see that mclust finds 4 groups among the two true groups adSrE and pSrE In general you have to be very careful about using mclust s notion of an error it is very hard to map the found groups onto the truth in an algorithmic way lean toward not using the truth option in mclust more and more model lt mclustSpectra SrE3 IR class plot 5 title ME Le I 3 105 model lt mclustSpectra SrE3 IR class plot IO title r model lt mclustSpectra SrE3 IR class plot gl title 4g truth SrE3 IR groups Warning message classification and truth differ in number of groups You can also do a similar analysis in 3D using mclust3dSpectra This function uses mclust to find the groups but then uses non mclust functions to draw confidence ellipses This function uses rgl graphics so it cannot demonstrated here but the commands would be mclust3dSpectra SrE3 IR class not run it s interactive You have all the options here that you do with plotScoresRGL namely classical robust or no ellipses control of the ellipse details and labeling of extreme points hope you have enjoyed this tour of the features of ChemoSpec 3 Functions Tha
39. t Are Not Discussed Here The help files of course do apply 1 splitSpectraGroups A good example of its use can be found in gt aovPCA 2 hypTestScores 3 hmapSpectra 3 FUNCTIONS THAT ARE NOT DISCUSSED HERE S repens IR Spectra How Many Clusters Mclust optimal model EEV EH ra 1 AU i J lt a au B x 8 oa si TIZI Sa o S 2 4 6 8 number of components Serenoa repens IR quality study centered noscale classical Figure 25 mclust Chooses an Optimal Model 36 3 FUNCTIONS THAT ARE NOT DISCUSSED HERE S repens IR Spectra Clusters Found by mclust Mclust optimal model EEV PC2 0 10 0 15 0 20 0 05 0 00 0 05 PC1 Serenoa repens IR quality study centered noscale classical Figure 26 mclust s Thoughts on the Matter 37 3 FUNCTIONS THAT ARE NOT DISCUSSED HERE S repens IR Spectra Misclassifications Mclust optimal model EEV PC2 0 10 0 15 0 20 0 05 0 00 0 05 PC1 Serenoa repens IR quality study centered noscale classical Figure 27 Comparing mclust Results to the TRUTH 38 4 TECHNICAL BACKGROUND 39 groupNcolor normSpectra o gt oo plotScree2 plotScoresG splitSpectraGroups r2qPCA classPCA coordProjCS melustadSpectra normVec mclustSpectra labelExtremes3d conColScheme qerPCA plotScoresDecoration lotScree JotScoresaD p lg a6ClExtremes removeSample plot2Loadings sPlotSpectr
40. t smoSpec R 2 1 Getting Data into ChemoSpec Currently there is only one means of moving raw data sets into ChemoSpec and that is the function getManyCsv it is relatively easy to write analogous functions for other formats This function assumes that your raw data files are formatted as csv files and contain only the data itself in two columns The first column should be the frequency values and this column must be the same for all files as it will be if these are data sets from the same instrument and experimental parameters The second column should contain the intensity values There should not be a header row If your data set contains treatment and control groups or any analogous class group information this information should be encoded in the file names getManyCsv argument gr crit will be the basis for a grep process on the file names and from there each file representing a sample will be assigned to a group and be assigned a color as well If your samples don t fall into groups that s fine too but you still have to give gr crit something to go on just give it one string that is common to all the file names Obviously this approach encourages one to name the files as they come off the instrument with forethought as to how they will be analyzed which in turn depends upon your experimental design Nothing wrong with having a plan Remember that getManyCsv acts on all csv files it finds in a directory so don t have any extra cs
41. t this region to be devoid of interesting peaks In fact even when pooling the groups the signal in this region is very weak and the only peak present is due to CO We can remove this region since it is primarily noise and artifact with the function removeFreq as follows Note that there are fewer frequency points now SrE3 IR lt removeFreq SrE2 IR rem freq SrE2 IR freq gt 1800 amp SrE2 IR freq lt 2500 sumSpectra SrE3 IR HH Serenoa repens IR quality study HH There are 16 spectra in this set The y axis unit is absorbance HH 2 A SAMPLE EXPLORATION 16 The frequency scale runs from 399 2 to 4002 wavenumber There are 1506 frequency x axis data points The frequency resolution is 1 929 wavenumber point HH This data set is not continuous along the frequency axis Here are the data chunks HH beg freq end freq size beg indx end indx 1 399 2 1799 1400 al 727 2 2501 3 4002 1500 728 1506 HH The spectra are divided into 4 groups HH group no color symbol alt sym 1 adSrE 10 984EA3 15 d 2 EPO 1 377EB38 2 b 3 00 1 4DAF4A 3 Cc 4 pSrE 4 E41A1C al a HH xxx Note this data is an S3 object of class Spectra Notice that sumSpectra has identified a gap in the data set You can see this gap in the data as shown in Figure 9 sumSpectra checks for gaps but doesn t produce the plot both the numerical results and a figure are provided check4Gaps Sr
42. tScoresG to produce an different interactive plot This is actually the most powerful analysis tool as GGobi is not restricted to 3 dimensions and can use projection pursuit methods to find interesting views of your data With an additional package DescribeDisplay you can create very nice plots of your data Note that as of December 2010 GGobi has been described as on life support and it seems there might be a new program in development to replace it However you can find a working version at and related mirrors thanks to Simon Urbanek Note that the communication between plotScoresG and GGobi does not quite work as described earlier under the discussion of the color schemes this will not be fixed until the future of GGobi is certain 2 A SAMPLE EXPLORATION 30 S repens IR Spectra Loadings Plot 0 05 0 10 0 05 PC 2 Loadings 0 15 0 1 n DI e J o e Si ye O a 0 2 0 1 E a Jo e 8 9 o 8 s S S fo 9 o dC oO 1000 2000 3000 4000 wavenumber centered noscale classical Figure 19 Loading Plot plotScoresG SrE3 IR class not run it s interactive In addition to the scores PCA also produces loadings which tell you how each variable frequencies in spectral applications affect the scores Examining these loadings can be critical to interpreting your results Figure 19 gives an example You can see that the different carbonyl peaks have a large and opposing eff
43. tic Samples In the process of plotting and inspecting your spectra you may find some spectra samples that have problems Perhaps they have instrumental artifacts Or maybe you have decided to eliminate one subgroup of samples from your data set to see how the results differ To remove a particular sample or samples meeting a certain criteria you use the removeSample function This function uses a grepping process based on its rem sam argument so you must be careful due to the greediness of grep Let s imagine that sample TD_adSrE has artifacts and needs to be removed The command would be noTD lt removeSample SrE IR rem sam c TD_adSrE sumSpectra noTD No gaps were found by check4Gaps No plot will be made HH Serenoa repens IR quality study HH There are 15 spectra in this set The y axis unit is absorbance HH The frequency scale runs from 399 2 to 4002 wavenumber There are 1869 frequency x axis data points The frequency resolution is 1 929 wavenumber point HH HH The spectra are divided into 4 groups HH group no color symbol alt sym 1 adSrE 9 984EA3 15 d 2 EPO 1 377EB8 2 b 3 00 1 4DAF4A 3 2 A SAMPLE EXPLORATION 10 4 pSrE 4 E41A1C 1 a HH Note this data is an S3 object of class Spectra grep TD_adSrE noTD names integer 0 The sumSpectra command confirms that there are now one fewer spectra in the set As shown you could also re gr
44. tually used in PCA any apparent correspondence between the sample group classification and the clusters found is accidental in terms of the computation but of course this is what one hopes to find mclust is a model based clustering package that takes a different approach 15 16 mclust assumes that there are groups within your data set and that those groups are multivariate normally distributed Using an iterative approach mclust samples various possible groupings within your data set and uses a Bayesian Information Criterion BIC to determine which of the various groupings it finds best fits the data distribution mclust looks for groups that follow certain constraints for instance one constraint is that all the groups found must have a spherical distribution of data points while another allows for ellipsoidal distributions See the paper by Fraley and Raftery 16 for more details The basic idea however is that mclust goes looking for groups in your data set and then you can compare the groupings it finds with the groupings you know to be true ChemoSpec contains several functions that interface with and extend mclust functions mclust first uses the BIC to determine which model best fits your data these results are shown in Figure 25 Next Figure 26 shows the 5 groups that mclust finds in the data which actually is composed of 4 groups though admittedly two of those groups are composed of one member each these are labeled e It
45. v files hanging around The output of getManyCsv is a Spectra object which is R speak for a file readable by R that contains not only your data but other information about the experiment as provided by you via the arguments to getManyCsv Here s a typical example we have to talk hypothetically because don t have your data Let s say you had a folder containing 30 NMR files of flower essential oils Imagine that 18 of these were from one hypothetical subspecies and 12 from another Further let s pretend that the question under investigation has something to do with the taxonomy of these two supposed subspecies in other words an investigation into whether or not they should be considered subspecies at all If the files were named like this sspAl csv sspA18 csv and sspBl csv sspB12 csv Then the following command should process the files and create the desired Spectra object getManyCsv gr crit c gr cols c a e freq unit 5 int unit descrip ub Dy out file JS 2s 1Users in the EU have different standards for a csv file they are delimited by semi colons and a comma is used where a decimal point is used in the United States For these files use the argument format csv2 to read the files properly 2 A SAMPLE EXPLORATION 4 This causes getManyCsv to read the file names for the strings sspA and sspB and use these to assign the samples into groups Samples in sspA csv files will be assigned the
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