Abstract
Assessing similarity between data sets with the reduced χ^{2} test requires the estimation of experimental errors, which, if incorrect, may render statistical comparisons invalid. We report a goodnessoffit test, Correlation Map (CorMap), for assessing differences between onedimensional spectra independently of explicit error estimates, using only data point correlations. Using smallangle Xray scattering data, we demonstrate that CorMap maintains the power of the reduced χ^{2} test; moreover, CorMap is also applicable to other physical experiments.
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Acknowledgements
We thank E. Morenzoni of the Laboratory for MuonSpin Spectroscopy, Paul Scherrer Institute, for providing the ZFμSR data, taken at the GPS instrument of the Swiss Muon Source, Villigen, Switzerland. We thank R.P. Rambo for providing the original implementation of the χ^{2}_{free} test for our analysis and H. Mertens and J. Trewhella for many useful discussions. This work was supported by the Bundesministerium für Bildung und Forschung (BMBF) project BIOSCAT, grant 05K12YE1, and by the European Commission, BioStructX grant 283570.
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The initial idea was conceived of and simulation studies were done by D.F. Experimental data were collected by C.M.J. D.F, C.M.J. and D.I.S. participated in critical discussion and wrote the manuscript.
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Integrated supplementary information
Supplementary Figure 1 Empirical and theoretical distributions of the reduced χ^{2} test.
The histogram shows the empirical distribution of 5,000 reduced χ^{2} values computed from 5,000 independent pairwise comparisons of 10,000 SAXS data frames obtained from water (bars) together with their expected values from a reduced χ^{2} distribution (line) assuming no differences. The good agreement between the observed and expected distributions indicates accurate error estimates for this data set. Under the assumptions of correct errors and frame similarity, the acceptable range of values of χ^{2} is approximately 0.9 to 1.1, values less or greater are indicative of either differences in the data or miscalculated errors.
Supplementary Figure 2 The statistical properties of SAXS intensities recorded using a photoncounting detector.
(a) Histograms and limiting distributions of experimental intensities, I_{exp}(q_{k}), of repeated measurements of water collected at a single value of q, in this case q_{k} = 0.2012 nm^{−1} (wide histogram: 10,000 frames at 0.1s, medium histogram: 1,000 frames at 1.0s, narrow histogram: 500 frames at 10s). Generally, the distribution of intensities at any given q_{k} is Gaussian and the respective standard deviations in this example decrease with √10 as expected by the Standard Error of the Mean. (b) Experimental error estimates of 10,000 frames of water according to Poisson counting statistics (dark gray) and the standard deviations of the Normals (light gray) across all of q. The spikes in the variations correspond to different numbers of pixels used to assess the errors caused by the gaps in the detector modules. (c) Example of a pairwise joint normal distribution of two qlocations (q_{k}, q_{l}). (d) Correlation map of 10,000 frames of water, highlighting that data points are uncorrelated across the whole qrange.
Supplementary Figure 3 Application of CorMap to detect the onset of Xray radiation damage to a protein sample during SAXS measurements.
Correlation map time series from experimental SAXS data frames of lysozyme (consecutive 50 ms exposures, 1 s total, n=1600 data points, unsubtracted data). The upper left panel shows an allvs.all frame comparison, indicating differences exist between the frames across the whole dataset. The topleft to bottomright panels show the pairwise correlation maps of the first frame relative to each subsequent frame together with Bonferroni adjusted pvalues. Up to frame 13, the adjusted pvalue is stable (1.00); frames 1416 show a reduced pvalue relative to frame 1 (0.05730.0143), while at frame 17 and later the adjusted pvalue drops to < 0.01 indicating of statistically significant differences. The column to the right shows the overlay of 1D scattering profiles of selected frame pairs.
Supplementary Figure 4 Application of CorMap to detect concentration effects (repulsive interparticle interference).
(a) SAXS scattering patterns of RNAse collected at 3.7 mg/ml, 7.5 mg/ml and 15 mg/ml; (b)(d) pairwise correlation maps from the RNAse sample scattering at the respective concentrations do not reveal statistically significant differences across the profiles (n=1675, C=14, 14, 12, adjusted Pvalues: 0.1485, 0.1485 and 0.5525); (e) scattering patterns of human serum albumin at 5 mg/ml, 10 mg/ml and 20 mg/ml; (f)(h) correlation maps of pairwise comparisons of HSA at the three concentrations show concentration effects at low q and statistically significant differences between the SAXS data frames (n=1200, C=50, 162, 180, adjusted P <10e6 in all cases).
Supplementary Figure 5 Empirical and theoretical distributions of the Correlation Map test.
Histogram of the edge lengths of maximum correlation patch sizes obtained from 5,000 independent experimental twoframe comparisons of water (bars), together with its expected distribution (dots). Here the number of available data points in the entire qrange, corresponds to coin tosses. In this figure, with n=1682 qvalues, the expected largest edge length of the patches of similar correlation lies in the range of 8 to 20. Any larger lengths are extremely unlikely to occur by chance.
Supplementary Figure 6 Variation of the theoretical distribution with respect to its parameter n.
(a). The theoretical correlation map distributions calculated for n = 400, 800 and 1600 points. The maximum is located at log_{2}(n). (b)(d) Comparison of SAXS data sets comprised of 20 frames of water illustrating: (b) 1600 × 1600 data point comparison; (c) data rebinning of the same frames into 800 × 800 and (d) 400 × 400 data points. The white diagonal corresponds to each point's correlation to itself. The evaluation of differences using the correlation map takes into account the reduction in n, i.e., the expected edge length at a significance level α is dependent on the number of data points.
Supplementary Figure 7 Examples of experimental data and simulations thereof.
Overview of experimental data used to derive the empirical radiation damage components used in the simulations of (H3,H4,H5). The top row shows three frames each of the different experimental data sets (columns), the middle row depicts the extracted additive component for the simulation and the last row shows examples of the simulated data sets.
Supplementary Figure 8 Comparison of the statistical power of the CorMap and the reduced χ^{2} test.
Power comparison of the reduced χ^{2} test (dotted) and correlation map (line) at α = 0.01. The panels show the power for experimental frame comparisons where (a) represents systematic random shift errors, (b) systematic random scale errors, and (c)(e) increasing contributions of modeled radiation damage. Effect sizes are in arbitrary units. True Positive proportions were estimated from 2,000 simulations each, the 99% ClopperPearson confidence intervals at each effect size are shown as vertical bars. Overlapping confidence intervals indicate equivalent tests at that effect size; fully separated intervals indicate significant differences between the tests. The corresponding count values are given in Supplementary Table 2.
Supplementary Figure 9 Models of bovine serum albumin used for statistical testing.
Backbone representations of the hypothetical BSA monomer modifications used to compare the False Positive rate and statistical power of the reduced χ^{2} test and correlation map for assessing SAXS datamodel fits. The arrow from lefttoright indicates the rotation from nativetorotated structure(s).
Supplementary Figure 10 Application of CorMap as a tool to assess datamodel fits.
Panel (a) shows a simulated BSA SAXS profile with a native model fit (pvalue: 0.1848) and corresponding correlation map in panel (c). Panel (b) shows the same data with a model that does not fit, (20° rotation in theTyr496 to Val497 bond angle pvalue: <10e6). The insert highlights the region of the misfit that is more clearly visible in a disturbance of the randomness pattern in the correlation map in panel (d). The corresponding reduced χ^{2} values with correct errors for these cases are 1.0 and 1.7 respectively. In many publications a reduced χ^{2} of 1.7 might be considered indicative of a good fit, while the correlation map shows this may not actually be the case (d). Panel (e) indicates the power of the reduced χ^{2} test (dotted line) and the correlation map (solid line) to correctly classify model fits. The effect size in this instance corresponds to an increasing rotation of around a bond angle of several BSA models (Supplementary Fig. 9). True Positive proportions were estimated from 10,000 simulations at each point, the 99% ClopperPearson confidence intervals at each effect size are shown as vertical. Overlapping confidence intervals indicate equivalent tests at that effect size; fully separated intervals indicate significant differences between the tests.
Supplementary Figure 11 The reduced χ^{2} and χ^{2}_{free} tests are equivalent if the errors are correctly specified.
Comparison of results of reduced χ^{2} and χ^{2}_{free} test to evaluate datamodel fitting. A total of 23,000 simulated BSA datasets with correctly specified errors were analyzed using both tests to assess the fits of the models shown in Supplementary Figure 9; ‘without effect' (black) and with increasingly larger effect (gray scale). The results of χ^{2} and χ^{2}_{free} tests are, up to sampling variation inc^{2}_{free}, essentially identical, but do not correspond precisely to the diagonal (black line); the values ofc^{2}_{free} are systematically larger than those of χ^{2}.
Supplementary Figure 12 The reduced χ^{2} and χ^{2}_{free} tests are equivalent if the errors are correctly specified, regardless of the actual error values.
(a) Example of a simulated SAXS dataset with 3% constant relative errors in black and the model scattering in white on top; (b) Comparison of reduced χ^{2} and χ^{2}_{free} tests of 1,000 repetitions of (a). The outcome is identical to what is shown in Supplementary Fig. 11.
Supplementary Figure 13 Comparison of reduced χ^{2} statistic and χ^{2}_{free} with incorrectly specified errors.
A total of 23,000 BSA model datasets were analyzed as described in the main text, the only difference being the assignment of incorrect errors prior to analysis. Panel (a) correct error structure, but half the magnitude, (b) correct error structure but twice the magnitude, (c) a random permutation of the correct errors and (d) a constant 75% relative error across the data set. The circle shown in each panel indicates the location of the correct results shown in Supplementary Fig. 11.
Supplementary Figure 14 Radial averaging of an idealized SAXS image.
Only pixels with a constant distance (black) from the beam center (red) are considered for each data point. Antialiasing must not be employed.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–14 and Supplementary Tables 1 and 2 (PDF 3704 kb)
Goodnessoffit of bead model refinement.
Dummy atom bead model refinement against lysozyme SAXS data. The left panel displays the progressive improvement of the fit (solid line) for the stepwise DAMMIF bead model refinement of the shape of lysozyme against lysozyme SAXS data (dots). As the fit improves, the correlation matrix (right panel) goes from having large contiguous areas of +1 or 1 correlations (i.e., large patches) to a randomized lattice pattern. The initial and finallyrefined lysozyme models are shown in Figure 2 of the main text. (MPG 6868 kb)
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Franke, D., Jeffries, C. & Svergun, D. Correlation Map, a goodnessoffit test for onedimensional Xray scattering spectra. Nat Methods 12, 419–422 (2015). https://doi.org/10.1038/nmeth.3358
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