Nature Methods: doi: /nmeth Supplementary Figure 1

Transcription

1 Supplementary Figure 1 Performance analysis under four different clustering scenarios. Performance analysis under four different clustering scenarios. i) Standard Conditions, ii) a sparse data set with only 10% as many localisations, iii) clusters which are twice as large and iv) only 10 localisations per cluster and 90% of localisations in the background. a) Histograms of the number of clusters per region. b) Histograms of the cluster radii. c) Histograms of the number of localisations per cluster and d) Histograms of the percentage of localisations found in clusters. In all cases the blue dashed line represents the true value. Histograms are calculated from 100 simulated data sets for each scenario.

2 Supplementary Figure 2 Estimation of cluster descriptors as simulation parameters vary (n = 100 per point). Estimation of cluster descriptors as simulation parameters vary (n = 100 per point). i) Measured localisations per cluster. ii) Measured cluster radii. iii) Measured percentage of localisations in clusters and iv) measured number of clusters per region. a) Simulated number of localisations per cluster, b) simulated cluster radii and c) simulated fraction of background localisations. Blue dashed lines represent simulated values.

3 Supplementary Figure 3 Comparison of our algorithm with DBSCAN. Comparison of our algorithm with DBSCAN. a-d) Comparison of the proposal generating algorithm (I) with DBSCAN (II) when each method is allowed to optimise its analysis parameters based on our Bayesian scoring mechanism, run on simulated data in the Standard Conditions (n = 100). a) Number of clusters per region, b) percentage of localisations in clusters, c) number of localisations per cluster and d) cluster radii. e-h) Histograms of key cluster descriptors generated by DBSCAN with fixed r = 50 nm and T = 78 from simulated data in the Standard Conditions (n = 100). e) Number of clusters per region, f) number of localisations per cluster, g) percentage of localisations in clusters and h) cluster radii. Blue dashed lines represent simulated values.

4 Supplementary Figure 4 Analysis of simulated data from the standard and sparse conditions (n = 100) by alternative clustering techniques. Analysis of simulated data from the Standard (a-f) and sparse (g-l) conditions (n = 100) by alternative clustering techniques. a and g) Cluster heat maps generated from Getis and Franklin s Local Point Pattern Analysis. b and h) Binary maps generated from these heat maps. c and i) Number of clusters per region extracted from the binary maps. d and j) Percentage of localisations in clusters. Blue dashed lines represent simulated values. e and k) Ripley s K function curves for example data sets together with 99% confidence intervals (C.I.) generated by simulating 100 CSR datasets. f and i) Pair Correlation curves for example data sets.

5 Supplementary Figure 5 Analysis of simulated data with 100 nm clusters and data with 90% of localizations in the background (n = 100) by alternative clustering techniques. Analysis of simulated data with 100 nm clusters (a-f) and data with 90% of localisations in the background (g-l) (n = 100) by alternative clustering techniques. a and g) Cluster heat maps generated from Getis and Franklin s Local Point Pattern Analysis. b and h) Binary maps generated from these heat maps. c and i) Number of clusters per region extracted from the binary maps. d and j) Percentage of localisations in clusters. Blue dashed lines represent simulated values. e and k) Ripley s K function curves for example data sets together with 99% confidence intervals (C.I.) generated by simulating 100 CSR datasets. f and i) Pair Correlation curves for example data sets.

6 Supplementary Figure 6 Performance of the algorithm on data sets (n = 100) with an uneven background. Performance of the algorithm on data sets (n = 100) with an uneven background, following a Beta(2,2) (i) or a Beta(5,1) (ii) distribution. a) Representative data. b) Log(posterior probability) heat maps. c) The highest scoring cluster proposal. d) Histograms of cluster radii. e) Histograms of the number of localisations per cluster. f) Histograms of the number of clusters. g) Histograms of the percentage of localisations found in clusters. Blue dashed lines represent true values.

7 Supplementary Figure 7 Analysis of simulated data with an uneven background by alternative clustering techniques. Analysis of simulated data with an uneven background, following a Beta(2,2) (a-f) or a Beta(5,1) (g-l) distribution (n = 100) by alternative clustering techniques. a and g) Cluster heat maps generated from Getis and Franklin s Local Point Pattern Analysis. b and h) Binary maps generated from these heat maps. c and i) Number of clusters per region extracted from the binary maps. d and j) Percentage of localisations in clusters. Blue dashed lines represent simulated values. e and k) Ripley s K function curves for example data sets together with 99% confidence intervals (C.I.) generated by simulating 100 CSR datasets. f and i) Pair Correlation curves for example data sets.

8 Supplementary Figure 8 Histograms of the measured number of localizations per cluster and cluster radii for simulated dimers, trimers and hexamers. Histograms of the measured number of localisations per cluster and cluster radii for simulated dimers, trimers and hexamers in a dense or sparse distribution (n = 100 per condition). These were simulated by generating localisations with identical coordinates which were then independently scrambled according to each point s localisation precision. The detection problem becomes harder as the overall density increases. We therefore performed the analysis at two different densities (2000 and 200 overall localisations).

9 Supplementary Figure 9 Performance of the algorithm on data sets (n = 100) with different cluster sizes within the same ROI. Performance of the algorithm on data sets (n = 100) with different cluster sizes within the same ROI, 5x 50 nm and 5x 100 nm (i), 5x 10 nm and 5x 100 nm (ii) and a range of cluster sizes between 10 and 100 nm (iii). a) Representative data. b) Log(posterior probability) heat maps. c) The highest scoring cluster proposal and d) histograms of the measured cluster radii. Blue dashed lines represent simulated values.

10 Supplementary Figure 10 Side by side comparison of methods applied to three simulated conditions. Side by side comparison of our method (I), Getis s method (II) and DBSCAN (III) applied to three simulated conditions (n = 100 datasets each). The conditions are Standard Conditions (representative dataset shown in Fig. 2a i), Standard Conditions but with larger clusters (representative dataset shown in Fig. 2a iii) and Standard Conditions with uneven background (representative dataset shown in Supplementary Fig. 6a ii).

11 Supplementary Figure 11 Sensitivity of the measured clustering descriptors to the prior settings. Sensitivity of the measured clustering descriptors (percentage of localisations in clusters, cluster radii, number of clusters per ROI and the number of localisations per cluster) to the prior settings. a) Illustration of the sensitivity of measured cluster descriptors to varying the Dirichlet process concentration coefficient, α. b) Sensitivity to the prior probability of any localisation being allocated to the background. c) Sensitivity to the prior distribution on the cluster radius. Two possible distributions are considered, one taken from experimental data (i) and a flat distribution between zero and half the size of the ROI (ii).

12 Supplementary Figure 12 Data preprocessing steps. Data preprocessing steps. a) Determination of the optimal merge time. Following the method of Annibale et al, we plot the total number of localisations in a representative image against the merge time for CD3ζ -meos3 in primary T cells. The average optimum merge time was found to be three frames (30 ms). This example is representative of four such plots. b and c) Representative histogram of the localisation precisions calculated for CD3ζ data in resting T cells (b) and in activated cells (c), using the method of Quan et al.

13 Supplementary Figure 13 Bayesian analysis of CD3ζ clustering in activated primary human T cells with localizations from the same ROI divided into two equally sized data sets. Bayesian analysis of CD3ζ clustering in activated primary human T cells with localisations from the same ROI divided into two equallysized data sets. a) and b) Highest scoring cluster proposals. c) and d) Log(posterior probability) heat maps.

14 Supplementary Figure 14 Data on CD3ζ clustering analysed by alternative approaches in resting and activated primary human T cells. Data on CD3ζ clustering analysed by alternative approaches in resting and activated primary human T cells. a) Representative cluster maps (n = 30) generated by Getis and Franklin s Local Point Pattern Analysis of a 3000 x 3000 nm area. b) Representative binary maps showing clustered areas. c) Ripley s K function (average of n = 30 regions). d) Pair correlation curves (average of n = 30 regions) and e) Cluster statistics on the percentage of localisations found in clusters, number of clusters per region and cluster radii extracted from the binary maps in b).