gaussian mixture model (gmm)  (MathWorks Inc)

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Scatterplot of R against f IC for all tumor voxels ( n = 11,519) and subjects (A; left side). The black contours show the 2D Gaussian mixture model (GMM) fit with each voxel data point color‐coded based on the probability of belonging to each component (blue, green, and red). Contours of the three individual GMM components are shown as smaller plots (right side). R and f IC maps of tumor ROIs were used to generate color‐coded posterior probability maps of each GMM component (B; Subject 6 shown as example).

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques:

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Model fit output of the Gaussian mixture model (GMM) of three clusters fitted to R and f IC for all tumor voxels. The table shows cluster ID as defined by histological analysis (Figures 4 and 6 ), mean values of R and f IC ( μ ) for each cluster, and the cluster fraction indicating the percentage of tumor voxel data that is associated with each Gaussian component.

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques:

Journal: Nmr in Biomedicine

Article Title: Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors

doi: 10.1002/nbm.70050

Figure Lengend Snippet: Gaussian mixture model (GMM) probability maps from the VERDICT cluster analysis of R and f IC (left columns) and classification maps from the histology analysis (right columns). The colors in the histology classification maps represent different tissue types: necrotic (red), fibrotic (blue), and viable cancer cells (green). Black pixels indicate areas where no stain was present. The colors in the VERDICT cluster maps represent the probability of each voxel belonging to the GMM clusters, with colors chosen for each cluster to best match with the histology maps.

Article Snippet: A 2D Gaussian mixture model (GMM) was then fitted to the f IC and R values of all tumor voxels using an algorithm based on the MATLAB function fitgmdist with 20 random initializations to avoid local optima and a regularization value of 3.5 × 10 −3 to avoid overfitting ( mathworks.com/matlabcentral/fileexchange/71496‐identification‐of‐subregions‐in‐parameter‐maps‐by‐gmm ) [ ].

Techniques: Staining

Journal: Cell reports

Article Title: Molecular basis for the increased fusion activity of the Ebola virus glycoprotein epidemic variant A82V: Insights from simulations and experiments

doi: 10.1016/j.celrep.2025.115521

Figure Lengend Snippet: (A) Schematic of the smFRET assay in which pseudovirions formed with the HIV-1 core are immobilized to enable imaging with TIRF microscopy. (B) A GP CL monomer with the sites of fluorophore attachment indicated (donor, green; acceptor, red. PDB: 5JQ3 with glycan cap removed to reflect GP CL ). (C) Example smFRET trajectories from individual GP CL trimers on pseudovirions with A82 (top) or V82 (bottom) GP CL . The experimental trajectories are shown in blue, overlaid with the idealized trajectory resulting from fitting to the 5-state HMM (red). The prefusion conformation is indicated with a gray bar, along with the observed intermediate conformations. (D) FRET histograms for A82 GP CL smFRET trajectories acquired under the indicated conditions. Histograms reflect the average of three independent groups of trajectories; error bars represent the standard error. Overlaid on the histograms are four Gaussian distributions (gray) for the four non-zero FRET states, with means determined through HMM analysis of the individual smFRET trajectories. The sum of the Gaussians is highlighted by the red line. The 0.8-FRET prefusion conformation and the 0.05-FRET state are indicated. The 0-FRET state has been removed from the histograms to facilitate visualization of the 0.05-FRET state. N indicates the number of smFRET trajectories used to compile each histogram. (E) FRET histograms for V82 GP CL as in (D). (F) Violin plots displaying the occupancy distribution in the 0.8-FRET prefusion conformation for each GP CL population imaged under the given conditions. Horizontal lines indicate the population mean occupancies; the gray circles and whiskers indicate the medians and quantiles, respectively. p values were determined by one-way ANOVA and multiple comparison test (ns, p > 0.05). (G) Violin plots displaying the occupancy distribution in the 0.05-FRET state, displayed as in (F). Numeric data are presented in . (H and I) Histograms of dwell times in the 0.8-FRET prefusion conformation extracted from the HMM analysis of smFRET trajectories (blue circles) acquired for (H) A82 and (I) V82 GP CL , under the indicated conditions. Histograms are displayed with logarithmically spaced bins to assist in visualization of the two time constants. Histograms were fit to double exponential functions A fast e x p t / t fast + A slow e x p t / t slow , where A fast and A slow are amplitudes, and t fast and t slow are the corresponding time constants. Double exponential fits are overlaid in solid red lines with the individual exponentials in dashed red lines. The two time constants are indicated with 95% confidence intervals in parentheses. (J) The amplitudes, A fast and A slow , determined in the exponential fitting in (A) and (B). Bars reflect the fitted values with error bars indicating the 95% confidence intervals. p values are indicated (ns, p > 0.05), determined by one-way ANOVA and multiple comparison test.

Article Snippet: Projection of the simulations into the eigenspace formed by the first three principal components and subsequent clustering using Gaussian mixture models was performed in Matlab.

Techniques: Smfret Assay, Imaging, Microscopy, Glycoproteomics, Comparison

Journal: bioRxiv

Article Title: Spike inference from calcium imaging data acquired with GCaMP8 indicators

doi: 10.1101/2025.03.03.641129

Figure Lengend Snippet: a , Examples of isolated action potentials with the corresponding ΔF/F trace (gray) and the inferred spike rate (blue) for an increasing standardized noise level ν . A low-noise population recording corresponds to ν ≈ 2, a higher-noise population recording to ν ≈ 8. Arrows indicate the time point when the true isolated action potential occurred (red arrow if not detected). Single action potentials are accurately inferred for low noise levels for GCaMP8m/s. b , Top left: Illustration of the integral of inferred action potentials (APs) in the shaded time window around the true action potential. Other panels: Number of detected action potentials (APs) for a true isolated single AP, plotted as a distribution (kernel density estimate). Shades of gray indicate the different noise levels (lowest noise level, dark grey; highest noise level, light grey). c , Fraction of APs correctly detected as a single AP (spike count >0.5 and <1.5) across datasets and noise levels. d , Fraction of APs correctly detected from noise (spike count >0.5) across datasets and noise levels. e , Percent of APs per neuron correctly detected as a single AP as defined in (c), averaged across noise levels ν = 2-4 for robustness. f , Percent of APs per neuron detected as defined in (d), averaged across noise levels ν = 2-4. g , Example of a calcium imaging recording (GCaMP8s ground truth) together with spike rates inferred using GC8s-tuned CASCADE. Isolated minimal events are visually detectable (circles). h , Histogram of inferred spike numbers for isolated events, together with a Gaussian mixture model (GMM) fit to the underlying data. The unitary amplitude, defined as the mean of the first Gaussian component, is used for auto-calibration. i , Auto-calibration with unitary amplitudes derived from GMM fits decreases the overestimation of the spike rate for isolated spike events and brings back outliers. (But see also ).

Article Snippet: A modified Gaussian mixture model (GMM) in MATLAB was then used to perform a quantal analysis from the histogram to infer the unitary amplitude.

Techniques: Isolation, Imaging, Derivative Assay

Journal: bioRxiv

Article Title: Spike inference from calcium imaging data acquired with GCaMP8 indicators

doi: 10.1101/2025.03.03.641129

Figure Lengend Snippet: To evaluate the transfer function of various CASCADE models and their nonlinearity, a synthetic ground truth was generated. The ground truth spike patterns consisted of experimentally recorded action potentials of excitatory neurons from refs. , , , (n = 237 neurons with variable recording duration, each represented by a gray line; 102,093 action potentials, 32.7 hours of recordings) to ensure naturalistic spike patterns. These experimentally obtained spike patterns were convolved with a double exponential kernel (rise time 5 ms, decay time 500 ms, peak amplitude 40% ΔF/F), with Gaussian noise added to reach a standardized noise level of “8”. Spike inference algorithms were applied to this entire linear synthetic dataset. From the inferred spike rates and the synthetic ground truth, transfer functions as in were retrieved, allowing to judge the linearity of the models trained on specific datasets. The Default CASCADE model exhibited the highest nonlinearity (black average transfer curve) compared to the weighted linear fit of the inferred spike rates (red dashed line); the sublinearity of the average transfer function at high firing rates reflects the supralinearity of the training data. CASCADE trained with GCaMP8 ground truth yielded a relatively linear transfer function, with mild but clear signs of saturation for models fine-tuned for GCaMP8f and GCaMP8s. The transfer function obtained from a model trained with GCaMP8m (GC8m-tuned CASCADE) data was the most linear. Since the models reflect the nonlinearities of their training data, these analyses indicate that GCaMP8, and in particular GCaMP8m, are distinctly more linear calcium indicators than for example GCaMP6 (which is the basis of most of the training data for Default CASCADE) across the firing rate regime that is typically covered by experimentally obtained ground truth recordings. The bottom right panel shows quantified deviations from linearity as introduced in for the different CASCADE models. Quantifications were pooled across multiple simulations with variable setting of the kernel parameters to emulate GCaMP8f, GCaMP8m, GCaMP8s, GCaMP7f and GCaMP6 with the kernel rise times (2, 3, 4, 11 and 50 ms, respectively), decay times (45, 80, 190, 100 and 300 ms) and peak amplitudes (55, 71, 81, 70 and 25 % ΔF/F). Deviations from linearity were reduced compared to Default CASCADE by 30% (GC8-trained), 19% (GC8f-trained), 60% (GC8m-trained) and 48% (GC8s-trained). Therefore, the GC8m-trained model exhibited the lowest deviation from linearity, suggesting the highest linearity of the GC8m indicator used for its training. All comparisons p ≪ 10 −10 , Wilcoxon signed-rank test, compared across n = 1422 instances of simulated neurons.

Article Snippet: A modified Gaussian mixture model (GMM) in MATLAB was then used to perform a quantal analysis from the histogram to infer the unitary amplitude.

Techniques: Generated

Journal: bioRxiv

Article Title: Spike inference from calcium imaging data acquired with GCaMP8 indicators

doi: 10.1101/2025.03.03.641129

Figure Lengend Snippet:

Article Snippet: A modified Gaussian mixture model (GMM) in MATLAB was then used to perform a quantal analysis from the histogram to infer the unitary amplitude.

Techniques: Isolation