Journal: PLoS Genetics

Article Title: Genome-Wide Patterns of Arabidopsis Gene Expression in Nature

doi: 10.1371/journal.pgen.1002662

Figure Lengend Snippet: The numbers listed to the left indicate the cluster number identified in the silhouette analyses. The four most significant (i.e., lowest p values) GO term categories enriched for each cluster are shown on the left. The vegetative and flowering samples are indicated at the top, and the rows corresponding to various flower development genes are shown. Several developmental genes associated with bolting and flower development are highlighted. Scale: from brightest blue equals most down-regulated to brightest red equals most up-regulated.

Article Snippet: Although an r = 0.7 has been arbitrarily used in other microarray analyses to define the number of clusters within a data set, we used the silhouette function in MATLAB (MathWorks 2009) to find an appropriate number of distinct clusters of genes that behave similarly across the data sets .

Techniques:

Journal: PLoS Genetics

Article Title: Genome-Wide Patterns of Arabidopsis Gene Expression in Nature

doi: 10.1371/journal.pgen.1002662

Figure Lengend Snippet: Silhouette analysis defined (A) 188 clusters in Sha and (B) 109 clusters in Bay-0. Each row indicates a specific cluster. The components of the transcriptional variance explained by environmental (precipitation, maximum and minimum daily temperatures) and developmental factors (age and flowering status) are shown. The percent of the transcriptional variance explained by a factor is shown in color (blue, <25%; yellow, between 25–50%; orange, between 50–75%; red, >75%). We also examined each cluster for enrichment of genes shown to be differentially expressed in previously-studied transcriptome analyses. A red bar indicates that the cluster is enriched in genes differentially expressed under that stress condition.

Article Snippet: Although an r = 0.7 has been arbitrarily used in other microarray analyses to define the number of clusters within a data set, we used the silhouette function in MATLAB (MathWorks 2009) to find an appropriate number of distinct clusters of genes that behave similarly across the data sets .

Techniques:

Journal: Journal of Neural Engineering

Article Title: Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus

doi: 10.1088/1741-2552/ad5b19

Figure Lengend Snippet: Top-left panel: Averaged beta-band (13–30 Hz) power spectral density half-violin plots. During the pre-processing stage, beta-band power magnitude measures from hippocampal contacts were used for both the ‘Go’ and ‘No-go’ conditions as input features. Top-right panel: Clusters were extracted by principal component analysis from all frequency bins within the beta-band power (13–30 Hz) and gray-matter contacts in the hippocampus, the first two principal components were used for cross-validated DA. Bottom panel: Silhouette score box plots from all participants grouped by ‘Combined’ (gray), ‘Go’ (green), and ‘No-go’ (red) trials.

Article Snippet: The Silhouette score (calculated using the Matlab function ‘silhouette’) measures how closely a data point resides within its own cluster (cohesion or tightness), as well as how distinct the same data point is from other clusters (separation).

Techniques:

Journal: Journal of Neural Engineering

Article Title: Beta-band power classification of go/no-go arm-reaching responses in the human hippocampus

doi: 10.1088/1741-2552/ad5b19

Figure Lengend Snippet: Evaluation of Go and No-Go clusters: Silhouette scores for ‘Go’ and ‘No-go’ task conditions for each participant, detailing mean and median values for each task condition and for both conditions combined. The final two rows display the average and median silhouette scores across all participants for each task condition and for both conditions combined.

Article Snippet: The Silhouette score (calculated using the Matlab function ‘silhouette’) measures how closely a data point resides within its own cluster (cohesion or tightness), as well as how distinct the same data point is from other clusters (separation).

Techniques:

Journal: Frontiers in Neurology

Article Title: Quantitative evaluation of electrographic response to electroconvulsive therapy in super-refractory status epilepticus

doi: 10.3389/fneur.2024.1493336

Figure Lengend Snippet: Evolution of pre- vs. post-ECT silhouette scores across sessions (days). (A) Individual session examples of high (upper panel) and low (lower panel) silhouette scores for data comparing 10 min immediately before vs. after ECT. Note the clear separation between nearly all pre-stimulation data points (turquoise to blue) and post-stimulation data points (red to orange) in the upper panel, suggesting an immediate and persistent difference in the scalp EEG linelength measure (surrogate for epileptiform activity) after ECT is delivered. Data in the lower panel is largely overlapping, suggesting poor separability and thus minimal no change related to ECT during this session. (B) Silhouette scores for all evaluated sessions across patients.

Article Snippet: We calculated silhouette scores ( ) ( silhouette.m function in MATLAB) for each ECT session using these labels in order to quantify changes in the EEG data associated with ECT.

Techniques: Activity Assay

Journal: Frontiers in Neurology

Article Title: Quantitative evaluation of electrographic response to electroconvulsive therapy in super-refractory status epilepticus

doi: 10.3389/fneur.2024.1493336

Figure Lengend Snippet: Evolution of pre- vs. post-ECT silhouette scores up to 60 min immediately post-ECT. (A) Schematic of analysis in panel (B,C) in which, for each session, consecutive 5-min periods of data are each compared to the same baseline of 5 min immediately preceding ECT. (B) Silhouette scores for each session (gray traces) of an example patient (Pt 3) illustrating the estimated separability between pre-ECT segment [blue period in panel (A) ] vs. post-ECT segments over time [consecutive red-orange segments in panel (A) ]. Green traces illustrate the average of all sessions for this patient. (C) Silhouette score means (across sessions) similar to green trace in panel (A) , shown for all patients (note Pt 1 and Pt 7 restricted to 5-min mark due to limited post-ECT data across sessions, ).

Article Snippet: We calculated silhouette scores ( ) ( silhouette.m function in MATLAB) for each ECT session using these labels in order to quantify changes in the EEG data associated with ECT.

Techniques: