principal component analysis (pca) using built in functions utilising singular value decomposition with mean centring  (MathWorks Inc)

Journal: Frontiers in Neuroscience

Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

doi: 10.3389/fnins.2023.1151309

Figure Lengend Snippet: The top-3 independent components of the spiking response of each trial type. (A) shows each stimulation type and the corresponding independent components over the trial time. Positive coefficients are correlated with spiking activity while negative coefficients are anti-correlated with spiking activity. In the scatter plots below, each component is shown as an axis and each trial is plotted as a point within the three dimensions. Exemplar trials are highlighted and shown in insets with spike rate over time. (B) shows how the component weights (boxes) scale the component shapes to describe the features of the mean firing rate of an example channel. The corresponding blue and green arrows point to the deviations in mean firing rate while the purple arrow and line generally indicate the background firing rate that are captured by the respective component and its weight. (C) shows the reconstruction (shaded yellow) of the mean spike rate of an example channel (black line) using the descriptive weightings of the independent components.

Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

Techniques: Activity Assay

Journal: Frontiers in Neuroscience

Article Title: Post-ischemic reorganization of sensory responses in cerebral cortex

doi: 10.3389/fnins.2023.1151309

Figure Lengend Snippet: Combined independent component analysis of the sensory response and its modulation. (A) shows the mean weights of the components sorted by stimulation type and area which are displayed in (B) . Positive values point to the presence of that component in the response while negative values indicate an inverse relationship; the error bars show the standard error of the mean. (C) displays the prediction of area and lesion volume for component 2 and 3 scores by the GLME model as compared to a linear fit. (D) highlights the changes in the component scores between Solenoid (yellow) and ICMS + Solenoid trials (purple) for each channel in an experimental block of an exemplar animal. (E) shows the reconstructed rates for each stimulation type by area. The mean component scores were used to weight each component and reconstruct the average response in spiking to stimulation.

Article Snippet: We first applied principal components analysis (PCA; MATLAB R2017a + ‘pca’ function with ‘Algorithm’ parameter set to ‘svd’) to qualitatively describe the different types of evoked responses for each condition, applying a singular value decomposition to the mean channel spike rates separately for each stimulus type; then, using the groupings for which the same basis subspace could accurately reconstruct the original observations, we seeded a reconstructed-independent components analysis algorithm (r-ICA; MATLAB R2017a + ‘rica’ function from the Statistics and Machine Learning Toolbox) using the top-3 combined-basis eigenvectors to recover a basis for the sets of components described above ( ).

Techniques: Blocking Assay

Journal: NAR Genomics and Bioinformatics

Article Title: In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

doi: 10.1093/nargab/lqaa112

Figure Lengend Snippet: Analysis of in silico data at a whole genome level points to in trans effects within the genome. ( A ) Variability of copy number levels genome-wide is governed by prominent origins. Results of a PCA analysis of the in silico copy number data, shown as a biplot of the first two principal components. Dots correspond to simulations and black vectors expose each origin's contribution to the first two components, both in terms of magnitude and direction (marked here for the two most prominent ones). ( B ) Heatmap of DNA content (rows: simulations, columns: origins) for 100 simulations at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$16{\boldsymbol{C}}$\end{document} after clustering with a k -means algorithm and k = 3. Color indicates DNA amplification levels, expressed as the log ratio of individual versus genome mean number of copies. Identified clusters are marked with different colors. ( C ) Scatterplot of number of copies for origins Ori III-11 and Ori III-118 shows a negative correlation ( ρ = −0.4). Colors correspond to simulations belonging to each of the three clusters identified in B. ( D ) Evolution of re-replication over time. Heatmap of DNA content for simulations of (B) at an earlier DNA content of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$2{\boldsymbol{C}}$\end{document} shows no cluster-specific patterns at a low-re-replication context. ( E ) Underlying characteristics of DNA re-replication. In cis effects between adjacent loci. Passive re-replication of inactive origins from their efficient neighbors leads to increased copy numbers and implicitly increases their firing activity. ( F ) In trans effects between distant loci. Increased amplification of one locus leads to in trans suppression of a distant locus. ( G ) Emerging properties of DNA re-replication, depending on the level of analysis. ( H ) In silico re-replication profiles. Simulation results reveal many possible genotypes within a population, shown here in a schematic view for three hypothetical origins. Although the total DNA content is the same in all four single cells, individual copy number levels vary greatly.

Article Snippet: To compute the principal components of the data we used the MATLAB function implementation of Principal Component Analysis (PCA) and visualized the results (variable loadings and principal components) using a biplot.

Techniques: In Silico, Genome Wide, DNA Amplification, Activity Assay, Amplification