RESUMO
Single-cell transcriptomics (scRNA-seq) is revolutionizing biological research, yet it faces challenges such as inefficient transcript capture and noise. To address these challenges, methods like neighbor averaging or graph diffusion are used. These methods often rely on k-nearest neighbor graphs from low-dimensional manifolds. However, scRNA-seq data suffer from the 'curse of dimensionality', leading to the over-smoothing of data when using imputation methods. To overcome this, sc-PHENIX employs a PCA-UMAP diffusion method, which enhances the preservation of data structures and allows for a refined use of PCA dimensions and diffusion parameters (e.g., k-nearest neighbors, exponentiation of the Markov matrix) to minimize noise introduction. This approach enables a more accurate construction of the exponentiated Markov matrix (cell neighborhood graph), surpassing methods like MAGIC. sc-PHENIX significantly mitigates over-smoothing, as validated through various scRNA-seq datasets, demonstrating improved cell phenotype representation. Applied to a multicellular tumor spheroid dataset, sc-PHENIX identified known extreme phenotype states, showcasing its effectiveness. sc-PHENIX is open-source and available for use and modification.
RESUMO
The instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000 ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was <25% and >75%. Entropy decreased when the overlap was <25% and >75% and when the window length was <100 ms and >500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p < 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p < 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.
RESUMO
A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.