PieParty: Visualizing cells from scRNA-seq data as pie charts

Single cell RNA sequencing (scRNA-seq) has been a transformative technology in many research fields. Dimensional reduction techniques such as UMAP and tSNE are used to visualize scRNA-seq data in two or three dimensions in order for cells to be clustered in biologically meaningful ways. Subsequently, gene expression is frequently mapped onto these plots to show the distribution of gene expression across the plots, for instance to distinguish cell types. However, plotting each cell with only one color leads to repetitive and unintuitive representations. Here, we present Pie Party, which allows scRNA-seq data to be plotted such that every cell is represented as a pie chart, and every slice in the pie charts corresponds to the gene expression of individual genes. This allows for the simultaneous visualization of the expression of multiple genes and gene networks. The resulting figures are information dense, space efficient and highly intuitive. PieParty is publicly available on GitHub at https://github.com/harbourlab/PieParty.

Effects of Dimensional Reduction Techniques on Structural Damage Assessment Under Uncertainty

In this paper, we present a study of dimensional reduction techniques for structural damage assessment of time-varying structures under uncertainty. Discrete tracking of the frequency response and the mode shape curvature index method is employed to perform damage assessment. Assessment of spontaneous damage in deteriorating structures is important as it can have potential benefits in improving their safety and performance. Most of the available damage assessment techniques incorporate the usage of system identification and classification techniques for detecting damage, location, and/or severity; however, much work is needed in the area of dimensional reduction in order to compress the ever-increasing data and facilitate decision-making in damage assessment classification. A comparison of dimensional reduction techniques is presented and evaluated in terms of separating damaged from undamaged data sets under two types of uncertainty, structural deterioration and environmental uncertainties. The use of a recursive principal component analysis for detecting and tracking structural deterioration and spontaneous damage is evaluated via computational simulations. The results of this study reveal that dimensional reduction techniques can greatly enhance structural damage assessment under uncertainties. This paper compares multiple dimensional reduction techniques by identifying their weaknesses and strengths.

Scaled Acoustics Experiments and Vibration Prediction Based Structural Health Monitoring

Vibration and acoustic-based health monitoring techniques are presented to monitor structural health under dynamic environment. In order to extract damage sensitive features, linear and nonlinear dimensional reduction techniques are applied and compared. First, a vibration numerical study based on the damage index method is used to provide both location and severity of impact damage. Next, controlled scaled experimental measurements are taken to investigate the aeroacoustic properties of sub-scale wings under known damage conditions. The aeroacoustic nature of the flow field in and around generic aircraft wing damage is determined to characterize the physical mechanism of noise generated by the damage and its applicability to battle damage detection. Simulated battle damage is investigated using a baseline, and two damage models introduced; namely, (1) an undamaged wing as baseline, (2) chordwise-spanwise-partial-penetration (SCPP), and (3) spanwise-chordwise-full-penetration (SCFP). Dimensional reduction techniques are employed to extract time-frequency domain features, which can be used to detect the presence of structural damage. Results are given to illustrate effectiveness of this approach.