Predicting Future States with Spatial Point Processes in Single Molecule Resolution Spatial Transcriptomics
In this paper, we introduce a pipeline based on XGboost to predict the future distribution of cells that are expressed by the Sog-D gene (active cells) in both the Anterior to posterior (AP) and the Dorsal to Ventral (DV) axis of the Drosophila in embryogenesis process. This method provides insights about how cells and living organisms control gene expression in super resolution whole embryo spatial transcriptomics imaging at sub cellular, single molecule resolution. An XGboost model was used to predict the next stage active distribution based on the previous one. To achieve this goal, we leveraged temporally resolved, spatial point processes by including Ripley's K-function in conjunction with the cell's state in each stage of embryogenesis, and found average predictive accuracy of active cell distribution. This tool is analogous to RNA Velocity for spatially resolved developmental biology, from one data point we can predict future spatially resolved gene expression using features from the spatial point processes.
Code (0)
등록된 구현이 없습니다.
Tasks
Point ProcessesregressionSuper-ResolutionSimilar Papers 제목 키워드 기반
FutureNav: Unified World-Action Modeling for Vision-and-Language Navigation
Vision-and-language navigation (VLN) in continuous environments requires an agent to ground instructions in egocentric observations while maintaining spatial understanding across long action sequences. Recent navigation …
Investigating Low Data, Confidence Aware Image Prediction on Smooth Repetitive Videos using Gaussian Processes
The ability to predict future states is crucial to informed decision-making while interacting with dynamic environments. With cameras providing a prevalent and information-rich sensing modality, the problem of predicting…
Decision MakingGaussian ProcessesBeyond Hawkes: Neural Multi-event Forecasting on Spatio-temporal Point Processes
Predicting discrete events in time and space has many scientific applications, such as predicting hazardous earthquakes and outbreaks of infectious diseases. History-dependent spatio-temporal Hawkes processes are often u…
Point ProcessesRules of the Road: Predicting Driving Behavior with a Convolutional Model of Semantic Interactions
We focus on the problem of predicting future states of entities in complex, real-world driving scenarios. Previous research has used low-level signals to predict short time horizons, and has not addressed how to leverage…
STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Prediction
Existing works typically treat spatial-temporal prediction as the task of learning a function $F$ to transform historical observations to future observations. We further decompose this cross-time transformation into thre…