DenoIST

DenoIST identifies and removes contamination in Image-based Spatial Transcriptomics data, using a transposed Poisson mixture model with local neighbourhood offsets to infer genes that are likely to be due to neighbourhood contamination rather than endogenous expression.

Key Features:

• Contamination Identification: Uses a transposed Poisson mixture model to distinguish contaminant genes from endogenous expression.
• Local Neighbourhood Offsets: Incorporates spatial information from local neighbourhoods to model contamination patterns in image-based spatial transcriptomics data.

Scientific Applications:

• Spatial Transcriptomics Data Denoising: Removes neighbourhood-derived contamination to improve accuracy of gene expression measurements in image-based spatial transcriptomics datasets.

Methodology:

DenoIST employs a transposed Poisson mixture model with local neighbourhood offsets to infer genes attributable to neighbourhood contamination rather than endogenous expression.