stInfer

Overview

Background: The rapid development of spatial transcriptomics (ST) technology has enabled the measurement of transcript abundance while simultaneously obtaining the cell spatial locations. However, the high cost has hindered its widespread adoption. Furthermore, commonly used ST platforms are often low-resolution, which restricts their utility in investigating intricate tissue structures.

Methods: Here, we propose stInfer, a novel method that can infer gene expression in full transcriptomics range and enhance resolution in ST data using Hematoxylin and Eosin-stained (H&E) images. Specifically, H&E images can be segmented into patches according to the corresponding spot locations. Then a pre-trained visual model is applied to encode these patches into feature vectors. Finally, the gene expression of the target spot can be predicted by weighted K-Nearest Neighbors (KNN) algorithm.

Results: We evaluated stInfer in expression prediction and super-resolution tasks. Comprehensive results on breast cancer datasets demonstrates the effectiveness of the proposed method. In summary, stInfer is a powerful and lightweight tool that can infer gene expression from H&E images. It holds great promise for being widely applied to complex ST data to bring novel insights to structural compositions and microenvironments. Keywords: spatial transcriptomes, H&E image, expression inference, super resolution

Doc

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Prerequisites

Data

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Environment

It is recommended to use a Python version 3.9.

• set up conda environment for stInfer:

conda create -n stInfer python==3.9

conda activate stInfer

• You need to choose the appropriate dependency pytorch and dgl for your own environment, and we recommend the following pytorch==1.13.1 and dgl==0.9.0 with cudatoolkit==11.6:

conda install cudatoolkit=11.6 -c conda-forge
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install dgl-cu116 -f https://data.dgl.ai/wheels/repo.html

The other versions of pytorch and dgl can be installed from torch and dgl.

Installation

You can install stInfer as follows:

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Tutorials

The following are detailed tutorials. All tutorials were carried out on a notebook with a 11800H cpu and a 3070 8G gpu.

1. 1OldST.ipynb.

Reference: TODO