Recent advancements in Spatial Transcriptomics (ST) technology have
facilitated detailed gene expression analysis within tissue contexts. However,
the high costs and methodological limitations of ST necessitate a more robust
predictive model. In response, this paper introduces TRIPLEX, a novel deep
learning framework designed to predict spatial gene expression from Whole Slide
Images (WSIs). TRIPLEX uniquely harnesses multi-resolution features, capturing
cellular morphology at individual spots, the local context around these spots,
and the global tissue organization. By integrating these features through an
effective fusion strategy, TRIPLEX achieves accurate gene expression
prediction. Our comprehensive benchmark study, conducted on three public ST
datasets and supplemented with Visium data from 10X Genomics, demonstrates that
TRIPLEX outperforms current state-of-the-art models in Mean Squared Error
(MSE), Mean Absolute Error (MAE), and Pearson Correlation Coefficient (PCC).
The model's predictions align closely with ground truth gene expression
profiles and tumor annotations, underscoring TRIPLEX's potential in advancing
cancer diagnosis and treatment.

TRIPLEX 是一个深度学习框架，通过整合多分辨率特征来准确预测基因表达，相较于现有的模型，在均方误差、平均绝对误差和皮尔逊相关系数方面表现优秀，并能够与基因表达谱和肿瘤注释密切匹配，强调其在癌症诊断和治疗方面的潜力。

通过整合多分辨率特征实现准确的空间基因表达预测

Accurate Spatial Gene Expression Prediction by integrating  Multi-resolution features

Spatial transcriptomics (ST) captures gene expression within distinct regions
(i.e., windows) of a tissue slide. Traditional supervised learning frameworks
applied to model ST are constrained to predicting expression from slide image
windows for gene types seen during training, failing to generalize to unseen
gene types. To overcome this limitation, we propose a semantic guided network
(SGN), a pioneering zero-shot framework for predicting gene expression from
slide image windows. Considering a gene type can be described by functionality
and phenotype, we dynamically embed a gene type to a vector per its
functionality and phenotype, and employ this vector to project slide image
windows to gene expression in feature space, unleashing zero-shot expression
prediction for unseen gene types. The gene type functionality and phenotype are
queried with a carefully designed prompt from a pre-trained large language
model (LLM). On standard benchmark datasets, we demonstrate competitive
zero-shot performance compared to past state-of-the-art supervised learning
approaches.

本研究提出了一种语义引导的网络，用于从滑动窗口的图像预测基因表达，解决了传统监督学习模型的局限性，并在标准基准数据集上展示了与过去最先进的监督学习方法相媲美的零样本表现。

零样本基因表达预测的空间转录组学分析

Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction

We propose a novel diverse feature selection method based on determinantal
point processes (DPPs). Our model enables one to flexibly define diversity
based on the covariance of features (similar to orthogonal matching pursuit) or
alternatively based on side information. We introduce our approach in the
context of Bayesian sparse regression, employing a DPP as a variational
approximation to the true spike and slab posterior distribution. We
subsequently show how this variational DPP approximation generalizes and
extends mean-field approximation, and can be learned efficiently by exploiting
the fast sampling properties of DPPs. Our motivating application comes from
bioinformatics, where we aim to identify a diverse set of genes whose
expression profiles predict a tumor type where the diversity is defined with
respect to a gene-gene interaction network. We also explore an application in
spatial statistics. In both cases, we demonstrate that the proposed method
yields significantly more diverse feature sets than classic sparse methods,
without compromising accuracy.

提出了一个基于行列式点加工（DPP）的新颖多样的特征选择方法，用于生物信息学中的癌症基因预测和空间统计，显著提高了特征多样性，不降低准确性。