Interventions play a pivotal role in the study of complex biological systems.
In drug discovery, genetic interventions (such as CRISPR base editing) have
become central to both identifying potential therapeutic targets and
understanding a drug's mechanism of action. With the advancement of CRISPR and
the proliferation of genome-scale analyses such as transcriptomics, a new
challenge is to navigate the vast combinatorial space of concurrent genetic
interventions. Addressing this, our work concentrates on estimating the effects
of pairwise genetic combinations on the cellular transcriptome. We introduce
two novel contributions: Salt, a biologically-inspired baseline that posits the
mostly additive nature of combination effects, and Peper, a deep learning model
that extends Salt's additive assumption to achieve unprecedented accuracy. Our
comprehensive comparison against existing state-of-the-art methods, grounded in
diverse metrics, and our out-of-distribution analysis highlight the limitations
of current models in realistic settings. This analysis underscores the
necessity for improved modelling techniques and data acquisition strategies,
paving the way for more effective exploration of genetic intervention effects.

通过对基因干预的影响进行估计，本研究聚焦于基因对的组合对细胞转录组的效应，并引入了 Salt 和 Peper 两种新方法，Salt 从生物学启发并假设组合效应主要是加性的，而 Peper 则通过深度学习拓展了 Salt 的加性假设以实现前所未有的准确性，与现有先进方法进行全面比较并在现实环境中进行越界分析突出了当前模型的局限性，凸显了改进建模技术和数据获取策略的必要性，为更有效地探索基因干预效应铺平了道路。

盐胡椒调味品与季节组合干预预测

Season combinatorial intervention predictions with Salt & Peper

Advances in single-cell omics allow for unprecedented insights into the
transcription profiles of individual cells. When combined with large-scale
perturbation screens, through which specific biological mechanisms can be
targeted, these technologies allow for measuring the effect of targeted
perturbations on the whole transcriptome. These advances provide an opportunity
to better understand the causative role of genes in complex biological
processes such as gene regulation, disease progression or cellular development.
However, the high-dimensional nature of the data, coupled with the intricate
complexity of biological systems renders this task nontrivial. Within the
machine learning community, there has been a recent increase of interest in
causality, with a focus on adapting established causal techniques and
algorithms to handle high-dimensional data. In this perspective, we delineate
the application of these methodologies within the realm of single-cell genomics
and their challenges. We first present the model that underlies most of current
causal approaches to single-cell biology and discuss and challenge the
assumptions it entails from the biological point of view. We then identify open
problems in the application of causal approaches to single-cell data:
generalising to unseen environments, learning interpretable models, and
learning causal models of dynamics. For each problem, we discuss how various
research directions - including the development of computational approaches and
the adaptation of experimental protocols - may offer ways forward, or on the
contrary pose some difficulties. With the advent of single cell atlases and
increasing perturbation data, we expect causal models to become a crucial tool
for informed experimental design.

单细胞组学的高维数据与生物学系统的复杂性共同构成了因果关系的挑战，本研究旨在应用机器学习方法解决这一问题，讨论了单细胞基因组学中因果推断方法的应用、相关的假设、存在的问题以及未来的研究方向，并预测因果模型将成为未来实验设计的重要工具。

单细胞基因组的因果机器学习

Causal machine learning for single-cell genomics

A molecular and cellular understanding of how SARS-CoV-2 variably infects and
causes severe COVID-19 remains a bottleneck in developing interventions to end
the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2
infection and COVID-19 severity by identifying transcriptomic patterns and cell
types associated with SARS-CoV-2 infection and COVID-19 severity. To do this,
we developed a new approach to generating self-supervised edge features. We
propose a model that builds on Graph Attention Networks (GAT), creates edge
features using self-supervised learning, and ingests these edge features via a
Set Transformer. This model achieves significant improvements in predicting the
disease state of individual cells, given their transcriptome. We apply our
model to single-cell RNA sequencing datasets of SARS-CoV-2 infected lung
organoids and bronchoalveolar lavage fluid samples of patients with COVID-19,
achieving state-of-the-art performance on both datasets with our model. We then
borrow from the field of explainable AI (XAI) to identify the features (genes)
and cell types that discriminate bystander vs. infected cells across time and
moderate vs. severe COVID-19 disease. To the best of our knowledge, this
represents the first application of deep learning to identifying the molecular
and cellular determinants of SARS-CoV-2 infection and COVID-19 severity using
single-cell omics data.

利用深度学习识别转录组模式与细胞类型，对 SARS-CoV-2 感染及其导致 COVID-19 病情严重程度的生物学进行研究，提出了一种模型并应用于单细胞组学数据集，首次识别 SARS-CoV-2 感染及 COVID-19 严重程度的分子和细胞学决定因素。