Alongside scvi-tools v1.5.0, we're shipping two new packages that round out the ecosystem in very different
directions: scVIVA-Tools, which consolidates seven spatial transcriptomics models into a single toolkit, and
scVI-Tools MCP, which puts scvi-tools knowledge directly in reach of LLM coding assistants like Claude.
We’re proud to introduce scvi‑tools v1.4, encompassing major advances in modeling, data loading, computational scalability, metric integration, and interpretability in single-cell analytics.
Featuring nine new or enhanced models, optimized for spatial, cytometry, methylation, perturbation, and multi‑omic data, it also introduces custom data loaders for large-scale datasets, multi‑GPU model training, on-the-fly metric tuning, and integrated model interpretability.
This article delves into each enhancement with depth, including detailed insights, illustrative figures, and manuscript references.
The task of deconvolution of spot-based spatial transcriptomics (ST) data consists in finding the cellular composition in spots of ST assays. Indeed, by design each spot consists of several individual cells. Our lab developed destVI as a tool to study cell type composition of spots (1). In the case where one expects within cell type variation (i.e., activation states), DestVI was designed to infer the activation state of individual cell types in each spot and therefore gives additional resolution of cell composition over competing algorithms. In our own benchmarking of destVI, we found comparable performance in prediction of cell type proportions to other state of the art algorithms like Cell2Location (2). A recent benchmarking study compared the performance of several gene imputation as well as deconvolution methods. Given the ever increasing number of deconvolution methods, the effort to benchmark those tools on a variety of simulated data is timely (3). In two of the benchmarking cases, DestVI had the worst performance. Along with the recent acceptance of the manuscript, we published several fixes to the codebase in scvi-tools v0.16.0, repeated those experiments and analyzed the reasons for failure of spot deconvolution in the given experiments. Briefly, we show that the poor performance was due to the mini-batch size used during optimization, and resolving this help recovering competitive accuracy. Additionally, we provide the user with a heuristic for future use.
Today we officially released scvi-tools version 0.9.0 (changelog). This release marks the culmination of five months of work on the backend of the codebase, which came after three months of work on the frontend.
In this short note, we officially introduce scvi-tools as a readily usable codebase that contains many implementations of probabilistic single-cell omics methods, and also features a high-level interface to accelerate the model development process. We start with some historical notes about our previous codebase, which was mostly used for internal developments in the last three years. We then describe the obstacles we found to its external adoption, and the foundational idea behind the new scvi-tools work: a high-level deep probabilistic programming library specialized for single-cell omics data.
While stochastic gradient-based optimization is highly successful for setting weights and other differentiable parameters of a neural network, it is in general useless for setting hyperparameters -- non-differentiable parameters that control the structure of the network (e.g. the number of hidden layers, or the dropout rate) or settings of the optimizer itself (e.g., the learning rate schedule). Yet finding good settings for hyperparameters is essential for good performance for deep methods like scVI. Furthermore, as pointed out by Hu and Greene (2019) selecting hyperparameters is necessary in order to compare different machine learning models, especially if those are substantially sensitive to hyperparameter variations.
Droplet- based single-cell RNA sequencing (scRNA-seq) datasets typically contain at least 90% zero entries. How can we best model these zeros? Recent work focused on modeling zeros with a mixture of count distributions. The first component is meant to reflect whether such an entry can be explained solely by the limited amount of sampling (on average ~5% or less of the molecules in the cell). The second component is generally used to reflect "surprising" zeros caused by measurement bias, transient transcriptional noise (e.g., "bursty" gene with a short mRNA half life), or true longer-term heterogeneity that can not be captured by a similified (low dimensional) representation of the data. Among others, zero-inflated distributions (i.e., zero-inflated negative binomial) have been widely adopted to model gene expression levels (1, 2).