Biology subjects

Pachter, L.

Publications and source records attributed to Pachter, L..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Depth normalization for single-cell genomics count data

Genomics data analysis requires normalization of feature counts that stabilizes technical variance, accounts for variable cell sequencing depth, and preserves monotonicity of within-cell feature abundances. We show that normalization via an optimal variance stabilizing transform for negative binomial count data followed by a proportional fitting step (PFlog) is the only feature-relabeling-equivariant method satisfying the three desiderata. We demonstrate superior performance of this method, which is equivalent to a shifted centered-log ratio transform, in comparison to other normalizations on numerous benchmarks across hundreds of single-cell RNA-seq datasets. We further show that both the shifted-log scale and centered-log ratio geometry are important for preserving PCA and k-NN structure.

bioinformatics↗

bioRxiv2022

RNA velocity unraveled

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

biophysics↗

bioRxiv2021

Efficient pre-processing of Single-cell ATAC-seq data

Single-cell and single-nucleus genomics assays are becoming increasingly complex, with multiple measurements of distinct modalities performed concurrently resulting in "multimodal" readouts. While multimodal single-cell and single-nucleus genomics offers the potential to better understand how distinct cellular processes are coordinated, there can be technical and cost tradeoffs associated with increasing the number of measurement modes. To assess some of the tradeoffs inherent in multimodal assays, we have developed snATAK for preprocessing sequencing-based high-throughput assays that measure single-nucleus chromatin accessibility. Coupled with kallisto bustools for single-nucleus RNA-seq preprocessing, the snATAK workflow can be used for uniform preprocessing of 10x Genomics Multiome and single-nucleus ATAC-seq, SHARE-seq, ISSAAC-seq, spatial ATAC-seq and other chromatin-related assays. Using snATAK, we are able to perform cross-platform comparisons and quantify some of the tradeoffs between Multiome and unregistered single-nucleus RNA-seq/ATAC-seq experiments. We also show that snATAK can be used to assess allele concordance between paired RNAseq and ATACseq. snATAK is available at https://github.com/pachterlab/snATAK/.

bioinformatics↗

bioRxiv2021

Interpretable and tractable models of transcriptional noise for the rational design of single-molecule quantification experiments

The question of how cell-to-cell differences in transcription rate affect RNA count distributions is fundamental for understanding biological processes underlying transcription. We argue that answering this question requires quantitative models that are both interpretable (describing concrete biophysical phenomena) and tractable (amenable to mathematical analysis). This enables the identification of experiments which best discriminate between competing hypotheses. As a proof of principle, we introduce a simple but flexible class of models involving a stochastic transcription rate coupled to a discrete stochastic RNA transcription and splicing process, and compare and contrast two biologically plausible hypotheses about transcription rate variation. One assumes variation is due to DNA experiencing mechanical strain, while the other assumes it is due to regulator number fluctuations. Although biophysically distinct, these models are mathematically similar, and we show they are hard to distinguish without comparing whole predicted probability distributions. Our work illustrates the importance of theory-guided data collection, and introduces a general framework for constructing and solving mathematically nontrivial continuous-discrete stochastic models. Significance StatementThe interpretation of transcriptomic observations requires detailed models of biophysical noise that can be compared and fit to experimental data. Models of intrinsic noise, describing stochasticity in molecular reactions, and extrinsic noise, describing cell-to-cell variation, are particularly common. However, integrating and solving them is challenging, and previous results are largely limited to summary statistics. We examine two mechanistically grounded stochastic models of transcriptional variation and demonstrate that (1) well-known regimes naturally emerge in limiting cases, and (2) the choice of noise model significantly affects the RNA distributions, but not the lower moments, offering a route to model identification and inference. This approach provides a simple and biophysically interpretable means to construct and unify models of transcriptional variation.

biophysics↗

bioRxiv2021

The Specious Art of Single-Cell Genomics

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to two or three dimensions to produce all-in-one visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to two, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data, and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration, to enable hypothesis-driven biological discovery.

genomics↗

bioRxiv2021

Length Biases in Single-Cell RNA Sequencing of pre-mRNA

Single-molecule pre-mRNA and mRNA sequencing data can be modeled and analyzed using the Markov chain formalism to yield genome-wide insights into transcription. However, quantitative inference with such data requires careful assessment and understanding of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data, and explore the mechanistic implications. A biological explanation for this phenomenon within our modeling framework requires unrealistic transcriptional parameters, leading us to posit a length-based model of capture bias. We provide solutions for this model, and use them to find concordant and mechanistically plausible parameter trends across data from multiple single-cell RNA-seq experiments in several species.

biophysics↗

bioRxiv2021

Museum of Spatial Transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

bioinformatics↗

bioRxiv2021

scvi-tools: a library for deep probabilistic analysis of single-cell omics data

AO_SCPLOWBSTRACTC_SCPLOWProbabilistic models have provided the underpinnings for state-of-the-art performance in many single-cell omics data analysis tasks, including dimensionality reduction, clustering, differential expression, annotation, removal of unwanted variation, and integration across modalities. Many of the models being deployed are amenable to scalable stochastic inference techniques, and accordingly they are able to process single-cell datasets of realistic and growing sizes. However, the community-wide adoption of probabilistic approaches is hindered by a fractured software ecosystem resulting in an array of packages with distinct, and often complex interfaces. To address this issue, we developed scvi-tools (https://scvi-tools.org), a Python package that implements a variety of leading probabilistic methods. These methods, which cover many fundamental analysis tasks, are accessible through a standardized, easy-to-use interface with direct links to Scanpy, Seurat, and Bioconductor workflows. By standardizing the implementations, we were able to develop and reuse novel functionalities across different models, such as support for complex study designs through nonlinear removal of unwanted variation due to multiple covariates and reference-query integration via scArches. The extensible software building blocks that underlie scvi-tools also enable a developer environment in which new probabilistic models for single cell omics can be efficiently developed, benchmarked, and deployed. We demonstrate this through a code-efficient reimplementation of Stereoscope for deconvolution of spatial transcriptomics profiles. By catering to both the end user and developer audiences, we expect scvi-tools to become an essential software dependency and serve to formulate a community standard for probabilistic modeling of single cell omics.

bioinformatics↗

bioRxiv2021

Analytical solutions of the chemical master equation with bursty production and isomerization reactions

Splicing cascades that alter gene products post-transcriptionally also affect expression dynamics. We study a class of processes and associated distributions that emerge from a bursty promoter model coupled to a path graph of downstream mRNA splicing, and more generally examine the behavior of finite-activity jump drivers coupled to a directed acyclic graph of splicing with one or more roots. These solutions provide full time-dependent joint distributions for an arbitrary number of species, offering qualitative and quantitative insights about how splicing can regulate expression dynamics. Finally, we derive a set of quantitative constraints on the minimum complexity necessary to reproduce gene co-expression patterns using synchronized burst models. We validate these findings by analyzing long-read sequencing data, where we find evidence of expression patterns largely consistent with these constraints.

biophysics↗

bioRxiv2021

A novel approach to comparative RNA-Seq does not support a conserved set of genes underlying animal regeneration

Molecular studies of animal regeneration typically focus on conserved genes and signaling pathways that underlie morphogenesis. To date, a holistic analysis of gene expression across animals has not been attempted, as it presents a suite of problems related to differences in experimental design and gene homology. By combining orthology analysis with a novel statistical method for testing gene enrichment across large datasets, we are able to test whether biological processes across organisms share transcriptional regulation. We applied this method to six publicly available RNA-seq datasets from diverse examples of animal regeneration. We recovered 160 conserved orthologous gene clusters, which are enriched in structural genes as opposed to those regulating morphogenesis. A breakdown of gene presence/absence provides only limited support for the conservation of pathways typically implicated in regeneration, such as Wnt signaling and cell pluripotency. Specifically, these pathways are only conserved if we allow gene paralogs to be interchangeable through evolution. Overall, our analysis does not support the hypothesis that a shared set of ancestral genes underlie regeneration mechanisms in animals. The methods described in this paper will be broadly applicable for studying the genetic underpinnings of traits across distantly related organisms.

evolutionary biology↗

bioRxiv2021

Low-cost, scalable, and automated fluid sampling for fluidics applications

We present colosseum, a low-cost, modular, and automated fluid sampling device for scalable fluidic applications. The colosseum fraction collector uses a single motor, can be built for less than $100 using off-the-shelf and 3D-printed components, and can be assembled in less than an hour. Build Instructions and source files are available at https://github.com/pachterlab/colosseum.

bioengineering↗

bioRxiv2021

Benchmarking of lightweight-mapping based single-cell RNA-seq pre-processing

We compare and benchmark the two lightweight-mapping tools that have been developed for pre-processing single-cell RNA-seq data, namely the kallisto-bustools and Salmon-Alevin-fry programs. We find that they output similar results, and to the extent that there are differences, they are irrelevant for downstream analysis. However, the Salmon-Alevin-fry program is significantly slower and requires much more memory to run, making it much more expensive to process large datasets limiting its use to larger servers.

bioinformatics↗

bioRxiv2021

Whole Animal Multiplexed Single-Cell RNA-Seq Reveals Plasticity of Clytia Medusa Cell Types

We present an organism-wide, transcriptomic cell atlas of the hydrozoan medusa Clytia hemisphaerica, and determine how its component cell types respond to starvation. Utilizing multiplexed scRNA-seq, in which individual animals were indexed and pooled from control and perturbation conditions into a single sequencing run, we avoid artifacts from batch effects and are able to discern shifts in cell state in response to organismal perturbations. This work serves as a foundation for future studies of development, function, and plasticity in a genetically tractable jellyfish species. Moreover, we introduce a powerful workflow for high-resolution, whole animal, multiplexed single-cell genomics (WHAM-seq) that is readily adaptable to other traditional or non-traditional model organisms.

systems biology↗

bioRxiv2021

Direct simulation of a stochastically driven multi-step birth-death process

1The description of transcription as a stochastic process provides a framework for the analysis of intrinsic and extrinsic noise in cells. To better understand the behaviors and possible extensions of existing models, we design an exact stochastic simulation algorithm for a multimolecular transcriptional system with an Ornstein-Uhlenbeck birth rate that is implemented via a special function-based time-stepping algorithm. We demonstrate that its joint copy-number distributions reduce to analytically well-studied cases in several limiting regimes, and suggest avenues for generalizations.

biophysics↗

bioRxiv2020

Intrinsic and extrinsic noise are distinguishable in a synthesis - export - degradation model of mRNA production

Intrinsic and extrinsic noise sources in gene expression, originating respectively from transcriptional stochasticity and from differences between cells, complicate the determination of transcriptional models. In particularly degenerate cases, the two noise sources are altogether impossible to distinguish. However, the incorporation of downstream processing, such as the mRNA splicing and export implicated in gene expression buffering, recovers the ability to identify the relevant source of noise. We report analytical copy-number distributions, discuss the noise sources qualitative effects on lower moments, and provide simulation routines for both models.

biophysics↗

bioRxiv2020

Addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays allows for the removal of amplification bias in the estimation of gene abundances. We show that UMIs can also be used to address a problem resulting from incomplete sequencing of amplified molecules in sequencing libraries that can lead to bias in gene abundance estimates. Our method, called BUTTERFLY, is based on a zero truncated negative binomial estimator and is implemented in the kallisto bustools single-cell RNA-seq workflow. We demonstrate its efficacy using a range of datasets and show that it can invert the relative abundance of certain genes in cases of a pooled amplification paradox.

bioinformatics↗

bioRxiv2020

log(x+1)* and log(1+x)*

Single-cell RNA-seq technologies have been successfully employed over the past decade to generate many high resolution cell atlases. These have proved invaluable in recent efforts aimed at understanding the cell type specificity of host genes involved in SARS-CoV-2 infections. While single-cell atlases are based on well-sampled highly-expressed genes, many of the genes of interest for understanding SARS-CoV-2 can be expressed at very low levels. Common assumptions underlying standard single-cell analyses dont hold when examining low-expressed genes, with the result that standard workflows can produce misleading results. Key PointsLowly expressed genes in single-cell RNA-seq can be easliy misanalyzed. log(1+x) count normalization introduces errors for lowly expressed genes The average log(1+x) expression differs considerably from log(x) when x is small An alternative approach is to use the fraction of cells with non-zero expression

bioinformatics↗

bioRxiv2020

Decrease in ACE2 mRNA expression in aged mouse lung

Angiotensin-converting enzyme 2 (ACE2) has been identified as a critical receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This has led to extensive speculation on the role of ACE2 in disease severity, and in particular, whether variation in its expression can explain higher mortality in older individuals. We examine this question in mouse lung and show that 24-month old mice have significantly reduced ACE2 mRNA expression relative to 3-month old mice. The differences appear to be localized to ciliated cells.

molecular biology↗