A Guide to Trajectory Inference and RNA Velocity

Weiler, Philipp; Berge, Koen Van Den; Street, Kelly; Tiberi, Simone

doi:10.1007/978-1-0716-2756-3_14

Cited by 12 publications

(10 citation statements)

References 28 publications

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“…If phase portraits lack the expected shape, RNA velocity may be inferred incorrectly. Moreover, if a gene includes multiple, pronounced kinetics, lineage-specific models are more appropriate 69 . Cases in which RNA velocity is inferred incorrectly include the presence of transcriptional bursts 70 , 71 .…”

Section: Transcriptomementioning

confidence: 99%

Best practices for single-cell analysis across modalities

et al. 2023

Self Cite

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Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

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Section: Transcriptomementioning

confidence: 99%

Best practices for single-cell analysis across modalities

et al. 2023

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“…Secondly, snRNA‐seq is favorable over scRNA‐seq as it provides a higher fraction of unspliced pre‐mRNA, owing to the subcellular localization from which RNA was extracted. This feature is crucial for determining the transient cellular dynamics of gene expression with RNA velocity analysis (La Manno et al, 2018; Bergen et al, 2020; Weiler et al, 2023). Indeed, we observed a higher fraction of unspliced pre‐mRNA in our snRNA‐seq data (~33% across all relevant cell types, Figure S9A, B) compared to less than 5% commonly seen in plant scRNA‐seq experiments (Jean‐Baptiste et al, 2019; Zhang et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

A high‐resolution transcriptomic atlas depicting nitrogen fixation and nodule development in soybean

Sun

Wang

Yang

et al. 2023

JIPB

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Although root nodules are essential for biological nitrogen fixation in legumes, the cell types and molecular regulatory mechanisms contributing to nodule development and nitrogen fixation in determinate nodule legumes, such as soybean (Glycine max), remain incompletely understood. Here, we generated a single‐nucleus resolution transcriptomic atlas of soybean roots and nodules at 14 days post inoculation (dpi) and annotated 17 major cell types, including six that are specific to nodules. We identified the specific cell types responsible for each step in the ureides synthesis pathway, which enables spatial compartmentalization of biochemical reactions during soybean nitrogen fixation. By utilizing RNA velocity analysis, we reconstructed the differentiation dynamics of soybean nodules, which differs from those of indeterminate nodules in Medicago truncatula. Moreover, we identified several putative regulators of soybean nodulation and two of these genes, GmbHLH93 and GmSCL1, were as‐yet uncharacterized in soybean. Overexpression of each gene in soybean hairy root systems validated their respective roles in nodulation. Notably, enrichment for cytokinin‐related genes in soybean nodules led to identification of the cytokinin receptor, GmCRE1, as a prominent component of the nodulation pathway. GmCRE1 knockout in soybean resulted in a striking nodule phenotype with decreased nitrogen fixation zone and depletion of leghemoglobins, accompanied by downregulation of nodule‐specific gene expression, as well as almost complete abrogation of biological nitrogen fixation. In summary, this study provides a comprehensive perspective of the cellular landscape during soybean nodulation, shedding light on the underlying metabolic and developmental mechanisms of soybean nodule formation.

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“…The SVG detection algorithm is used to identify marker genes responsible for the tumor microenvironment. Finally, cross-cluster CCI analysis is performed using TraSig [ 101 ], and within-cluster analysis is carried out by label transfer from Seurat to identify active ligand–receptor pairs. The results showed conST’s capability to detect IDC, DCIS and edge tumor cell regions and active L-R pairs within IDC regions.…”

Section: Survey Of DL Models For Srt Analysismentioning

confidence: 99%

Deep learning in spatially resolved transcriptomics: a comprehensive technical view

Zahedi,

Ghamsari,

Argha

et al. 2024

Briefings in Bioinformatics

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Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

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A Guide to Trajectory Inference and RNA Velocity

Cited by 12 publications

References 28 publications

Best practices for single-cell analysis across modalities

Best practices for single-cell analysis across modalities

A high‐resolution transcriptomic atlas depicting nitrogen fixation and nodule development in soybean

Deep learning in spatially resolved transcriptomics: a comprehensive technical view

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