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Journal articles on the topic 'Pseudotime'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 June 2025

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1

Lee, Hyunjong, Hojoong Kim, Young Soo Choi, Hong Ryul Pyo, Myung-Ju Ahn, and Joon Young Choi. "Prognostic Significance of Pseudotime from Texture Parameters of FDG PET/CT in Locally Advanced Non-Small-Cell Lung Cancer with Tri-Modality Therapy." Cancers 14, no. 15 (2022): 3809. http://dx.doi.org/10.3390/cancers14153809.

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Texture analysis provides image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters are associated with tumor biology and clinical features, the types and implications of these parameters are complicated. We applied pseudotime analysis, which has recently been used to estimate changes in individual sample characteristics, to texture parameters from FDG PET/CT images of locally advanced non-small-cell lung cancer (NSCLC) patients undergoing neoadjuvant concurrent chemoradiation therapy (CCRT) followed by surgery. Our su

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Mao, Shuainan, Jiajia Liu, Weiling Zhao, and Xiaobo Zhou. "LVPT: Lazy Velocity Pseudotime Inference Method." Biomolecules 13, no. 8 (2023): 1242. http://dx.doi.org/10.3390/biom13081242.

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The emergence of RNA velocity has enriched our understanding of the dynamic transcriptional landscape within individual cells. In light of this breakthrough, we embarked on integrating RNA velocity with cellular pseudotime inference, aiming to improve the prediction of cell orders along biological trajectories beyond existing methods. Here, we developed LVPT, a novel method for pseudotime and trajectory inference. LVPT introduces a lazy probability to indicate the probability that the cell stays in the original state and calculates the transition matrix based on RNA velocity to provide the pro

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3

Lapborisuth, Kalsuda, Colin Farrell, and Matteo Pellegrini. "Pseudotime Analysis Reveals Exponential Trends in DNA Methylation Aging with Mortality Associated Timescales." Cells 11, no. 5 (2022): 767. http://dx.doi.org/10.3390/cells11050767.

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The epigenetic trajectory of DNA methylation profiles has a nonlinear relationship with time, reflecting rapid changes in DNA methylation early in life that progressively slow with age. In this study, we use pseudotime analysis to determine the functional form of these trajectories. Unlike epigenetic clocks that constrain the functional form of methylation changes with time, pseudotime analysis orders samples along a path, based on similarities in a latent dimension, to provide an unbiased trajectory. We show that pseudotime analysis can be applied to DNA methylation in human blood and brain t

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4

Zhai, Jingyi, Hongkai Ji, and Hui Jiang. "Reconstruction of Single-Cell Trajectories Using Stochastic Tree Search." Genes 14, no. 2 (2023): 318. http://dx.doi.org/10.3390/genes14020318.

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The recent advancement in single-cell RNA sequencing technologies enables the understanding of dynamic cellular processes at the single-cell level. Using trajectory inference methods, pseudotimes can be estimated based on reconstructed single-cell trajectories which can be further used to gain biological knowledge. Existing methods for modeling cell trajectories, such as minimal spanning tree or k-nearest neighbor graph, often lead to locally optimal solutions. In this paper, we propose a penalized likelihood-based framework and introduce a stochastic tree search (STS) algorithm aiming at the

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Haghverdi, Laleh, Maren Büttner, F. Alexander Wolf, Florian Buettner, and Fabian J. Theis. "Diffusion pseudotime robustly reconstructs lineage branching." Nature Methods 13, no. 10 (2016): 845–48. http://dx.doi.org/10.1038/nmeth.3971.

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6

Zhang, Miao, and Luis F. Ayala H. "Gas-Production-Data Analysis of Variable-Pressure-Drawdown/Variable-Rate Systems: A Density-Based Approach." SPE Reservoir Evaluation & Engineering 17, no. 04 (2014): 520–29. http://dx.doi.org/10.2118/172503-pa.

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Summary This study demonstrates that production-data analysis of variable-bottomhole-flowing-pressure/variable-rate gas wells under boundary-dominated flow (BDF) is possible by use of a density-based approach. In this approach, governing equations are expressed in terms of density variables and dimensionless viscosity/compressibility ratios. Previously, the methodology was successfully used to derive rescaled exponential models for gas-rate-decline analysis of wells primarily producing at constant bottomhole pressure (Ayala and Ye 2013a, b; Ayala and Zhang 2013; Ye and Ayala 2013; Zhang and Ay

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7

Liu, Jiajia, Mengyuan Yang, Weiling Zhao, and Xiaobo Zhou. "CCPE: cell cycle pseudotime estimation for single cell RNA-seq data." Nucleic Acids Research 50, no. 2 (2021): 704–16. http://dx.doi.org/10.1093/nar/gkab1236.

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Abstract Pseudotime analysis from scRNA-seq data enables to characterize the continuous progression of various biological processes, such as the cell cycle. Cell cycle plays an important role in cell fate decisions and differentiation and is often regarded as a confounder in scRNA-seq data analysis when analyzing the role of other factors. Therefore, accurate prediction of cell cycle pseudotime and identification of cell cycle stages are important steps for characterizing the development-related biological processes. Here, we develop CCPE, a novel cell cycle pseudotime estimation method to cha

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8

Mondal, Pronoy Kanti, Udit Surya Saha, and Indranil Mukhopadhyay. "PseudoGA: cell pseudotime reconstruction based on genetic algorithm." Nucleic Acids Research 49, no. 14 (2021): 7909–24. http://dx.doi.org/10.1093/nar/gkab457.

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Abstract Dynamic regulation of gene expression is often governed by progression through transient cell states. Bulk RNA-seq analysis can only detect average change in expression levels and is unable to identify this dynamics. Single cell RNA-seq presents an unprecedented opportunity that helps in placing the cells on a hypothetical time trajectory that reflects gradual transition of their transcriptomes. This continuum trajectory or ‘pseudotime’, may reveal the developmental pathway and provide us with information on dynamic transcriptomic changes and other biological processes. Existing appro

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9

Xu, Qian, Guanxun Li, Daniel Osorio, et al. "scInTime: A Computational Method Leveraging Single-Cell Trajectory and Gene Regulatory Networks to Identify Master Regulators of Cellular Differentiation." Genes 13, no. 2 (2022): 371. http://dx.doi.org/10.3390/genes13020371.

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Trajectory inference (TI) or pseudotime analysis has dramatically extended the analytical framework of single-cell RNA-seq data, allowing regulatory genes contributing to cell differentiation and those involved in various dynamic cellular processes to be identified. However, most TI analysis procedures deal with individual genes independently while overlooking the regulatory relations between genes. Integrating information from gene regulatory networks (GRNs) at different pseudotime points may lead to more interpretable TI results. To this end, we introduce scInTime—an unsupervised machine lea

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10

Turner, T. C., M. C. P. Sok, L. A. Hymel, et al. "Harnessing lipid signaling pathways to target specialized pro-angiogenic neutrophil subsets for regenerative immunotherapy." Science Advances 6, no. 44 (2020): eaba7702. http://dx.doi.org/10.1126/sciadv.aba7702.

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To gain insights into neutrophil heterogeneity dynamics in the context of sterile inflammation and wound healing, we performed a pseudotime analysis of single-cell flow cytometry data using the spanning-tree progression analysis of density-normalized events algorithm. This enables us to view neutrophil transitional subsets along a pseudotime trajectory and identify distinct VEGFR1, VEGFR2, and CXCR4 high-expressing pro-angiogenic neutrophils. While the proresolving lipid mediator aspirin-triggered resolvin D1 (AT-RvD1) has a known ability to limit neutrophil infiltration, our analysis uncovers

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11

Reid, John E., and Lorenz Wernisch. "Pseudotime estimation: deconfounding single cell time series." Bioinformatics 32, no. 19 (2016): 2973–80. http://dx.doi.org/10.1093/bioinformatics/btw372.

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12

Sun, Rui, Wenjie Cao, ShengXuan Li, Jian Jiang, Yazhou Shi, and Bengong Zhang. "scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy." PLOS Computational Biology 20, no. 11 (2024): e1012638. http://dx.doi.org/10.1371/journal.pcbi.1012638.

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Research on cell differentiation facilitates a deeper understanding of the fundamental processes of life, elucidates the intrinsic mechanisms underlying diseases such as cancer, and advances the development of therapeutics and precision medicine. Existing methods for inferring cell differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data primarily rely on static gene expression data to measure distances between cells and subsequently infer pseudotime trajectories. In this work, we introduce a novel method, scGRN-Entropy, for inferring cell differentiation trajectories and

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13

Iollo, Angelo, Geojoe Kuruvila, and Shlomo Ta'asan. "Pseudotime method for shape design of Euler flows." AIAA Journal 34, no. 9 (1996): 1807–13. http://dx.doi.org/10.2514/3.13311.

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14

Escobar, Freddy Humberto, Aura María López, and José Humberto Cantillo. "EFFECT OF THE PSEUDOTIME FUNCTION ON GAS RESERVOIR DRAINAGE AREA DETERMINATION." CT&F - Ciencia, Tecnología y Futuro 3, no. 3 (2007): 113–24. http://dx.doi.org/10.29047/01225383.480.

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The gas flow equation is normally linearized to allow the liquid solution of the diffusivity equation to satisfy gas behavior when analyzing transient test data of gas reservoirs. When wellbore storage conditions are insignificant, drawdown tests are best analyzed using the pseudopressure function. On the other hand, buildup pressure tests require linearization of both pseudotime and pseudopressure. It is not the case for the TDS technique which is indifferently applied to either drawdown or buildup tests. However, whichever the case, pseudotime has certain effect at very long testing times in

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15

Jongkittinarukorn, Kittiphong, Nick Last, Sarfaraz Ahmed Jokhio, Freddy Humberto Escobar, and Jirawat Chewaroungroaj. "A simple approach to dynamic material balance for a dry-gas reservoir." Journal of Petroleum Exploration and Production Technology 11, no. 7 (2021): 3011–29. http://dx.doi.org/10.1007/s13202-021-01231-0.

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AbstractThe dynamic material balance methodology can be used to estimate gas initially-in-place using only production and PVT data. With this methodology, reservoir pressure is obtained without requiring the well to be shut in; it is therefore superior to the static material balance method since there is no loss of production. However, the technique requires iterative calculations and numerical integration of gas pseudotime and is quite complex to implement in practice. A simpler and equally accurate methodology is proposed in this study. It requires only production and PVT data and also does

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16

Yu, Kaijia, Henrik Koblitz Rasmussen, and Anne Ladegaard Skov. "Experimental evaluation of the pseudotime principle for nonisothermal polymer flows." Journal of Rheology 55, no. 5 (2011): 1059–67. http://dx.doi.org/10.1122/1.3610439.

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17

Neumaier, Arnold, and Vladimir A. Mandelshtam. "Pseudotime Schrödinger Equation with Absorbing Potential for Quantum Scattering Calculations." Physical Review Letters 86, no. 22 (2001): 5031–34. http://dx.doi.org/10.1103/physrevlett.86.5031.

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18

Campbell, Kieran R., and Christopher Yau. "A descriptive marker gene approach to single-cell pseudotime inference." Bioinformatics 35, no. 1 (2018): 28–35. http://dx.doi.org/10.1093/bioinformatics/bty498.

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19

Yin, Mianmian, Kamir Hiam, Irina Proekt, et al. "Correlation of autoantigen-specific Treg frequency with development of spontaneous organ-specific autoimmunity in a mouse model of uveitis." Journal of Immunology 206, no. 1_Supplement (2021): 51.05. http://dx.doi.org/10.4049/jimmunol.206.supp.51.05.

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Abstract About 50% of AireGW/+Lyn−/− mice developed autoimmune uveitis, and this was accompanied by the expansion of interphotoreceptor retinoid-binding protein (IRBP) specific CD4+T cells. Moreover, the response to IRBP was necessary for disease initiation, as deletion of IRBP prevented uveitis. Mass cytometry (CyTOF) analysis of CD4+T cells in the retina of the mice with uveitis identified activated T cell subsets characterized as Ly6Chi effectors, PD-1hi effectors, and Treg. To study cellular mechanisms that promoted development of uveitis, we did single-cell RNA sequencing (scRNA-seq) of e

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20

Deshmukh, Abhijeet P., Suhas V. Vasaikar, Katarzyna Tomczak, et al. "Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing." Proceedings of the National Academy of Sciences 118, no. 19 (2021): e2102050118. http://dx.doi.org/10.1073/pnas.2102050118.

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The epithelial-to-mesenchymal transition (EMT) plays a critical role during normal development and in cancer progression. EMT is induced by various signaling pathways, including TGF-β, BMP, Wnt–β-catenin, NOTCH, Shh, and receptor tyrosine kinases. In this study, we performed single-cell RNA sequencing on MCF10A cells undergoing EMT by TGF-β1 stimulation. Our comprehensive analysis revealed that cells progress through EMT at different paces. Using pseudotime clustering reconstruction of gene-expression profiles during EMT, we found sequential and parallel activation of EMT signaling pathways. W

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21

Sun, Liang, Qianyi Ma, Chunhui Cai, et al. "scDown: A Pipeline for Single-Cell RNA-Seq Downstream Analysis." International Journal of Molecular Sciences 26, no. 11 (2025): 5297. https://doi.org/10.3390/ijms26115297.

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Single-cell transcriptomics data are analyzed using two popular tools, Seurat and Scanpy. Multiple separate tools are used downstream of Seurat and Scanpy cell annotation to study cell differentiation and communication, including cell proportion difference analysis between conditions, pseudotime and trajectory analyses to study cell transition, and cell–cell communication analysis. To automate the integrative cell differentiation and communication analyses of single-cell RNA-seq data, we developed a single-cell RNA-seq downstream analysis pipeline called “scDown”. This R package includes cell

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22

Yao, Shuhei, Kaito Uemura, Shigeto Seno, and Hideo Matsuda. "A Novel Method for Gene Regulatory Network Inference with Pseudotime Data Using Information Criterion." International Journal of Bioscience, Biochemistry and Bioinformatics 12, no. 3 (2022): 43–52. http://dx.doi.org/10.17706/ijbbb.2022.12.3.43-52.

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23

Smolander, Johannes, Sini Junttila, Mikko S. Venäläinen, and Laura L. Elo. "scShaper: an ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data." Bioinformatics 38, no. 5 (2021): 1328–35. http://dx.doi.org/10.1093/bioinformatics/btab831.

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Abstract Motivation Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods. Results We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a

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24

Chen, H. Y., and S. W. Poston. "Application of a Pseudotime Function To Permit Better Decline-Curve Analysis." SPE Formation Evaluation 4, no. 03 (1989): 421–28. http://dx.doi.org/10.2118/17051-pa.

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25

Attar, Peter J. "Using Pseudotime Solution Framework in Harmonic Balance Methods for Aperiodic Problems." AIAA Journal 51, no. 12 (2013): 2982–87. http://dx.doi.org/10.2514/1.j052635.

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26

Alahmari, Saeed S., Andrew Schultz, Jordan Albrecht, et al. "Abstract 3703: Cell identity revealed by precise cell cycle state mapping links data modalities." Cancer Research 85, no. 8_Supplement_1 (2025): 3703. https://doi.org/10.1158/1538-7445.am2025-3703.

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Abstract Purpose: Despite advances in single-cell technologies, integrating multimodal data to capture both the genetic and phenotypic dynamics of tumor cells in their microenvironment remains a challenge. Approach: We leverage variations in cell cycle state as a natural barcode to link data across different modalities. We have demonstrated the feasibility of this method in a gastric cancer cell line (NCI-N87) using two widely different technologies: single-cell RNA sequencing and live-cell imaging. Our approach begins with the transfection ofFUCCI probes into cells, followed by acquisition of

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27

Finjord, Jan. "An Analytical Study of Pseudotime for Pressure Drawdown in a Gas Reservoir." SPE Formation Evaluation 4, no. 02 (1989): 287–92. http://dx.doi.org/10.2118/15205-pa.

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28

Zhang, M., and L. F. F. Ayala H. "Gas-Rate Forecasting in Boundary-Dominated Flow: Constant-Bottomhole-Pressure Decline Analysis by Use of Rescaled Exponential Models." SPE Journal 19, no. 03 (2013): 410–17. http://dx.doi.org/10.2118/168217-pa.

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Summary Gas-well performance forecasting during boundary-dominated flow (BDF) is largely based on the application of pseudopressure, pseudotime, and material-balance-pseudotime concepts to rate, pressure, and time data. Recently, Ayala H. and Ye (2012; 2013) and Ye and Ayala H. (2013) demonstrated the convenience and importance of a rescaled exponential model that successfully forecasted gas-well decline in BDF by use of density-based dimensionless parameters in place of pseudovariables. In this study, the interdependability and interchangeability of these methodologies is formally demonstrate

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Loeffler-Wirth, Henry, Hans Binder, Edith Willscher, Tobias Gerber, and Manfred Kunz. "Pseudotime Dynamics in Melanoma Single-Cell Transcriptomes Reveals Different Mechanisms of Tumor Progression." Biology 7, no. 2 (2018): 23. http://dx.doi.org/10.3390/biology7020023.

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30

Campbell, Kieran R., and Christopher Yau. "Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference." PLOS Computational Biology 12, no. 11 (2016): e1005212. http://dx.doi.org/10.1371/journal.pcbi.1005212.

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Pagliaro, Sarah, Christophe Desterke, Herve Acloque, Annelise Bennaceur-Griscelli, and Ali G. Turhan. "Single Cell Transcriptome in Chronic Myeloid Leukemia (CML): Pseudotime Analysis Reveals a Rare Population with Embryonic Stem Cell Features and Druggable Intricated Transitional Stem Cell States." Blood 132, Supplement 1 (2018): 934. http://dx.doi.org/10.1182/blood-2018-99-110679.

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Abstract Recent experimental data suggest that the heterogeneity of CML stem cells can be due not only to their differential BCR-ABL expression but also to the development of unique molecular events generating functional consequences in terms of their stem cell potential. Understanding these events at diagnosis of the disease and during tyrosine kinase inhibitor (TKI) therapies is of major interest as this can explain the clonal evolution and selection explaining resistance and persistence of LSC. Single cell (sc) RNA-transcriptome technology allows to study cell heterogeneity with simultaneou

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Durmaz, Arda, and Jacob G. Scott. "Stability of scRNA-Seq Analysis Workflows is Susceptible to Preprocessing and is Mitigated by Regularized or Supervised Approaches." Evolutionary Bioinformatics 18 (January 2022): 117693432211230. http://dx.doi.org/10.1177/11769343221123050.

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Background: Statistical methods developed to address various questions in single-cell datasets show increased variability to different parameter regimes. In order to delineate further the robustness of commonly utilized methods for single-cell RNA-Seq, we aimed to comprehensively review scRNA-Seq analysis workflows in the setting of dimension reduction, clustering, and trajectory inference. Methods: We utilized datasets with temporal single-cell transcriptomics profiles from public repositories. Combining multiple methods at each level of the workflow, we have performed over 6 k analysis and e

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Johansson, Emil, Priscilla Kerkman, Lydia Scharf, et al. "Hierarchical Clustering and Trajectory Analyses Reveal Viremia-Independent B-Cell Perturbations in HIV-2 Infection." Cells 11, no. 19 (2022): 3142. http://dx.doi.org/10.3390/cells11193142.

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Time to AIDS in HIV-2 infection is approximately twice as long compared to in HIV-1 infection. Despite reduced viremia, HIV-2-infected individuals display signs of chronic immune activation. In HIV-1-infected individuals, B-cell hyperactivation is driven by continuous antigen exposure. However, the contribution of viremia to B-cell perturbations in HIV-2-infected individuals remains largely unexplored. Here, we used polychromatic flow cytometry, consensus hierarchical clustering and pseudotime trajectory inference to characterize B-cells in HIV-1- or HIV-2-infected and in HIV seronegative indi

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Fang, Meichen, Gennady Gorin, and Lior Pachter. "Trajectory inference from single-cell genomics data with a process time model." PLOS Computational Biology 21, no. 1 (2025): e1012752. https://doi.org/10.1371/journal.pcbi.1012752.

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Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of “pseudotime” that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of “process time” via a principl

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Yao, Mingze, Tinglin Ren, Yuanqing Pan, et al. "A New Generation of Lineage Tracing Dynamically Records Cell Fate Choices." International Journal of Molecular Sciences 23, no. 9 (2022): 5021. http://dx.doi.org/10.3390/ijms23095021.

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Reconstructing the development of lineage relationships and cell fate mapping has been a fundamental problem in biology. Using advanced molecular biology and single-cell RNA sequencing, we have profiled transcriptomes at the single-cell level and mapped cell fates during development. Recently, CRISPR/Cas9 barcode editing for large-scale lineage tracing has been used to reconstruct the pseudotime trajectory of cells and improve lineage tracing accuracy. This review presents the progress of the latest CbLT (CRISPR-based Lineage Tracing) and discusses the current limitations and potential technic

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Ahmed, Sumon. "Effects of Pseudotime Uncertainty in Downstream Analysis for Single-cell Data: A Case Study." International Journal of Computer Applications 175, no. 18 (2020): 1–6. http://dx.doi.org/10.5120/ijca2020920696.

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Song, Dongyuan, and Jingyi Jessica Li. "PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data." Genome Biology 22, no. 1 (2021). http://dx.doi.org/10.1186/s13059-021-02341-y.

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AbstractTo investigate molecular mechanisms underlying cell state changes, a crucial analysis is to identify differentially expressed (DE) genes along the pseudotime inferred from single-cell RNA-sequencing data. However, existing methods do not account for pseudotime inference uncertainty, and they have either ill-posed p-values or restrictive models. Here we propose PseudotimeDE, a DE gene identification method that adapts to various pseudotime inference methods, accounts for pseudotime inference uncertainty, and outputs well-calibrated p-values. Comprehensive simulations and real-data appli

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Rams, Mona, and Tim O. F. Conrad. "Dictionary learning allows model-free pseudotime estimation of transcriptomic data." BMC Genomics 23, no. 1 (2022). http://dx.doi.org/10.1186/s12864-021-08276-9.

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Abstract Background Pseudotime estimation from dynamic single-cell transcriptomic data enables characterisation and understanding of the underlying processes, for example developmental processes. Various pseudotime estimation methods have been proposed during the last years. Typically, these methods start with a dimension reduction step because the low-dimensional representation is usually easier to analyse. Approaches such as PCA, ICA or t-SNE belong to the most widely used methods for dimension reduction in pseudotime estimation methods. However, these methods usually make assumptions on the

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Lee, Hyunjong, and Hongyoon Choi. "Investigating the Clinico-Molecular and Immunological Evolution of Lung Adenocarcinoma Using Pseudotime Analysis." Frontiers in Oncology 12 (March 4, 2022). http://dx.doi.org/10.3389/fonc.2022.828505.

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IntroductionAs the molecular features of lung adenocarcinoma (LUAD) have been evaluated as a cross-sectional study, the course of tumor characteristics has not been modeled. The temporal evolution of the tumor immune microenvironment (TIME), as well as the clinico-molecular features of LUAD, could provide a precise strategy for immunotherapy and surrogate biomarkers for the course of LUAD.MethodsA pseudotime trajectory was constructed in patients with LUAD from the Cancer Genome Atlas and non-small cell lung cancer radiogenomics datasets. Correlation analyses were performed between clinical fe

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He, Bing, Shannon L. Risacher, Andrew J. Saykin, and Jingwen Yan. "Multi‐modal Imaging‐based peudotime analysis for AD progression." Alzheimer's & Dementia 19, S17 (2023). http://dx.doi.org/10.1002/alz.077057.

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AbstractBackgroundAlzheimer’s disease (AD) progresses along a continuum and begins many years before symptom onset. AD takes a long progression time, making it hard to study the entire spectrum of AD development. Pseudotime analysis widely used in cell lineage analysis have been applied to AD gene expression data to understand the molecular changes along progression. In this work, we performed a neuroimaging‐based pseudotime analysis to explore the progression patterns of different imaging modalities.MethodData were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database

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Heath, Laura M., Richa Batra, Gregory A. Cary, et al. "Pseudo‐temporal models of Alzheimer’s Disease progression across brain‐derived transcriptomics, proteomics, and metabolomics." Alzheimer's & Dementia 19, S12 (2023). http://dx.doi.org/10.1002/alz.080320.

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AbstractBackgroundWe applied a pseudo‐temporal model to multi‐omic data from individuals with heterogeneous neuropathology to examine molecular changes across the spectrum of Alzheimer’s Disease (AD) progression.MethodsManifold learning algorithms ‘order’ samples based on similarity of expression or abundance patterns to calculate a ‘trajectory’ of disease progression. We applied this approach to RNAseq (N = 1078), TMT‐proteomics (N = 602), and metabolomics (N = 500) profiles of human postmortem brain samples from the ROS/MAP study within the Accelerating Medicine Partnership‐AD consortia (AMP

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Zhang, Yifan, Duc Tran, Tin Nguyen, Sergiu M. Dascalu, and Frederick C. Harris. "A robust and accurate single-cell data trajectory inference method using ensemble pseudotime." BMC Bioinformatics 24, no. 1 (2023). http://dx.doi.org/10.1186/s12859-023-05179-2.

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Abstract Background The advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in individual cell resolution. In recent years, many trajectory inference methods have been developed. They have focused on using the graph method to infer the trajectory using single-cell data, and then calculate the geodesic distance as the pseudotime. However, these methods are vulnerable to errors caused by the inferred trajectory. Therefore, the calculated pseudotime suffers from such errors. Results We proposed a novel framework for traje

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Mukherjee, Sumit, Laura Heath, Christoph Preuss, et al. "Molecular estimation of neurodegeneration pseudotime in older brains." Nature Communications 11, no. 1 (2020). http://dx.doi.org/10.1038/s41467-020-19622-y.

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AbstractThe temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage–or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the b

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Ma, Li, Na Zhou, Rongjun Zou, et al. "Single-Cell RNA Sequencing and Quantitative Proteomics Analysis Elucidate Marker Genes and Molecular Mechanisms in Hypoplastic Left Heart Patients With Heart Failure." Frontiers in Cell and Developmental Biology 9 (February 25, 2021). http://dx.doi.org/10.3389/fcell.2021.617853.

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ObjectiveTo probe markers and molecular mechanisms of the hypoplastic left heart (HLH) by single-cell RNA sequencing (scRNA-seq) and quantitative proteomics analysis.MethodsFollowing data preprocessing, scRNA-seq data of pluripotent stem cell (iPSC)-derived cardiomyocytes from one HLH patient and one control were analyzed by the Seurat package in R. Cell clusters were characterized, which was followed by a pseudotime analysis. Markers in the pseudotime analysis were utilized for functional enrichment analysis. Quantitative proteomics analysis was based on peripheral blood samples from HLH pati

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Sasaki, Kotaro, Akiko Oguchi, Keren Cheng, et al. "The embryonic ontogeny of the gonadal somatic cells in mice and monkeys." March 25, 2021. https://doi.org/10.5281/zenodo.4632324.

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The codes and raw data for <em> The embryonic ontogeny of the gonadal somatic cells in mice and monkeys, </em>published on<em> Cell Reports.</em> This code is for showing how we did DiffusionMap and pseudotime analysis. And the embedding of DiffusionMap and pseudotime also depend on your own platform and R session.

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Lyu, Hongqiang, Erhu Liu, Zhifang Wu, Yao Li, Yuan Liu, and Xiaoran Yin. "scHiCPTR: unsupervised pseudotime inference through dual graph refinement for single-cell Hi-C data." Bioinformatics, October 7, 2022. http://dx.doi.org/10.1093/bioinformatics/btac670.

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Abstract Motivation The emerging single-cell Hi-C technology provides opportunities to study dynamics of chromosomal organization. How to construct a pseudotime path using single-cell Hi-C contact matrices to order cells along developmental trajectory is a challenging topic, since these matrices produced by the technology are inherently high-dimensional and sparse, they suffer from noises and biases, and the topology of trajectory underlying them may be diverse. Results We present scHiCPTR, an unsupervised graph-based pipeline to infer pseudotime from single-cell Hi-C contact matrices. It prov

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Song, Qi, Jingtao Wang, and Ziv Bar-Joseph. "scSTEM: clustering pseudotime ordered single-cell data." Genome Biology 23, no. 1 (2022). http://dx.doi.org/10.1186/s13059-022-02716-9.

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AbstractWe develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes and assigns a p-value to clusters enabling the identification of significant profiles and comparison of profiles across different paths. Application of scSTEM to several scRNA-seq datasets demonstrates its usefulness and ability to improve downstream analysis of biological processes. scSTEM is available at https://github.com/alexQiSong/scST

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Sugihara, Reiichi, Yuki Kato, Tomoya Mori, and Yukio Kawahara. "Alignment of single-cell trajectory trees with CAPITAL." Nature Communications 13, no. 1 (2022). http://dx.doi.org/10.1038/s41467-022-33681-3.

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AbstractGlobal alignment of complex pseudotime trajectories between different single-cell RNA-seq datasets is challenging, as existing tools mainly focus on linear alignment of single-cell trajectories. Here we present CAPITAL (comparative analysis of pseudotime trajectory inference with tree alignment), a method for comparing single-cell trajectories with tree alignment whereby branching trajectories can be automatically compared. Computational tests on synthetic datasets and authentic bone marrow cells datasets indicate that CAPITAL has achieved accurate and robust alignments of trajectory t

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Nazifa, Tasnim Hia, and Ahmed Sumon. "A Literature Review on Pseudotime Estimation Using Single-Cell Data." June 23, 2023. https://doi.org/10.5281/zenodo.8074673.

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Transcriptomics and lineage tracing have enabled the development of computational methods for determining developmental trajectories, allowing for high-resolution examination of cellular dynamics. Since 2014, numerous methodologies have been proposed in the field of pseudotime estimation. Although these algorithms are potent, they are still in their infancy, and consideration must be taken when employing their strengths and weaknesses. This paper describes prominent computational approaches in depth for pseudotime estimation based on the information they utilize, as well as describing future c

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Dongyuan, Song, and Jessica Li Jingyi. "PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data." April 6, 2021. https://doi.org/10.5281/zenodo.4663580.

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