psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data

psupertime: supervised pseudotime analysis for time-series single-cell RNA-seq data

MOTIVATION: Improvements in single-cell RNA-seq technologies mean that studies measuring multiple experimental conditions, such as time series, have become more common. At present, few computational methods exist to infer time series-specific transcriptome changes, and such studies have therefore ty...

Full description

Bibliographic Details
Main Authors:	Claassen, M. (Author), Gupta, R. (Author), Macnair, W. (Author)
Format:	Article
Language:	English
Published:	NLM (Medline) 2022
Online Access:	View Fulltext in Publisher

Similar Items

PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data
by: Dongyuan Song, et al.
Published: (2021-04-01)

Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
by: Kelly Street, et al.
Published: (2018-06-01)

Molecular estimation of neurodegeneration pseudotime in older brains
by: Sumit Mukherjee, et al.
Published: (2020-11-01)

Pseudotime Dynamics in Melanoma Single-Cell Transcriptomes Reveals Different Mechanisms of Tumor Progression
by: Henry Loeffler-Wirth, et al.
Published: (2018-04-01)

A robust semi-supervised NMF model for single cell RNA-seq data
by: Peng Wu, et al.
Published: (2020-10-01)

DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data
by: Zhuo Wang, et al.
Published: (2017-05-01)

Author Correction: Molecular estimation of neurodegeneration pseudotime in older brains
by: Sumit Mukherjee, et al.
Published: (2020-12-01)

Integrating Deep Supervised, Self-Supervised and Unsupervised Learning for Single-Cell RNA-seq Clustering and Annotation
by: Liang Chen, et al.
Published: (2020-07-01)

AdImpute: An Imputation Method for Single-Cell RNA-Seq Data Based on Semi-Supervised Autoencoders
by: Li Xu, et al.
Published: (2021-09-01)

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
by: Jose Alquicira-Hernandez, et al.
Published: (2019-12-01)

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data
by: Vera-Khlara S. Oh, et al.
Published: (2021-02-01)

SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
by: Tao Peng, et al.
Published: (2019-05-01)

An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data
by: Xifang Sun, et al.
Published: (2019-09-01)

Assessment of supervised classification methods for the analysis of RNA-seq data
by: Abuelqumsan, Mustafa
Published: (2018)

Contrastive self-supervised clustering of scRNA-seq data
by: Ciortan, M., et al.
Published: (2021)

Contrastive self-supervised clustering of scRNA-seq data
by: Madalina Ciortan, et al.
Published: (2021-05-01)

Current Challenges in Cell-Type Discovery Through Single-Cell Data
by: De Vargas Roditi Laura, et al.
Published: (2015-01-01)

Dynamic distribution decomposition for single-cell snapshot time series identifies subpopulations and trajectories during iPSC reprogramming.
by: Jake P Taylor-King, et al.
Published: (2020-01-01)

A combination approach of pseudotime analysis and mathematical modeling for understanding drug-resistant mechanisms
by: Shigeyuki Magi, et al.
Published: (2021-09-01)

A Non-Negative Matrix Factorization-Based Framework for the Analysis of Multi-Class Time-Series Single-Cell RNA-Seq Data
by: Inuk Jung, et al.
Published: (2020-01-01)

DSAVE: Detection of misclassified cells in single-cell RNA-Seq data.
by: Johan Gustafsson, et al.
Published: (2020-01-01)

Methods for time series analysis of RNA-seq data with application to human Th17 cell differentiation
by: Aiho, Tarmo, et al.
Published: (2015)

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.
by: Kieran R Campbell, et al.
Published: (2016-11-01)

CEL-Seq: Single-Cell RNA-Seq by Multiplexed Linear Amplification
by: Tamar Hashimshony, et al.
Published: (2012-09-01)

Pathway activity analysis of bulk and single-cell RNA-Seq data
by: Jenkins, David
Published: (2019)

Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment
by: Yuheng Du, et al.
Published: (2020-05-01)

Detection and removal of barcode swapping in single-cell RNA-seq data
by: Jonathan A. Griffiths, et al.
Published: (2018-07-01)

Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey
by: Nicholas Lytal, et al.
Published: (2020-02-01)

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
by: Geng Chen, et al.
Published: (2019-04-01)

Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction
by: Wenjing Ma, et al.
Published: (2021-09-01)

Approximate inference of gene regulatory network models from RNA-Seq time series data
by: Thomas Thorne
Published: (2018-04-01)

Annotating cell types in human single-cell RNA-seq data with CellO
by: Matthew N. Bernstein, et al.
Published: (2021-09-01)

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
by: Peijie Lin, et al.
Published: (2017-03-01)

ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data
by: Dario Romagnoli, et al.
Published: (2018-12-01)

CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
by: Hashimshony, Tamar, et al.
Published: (2016)

Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data
by: Chunxiang Wang, et al.
Published: (2020-10-01)

Correction: Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.
by: PLOS Computational Biology Staff
Published: (2017-04-01)

Decontamination of ambient RNA in single-cell RNA-seq with DecontX
by: Shiyi Yang, et al.
Published: (2020-03-01)

Latent periodic process inference from single-cell RNA-seq data
by: Shaoheng Liang, et al.
Published: (2020-03-01)

A web server for comparative analysis of single-cell RNA-seq data
by: Amir Alavi, et al.
Published: (2018-11-01)