scholarly journals Impulse model-based differential expression analysis of time course sequencing data

2017 ◽  
Author(s):  
David S. Fischer ◽  
Fabian J. Theis ◽  
Nir Yosef
Keyword(s):  
Differential Expression ◽  
Time Course ◽  
State Transition ◽  
Stem Cell Differentiation ◽  
Cellular Systems ◽  
Embryonic Stem Cell Differentiation ◽  
Sequencing Data ◽  
Cell State ◽  
Course Sequencing ◽  
Impulse Model

The global gene expression trajectories of cellular systems in response to developmental or environmental stimuli often follow the prototypic single-pulse or state-transition patterns which can be modeled with the impulse model. Here we combine the continuous impulse expression model with a sequencing data noise model in ImpulseDE2, a differential expression algorithm for time course sequencing experiments such as RNA-seq, ATAC-seq and ChIP-seq. We show that ImpulseDE2 outperforms currently used differential expression algorithms on data sets with sufficiently many sampled time points. ImpulseDE2 is capable of differentiating between transiently and monotonously changing expression trajectories. This classification separates genes which are responsible for the initial and final cell state phenotypes from genes which drive or are driven by the cell state transition and identifies down-regulation of oxidative-phosphorylation as a molecular signature which can drive human embryonic stem cell differentiation.

scholarly journals A relative comparison between Hidden Markov- and Log-Linear-based models for differential expression analysis in a real time course RNA sequencing data

2018 ◽  
Author(s):  
Fatemeh Gholizadeh ◽  
Zahra Salehi ◽  
Ali Mohammad banaei-Moghaddam ◽  
Abbas Rahimi Foroushani ◽  
Kaveh kavousi
Keyword(s):  
Real Time ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Time Course ◽  
Hidden Markov ◽  
Differential Expression Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Normalization Methods ◽  
Log Linear

AbstractWith the advent of the Next Generation Sequencing technologies, RNA-seq has become known as an optimal approach for studying gene expression profiling. Particularly, time course RNA-seq differential expression analysis has been used in many studies to identify candidate genes. However, applying a statistical method to efficiently identify differentially expressed genes (DEGs) in time course studies is challenging due to inherent characteristics of such data including correlation and dependencies over time. Here we aim to relatively compare EBSeq-HMM, a Hidden Markov-based model, with multiDE, a Log-Linear-based model, in a real time course RNA sequencing data. In order to conduct the comparison, common DEGs detected by edgeR, DESeq2 and Voom (referred to as Benchmark DEGs) were utilized as a measure. Each of the two models were compared using different normalization methods. The findings revealed that multiDE identified more Benchmark DEGs and showed a higher agreement with them than EBSeq-HMM. Furthermore, multiDE and EBSeq-HMM displayed their best performance using TMM and Upper-Quartile normalization methods, respectively.

scholarly journals Differential Expression of Epigenetic Modulators During Human Embryonic Stem Cell Differentiation

2008 ◽  
Vol 41 (3) ◽  
pp. 201-207 ◽  
Author(s):  
Sharla M. O. Phipps ◽  
William K. Love ◽  
Troy E. Mott ◽  
Lucy G. Andrews ◽  
Trygve O. Tollefsbol
Keyword(s):  
Stem Cell ◽  
Cell Differentiation ◽  
Embryonic Stem Cell ◽  
Differential Expression ◽  
Human Embryonic Stem Cell ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Embryonic Stem Cell Differentiation

scholarly journals pulseTD: RNA life cycle dynamics analysis based on pulse model of 4sU-seq time course sequencing data

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9371
Author(s):  
Xin Wang ◽  
Siyu He ◽  
Jian Li ◽  
Jun Wang ◽  
Chengyi Wang ◽  
...  
Keyword(s):  
Life Cycle ◽  
Time Course ◽  
Functional Form ◽  
R Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Future Time ◽  
Dynamic Changes ◽  
Robust Identification ◽  
Course Sequencing

The life cycle of intracellular RNA mainly involves transcriptional production, splicing maturation and degradation processes. Their dynamic changes are termed as RNA life cycle dynamics (RLCD). It is still challenging for the accurate and robust identification of RLCD under unknow the functional form of RLCD. By using the pulse model, we developed an R package named pulseTD to identify RLCD by integrating 4sU-seq and RNA-seq data, and it provides flexible functions to capture continuous changes in RCLD rates. More importantly, it also can predict the trend of RNA transcription and expression changes in future time points. The pulseTD shows better accuracy and robustness than some other methods, and it is available on the GitHub repository (https://github.com/bioWzz/pulseTD_0.2.0).

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