Tag: Time Series Analysis

Monocle 3 0.2.0 – Differential Expression and Time-series analysis for Single-cell RNA-Seq and qPCR Experiments

Monocle 3 0.2.0

:: DESCRIPTION

Monocle is a toolkit for analyzing single-cell gene expression experiments. It was designed for RNA-Seq, but can also work with single cell qPCR. It performs differential expression analysis, and can find genes that differ between cell types or between cell states. When used to study an ongoing biological process such as cell differentiation, Monocle learns that process and places cells in order according to their progress through it. Monocle finds genes that are dynamically regulated during that process.

::DEVELOPER

Trapnell Lab at the University of Washington.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux
  • R

:: DOWNLOAD

 Monocle

:: MORE INFORMATION

Citation

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.
Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL.
Nat Biotechnol. 2014 Apr;32(4):381-6. doi: 10.1038/nbt.2859.

TRANS-MNET 20070315 – Time Series Analysis of Gene Expression Profiles

TRANS-MNET 20070315

:: DESCRIPTION

TRANS-MNET (transcriptional module Network) performs State Space Model to time-course microarray data. State Space Model is a statistical model for analyzing time-series data and state space model implemented in TRANS-MNET is optimized for microarray data. The typical microarray time-course data is high dimensional, but has few time-points, TRANS-MNET can use replicated time-courses includes biological and technical replicates. Also, parameter constraint imposed in TRANS-MNET yields the first-order autoregressive representation of state space models that can be viewed as a parsimonius parameterization of vector AR(1). The permutation test can be applied for finding significance of its AR coefficient and this achives gene regulatory networks.

::DEVELOPER

TRANS-MNET Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 TRANS-MNET

:: MORE INFORMATION

Citation

R.Yoshida, T.Higuchi S.Imoto,
Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov swotching,
Proc.4th Computational Systems Bioinformatics (CSB2005: Refereed Conference), 289-298, 2005.

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