| Name | topicvelo JSON |
| Version |
0.0.2
JSON |
| download |
| home_page | |
| Summary | TopicVelo: Dissection and Integration of Bursty Transcriptional Dynamics for Complex Systems |
| upload_time | 2023-12-12 22:54:16 |
| maintainer | |
| docs_url | None |
| author | Frank Gao |
| requires_python | >=3.7 |
| license | BSD 3-Clause License Copyright (c) 2023, chengfgao Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
rna
single cell
stochastic
topic modeling
transcriptional bursting
transcriptomics
velocity
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
TopicVelo is a novel approach for RNA velocity inference in general systems, including immune
response studies. It infers the cells and genes associated with distinct active processes
via probabilistic topic modeling, and uses these to estimate process-specific velocity
parameters and transition probabilities, which are then integrated into large-scale transition
matrices. Parameter accuracy is also improved by efficiently fitting unsmoothed counts to a
transcriptional burst model. In biologically varied datasets, this approach outperformed the
state-of-the-art method, recovering parameters and transitions that were better experimentally
supported or recovered previously only with the aid of metabolic labeling or multiple time points.
For more information please see our preprint
(https://www.biorxiv.org/content/10.1101/2023.06.13.544828v1.full)
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