update storm paper

_papers/spateo.md

@@ -3,6 +3,7 @@ layout: paper
 title: "Read our work"
 type: selected work
 paper_title: "Spatiotemporal modeling of molecular holograms"
+paper_subtitle: -published in Cell and also highlighted in [*Nature*](https://www.nature.com/articles/d41586-024-03615-8) or [*Nature Methods*](https://www.nature.com/articles/s41592-024-02587-x.pdf)
 author_list: Xiaojie Qiu*+, Daniel Y Zhu*, Yifan Lu*, Jiajun Yao*, Zehua Jing*, Kyung Hoi Min*, Mengnan Cheng*, Hailin Pan, Lulu Zuo, Samuel King, Qi Fang, Huiwen Zheng, Mingyue Wang, Shuai Wang, Qingquan Zhang, Sichao Yu, Sha Liao, Chao Liu, Xinchao Wu, Yiwei Lai, Shijie Hao, Zhewei Zhang, Liang Wu, Yong Zhang, Mei Li, Zhencheng Tu, Jinpei Lin, Zhuoxuan Yang, Yuxiang Li, Ying Gu, David Ellison, Yuancheng Ryan Lu, Qinan Hu, Yuhui Hu, Ao Chen, Longqi Liu, Jonathan S Weissman, Jiayi Ma+, Xun Xu+, Shiping Liu+, Yinqi Bai+
 journal: Cell
 doi: https://doi.org/10.1016/j.cell.2024.10.011

_papers/storm.md

@@ -1,28 +1,16 @@
 ---
 layout: paper
 title: "Read our work"
-type: preprints
-paper_title: "Storm: Incorporating transient dynamics to infer the RNA velocity with metabolic labeling information"
+type: previous involved work
+paper_title: "Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information"
 author_list: Qiangwei Peng, Xiaojie Qiu+, Tiejun Li+
-journal: Biorxiv
-doi: 10.1101/2023.06.21.545990
-year: 2023
-pdf_url: /assets/PDFs/qiangwei_storm_2023.pdf
+journal: PLOS computational biology
+doi: doi.org/10.1371/journal.pcbi.1012606
+year: 2024
+pdf_url: /assets/PDFs/qiangwei_storm_2024.pdf
 image_url: /assets/images/papers/storm.png
 paper_alt: storm Paper Image
-rank: 1
+rank: 0
 ---
 
-The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic 
-parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and 
-RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics 
-and steady-state assumption on gene expression states are insufficient to achieve favorable results for the 
-data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), 
-to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation 
-reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly 
-considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and 
-extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. 
-Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific 
-transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state 
-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, 
-eventually advancing our understanding of the dynamic transcription regulation during development and disease.
+The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

papers.html

@@ -35,7 +35,7 @@ <h4>Selected work</h4>
                             {% endif %}
                             <p class="note" style="text-align: justify;"> {{ paper.author_list }} {{ paper.year }}</p>
                             <p class="note"><i>{{ paper.journal }}</i></p>
-                            {% if paper.rank == 1 %}
+                            {% if paper.rank == 0 %}
                                 <p class="note"><sup>*</sup>Equal Contribution, <sup>+</sup>Corresponding Author</p>
                             {% endif %}
                         </div>