数据收集-分化轨迹推断

数据收集-分化轨迹推断

1

参考

Ranek, J.S., Stanley, N. & Purvis, J.E. Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction. Genome Biol 23, 186 (2022). https://doi.org/10.1186/s13059-022-02749-0

内容

The raw publicly available single-cell RNA sequencing datasets downloaded and used in this study are available in the

Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) repository, under the accession codes GSE81682

for hematopoiesis diferentiation (Nestorowa) [86]; GSE74596 for NKT cell diferentiation [87]; GSE70236, GSE70240,

and GSE70244 for hematopoiesis diferentiation (Olsson) [88]; GSE94383 for LPS stimulation [89]; GSE161465 for INFγ
stimulation
[90]; GSE116481 for AML chemotherapy [91]; GSE126068 for AML diagnosis/relapse [92]; and GSE138266 for MS case/control PBMC and CSF datasets [93] and in the European Nucleotide Archive (ENA; https://www.ebi.ac.uk/arrayexpress/experiments/) repository, under accession numbers E-MTAB-2805 for mouse embryonic cell cycle [94] datasets, respectively. Loom fles and preprocessed data are available in the Zenodo repository https://doi.org/10.5281/zenodo.6587903 [95]. Source code including all functions for preprocessing, integration, and evaluation are publicly available at www.github.com/jranek/EVI [96] and in the Zenodo repository [97].

2

参考

Saelens, W., Cannoodt, R., Todorov, H. et al. A comparison of single-cell trajectory inference methods. Nat Biotechnol 37, 547--554 (2019). https://doi.org/10.1038/s41587-019-0071-9

内容

The scripts to download and process these datasets are available on our repository (https://benchmark.dynverse.org/tree/master/scripts/01-datasets).

3

参考

Wolf, F. & Hamey, Fiona & Plass, Mireya & Solana, Jordi & Dahlin, Joakim & Rajewsky, Nikolaus & Simon, Lukas & Theis, Fabian. (2019). PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome biology. 20. 10.1186/s13059-019-1663-x.

内容


4

参考

Zeng, Y., He, J., Bai, Z. et al. Tracing the first hematopoietic stem cell generation in human embryo by single-cell RNA sequencing. Cell Res 29, 881--894 (2019). https://doi.org/10.1038/s41422-019-0228-6

内容

The scRNA-seq data reported in this study have been deposited in NCBI's Gene Expression Omnibus (GEO) with the accession number GSE135202. All other relevant data in this study are available from the corresponding authors upon reasonable request.

5

参考

Junjie Du, Han He, Zongcheng Li, Jian He, Zhijie Bai, Bing Liu, Yu Lan,

Integrative transcriptomic analysis of developing hematopoietic stem cells in human and mouse at single-cell resolution,

Biochemical and Biophysical Research Communications,Volume 558,2021,Pages 161-167,ISSN 0006-291X,

内容

2.1.1. Mouse

Zhou et al. performed single-cell transcriptome sequencing on cells during the development of HSCs [3]. Expression matrix of cell populations (type 1 and 2 pre-HSCs, E12/E14 HSCs and adult HSCs, abbreviated as mT1 and mT2 pre-HSCs, mE12/mE14 HSCs and mAdult HSC respectively) was downloaded from GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE67120).

Hou et al. constructed high-precision single-cell transcriptomics to unbiasedly examine the endothelial cells populations at continuous developmental stages with intervals of 0.5 days from embryonic day (E) 9.5 to E11.0 [4]. Expression matrix of cell populations (HECs, type 1 pre-HSCs, abbreviated as mHECs and mT1 pre-HSCs respectively) was downloaded from: https://github.com/Liu-Lan-lab/Project_mHEC_CR2020.

2.1.2. Human

Zeng et al. analyzed the cell populations of the dorsal aorta during the temporal window of human embryonic HSC generation using single-cell transcriptome sequencing [6]. Expression matrix of cell populations (HECs, three subsets of HSPCs, abbreviated as hHECs and hHSPC_GJA5+/Cycling/GFI1B+, respectively) was downloaded from: https://github.com/Liu-Lan-lab/Project_hHEC_HSPC_CR2019.

Bian et al. applied scRNA-seq on CD45+ hematopoietic cell populations from a range of tissues in human embryos, and transcriptomically identified HSPCs with high expression of such as CD34, MYB, and HOX family transcription factors HOXA6 and HOXA10 in liver at CS20 and CS23 [9]. Expression matrix of HSPCs in human fetal liver (abbreviated as hHSPC_FL) was downloaded from: https://github.com/Liu-Lan-lab/human_macrophage_project_data

6:methods and datasets review

参考

Shen, Sophie et al. "Integrating single-cell genomics pipelines to discover mechanisms of stem cell differentiation." Trends in molecular medicine vol. 27,12 (2021): 1135-1158. doi:10.1016/j.molmed.2021.09.006

内容

相关推荐
AI完全体25 分钟前
【AI日记】24.11.22 学习谷歌数据分析初级课程-第2/3课
学习·数据分析
请你喝好果汁6413 小时前
单细胞|M3-4. 细胞聚类与轨迹推断
机器学习·数据挖掘·聚类
吾门3 小时前
YOLO入门教程(三)——训练自己YOLO11实例分割模型并预测【含教程源码+一键分类数据集 + 故障排查】
yolo·分类·数据挖掘
电子手信4 小时前
知识中台在多语言客户中的应用
大数据·人工智能·自然语言处理·数据挖掘·知识图谱
databook5 小时前
『玩转Streamlit』--布局与容器组件
python·机器学习·数据分析
shansjqun5 小时前
教学内容全覆盖:航拍杂草检测与分类
人工智能·分类·数据挖掘
SelectDB技术团队5 小时前
兼顾高性能与低成本,浅析 Apache Doris 异步物化视图原理及典型场景
大数据·数据库·数据仓库·数据分析·doris
panpantt3216 小时前
【参会邀请】第二届大数据与数据挖掘国际会议(BDDM 2024)邀您相聚江城!
大数据·人工智能·数据挖掘
statistican_ABin7 小时前
R语言数据分析案例45-全国汽车销售数据分析(可视化与回归分析)
数据挖掘·数据分析
CV学术叫叫兽8 小时前
快速图像识别:落叶植物叶片分类
人工智能·分类·数据挖掘