scDrugPrio分析克罗恩病单细胞RNA测序数据的元数据

数据集获取（Figshare）

• 源站标题：Metadata for scDrugPrio analysis of pooled Crohn’'s disease scRNA-sequencing data
• DOI：10.25452/figshare.plus.22343980.v1
• 发布日期：2024-02-29
• 文件数：3 个
• 文件总体积：11.80 GB
• 源站页面：https://plus.figshare.com/articles/dataset/Metadata_for_scDrugPrio_analysis_of_pooled_Crohn_s_disease_scRNA-sequencing_data/22343980

命令行下载

curl -L -o "latent.tsv" "https://ndownloader.figshare.com/files/39755425"
curl -L -o "Cell_identity.txt" "https://ndownloader.figshare.com/files/39755428"
curl -L -o "mean.tsv" "https://ndownloader.figshare.com/files/39755464"

文件清单

• latent.tsv（28.79 MB）
• Cell_identity.txt（3.58 MB）
• mean.tsv（11.76 GB）

文件总体积 11.80 GB（源站 API 实测）。

数据说明

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases
Ineffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. In our recent work, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs.
scDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohn’s disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohn’s disease patients. The analysis showed great variations in drug predictions between patients, for example,assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. Application to individual patients indicates scDrugPrio’s potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/s

许可

CC BY-SA 4.0

引用

学术使用请引用 DOI 10.25452/figshare.plus.22343980.v1（source: https://plus.figshare.com/articles/dataset/Metadata_for_scDrugPrio_analysis_of_pooled_Crohn_s_disease_scRNA-sequencing_data/22343980）。