Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therape
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq...
Bulk and single cell measurements are integrated to identify phenotype-associated subpopulations of cells. Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current
GeneexpressionTissue-specificdeconvolutionofimmunecellcompositionbyintegratingbulkandsingle-celltranscriptomesZiyiChen1,2,ChengyangJi1,2,QinShen1,2,WeiLiu1,2,F.Xiao-FengQin1,2andAipingWu1,2,*1CenterforSystemsMedicine,InstituteofBasicMedicalSciences,ChineseAcademyofMedicalSciencesandPekingUnionMedicalCollege,Beiji...
Addressing this knowledge gap is essential, as understanding the function of TSPs in colon cancer could identify new therapeutic targets and prognostic markers. Methods Analyzing 1981 samples from 10 high-throughput datasets, including six bulk RNA-seq, three scRNA-seq, and one spatial transcriptome ...
Single-cell sequencing and bulk RNA sequencing reveal a cell differentiation-related multigene panel to predict the prognosis and immunotherapy response of hepatocellular carcinoma. Chin Med J. 2023;136:485–7. Article PubMed PubMed Central Google Scholar Dennis G Jr, Sherman BT, Hosack DA, Yang...
Triosephosphate isomerase 1 may be a risk predictor in laryngeal squamous cell carcinoma: a multi-centered study integrating bulk RNA, single-cell RNA, and protein immunohistochemistryJian-Di Li, Yi Chen, Shu-Wen Jing, Li-Ting Wang, Yu-Hong Zhou, Zhi-Su Liu, Chang Song, Da-Zhi ...
(i.e., bulk RNAseq data and single-cell RNAseq data). However, existing supervised methods mainly project the distribution of expression of two genes onto a 2D image, where the non-European data is forcibly mapped to the grid image [3], [4], [6]. The pseudo image is inputted to a...
Next, we follow RCTD’s strategy to accurately approximate γg by viewing ST data as a bulk RNA-seq data using the convenient property of Poisson distribution14. Other parameters, including αi, can be obtained accordingly (Supplementary Methods). Then we iteratively find MAP estimate of {ki,...
Live cell imaging allows direct observation and monitoring of phenotypes that are difficult to infer from transcriptomics. However, existing methods for linking microscopy and single-cell RNA-seq (scRNA-seq) have limited scalability. Here, we describe an upgraded version of Single Cell Optical ...