Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge—gathered from decades of detailed biochemical studies—allows us to obtain functional insights, focusing on gene regulatory processes and cell–cell interactions. We present diverse applications in ...
In the context of spatial transcriptomics integration methods, the maximum a posteriori estimate most often refers to the cell-type distribution given the gene distribution at a capture spot. Enrichment Refers to a particular class of genes that is over-represented in a large set of genes. In ...
2420*? (scATAC-seq) Provide a framework for integration of different types of data; the output of this method is an integrated expression matrix that could be used in any single-cell GRN inference method. 1. Single-cell sequencing for GRN reconstruction Different from bulk sequencing that ...
In summary, our study constructed and validated a nine-gene signature associated with NAC prognosis, which was accomplished through the integration of single-cell and bulk RNA data. The results of our study are of crucial significance in the prediction of the efficacy of NAC in BC, and may ...
To overcome this problem, we have proposed Single Cell Integration by Disentangled Representation Learning (SCIDRL), a domain adaption-based method, to learn low-dimensional representations invariant to batch effect. This method can efficiently remove batch effect while retaining cell type purity. We ...
其实挺好奇为什么旧事重提的,因为在笔者看来这个问题很早就出现了,但因为之前 HCA 以及各个计算的工具主要集中在integration/removing batch effect,所以好像不是那么明显,但是比如 Cell Blast 就是想做这件事,不是很懂为什么 Satijia 他们完全漏过了。。
注意老铁说的“Seurat’s integration method is quite heavy handed in my experience,so if you decide to go the integration route,I’d recommend using the SeuratWrapper around the fastMNN”(单细胞分析Seurat使用相关的10个问题答疑精选!) QC # quality control kid[["percent.mt"]] <- PercentageFeatu...
Single-cell multi-omics data integration methods have had great success in fusing different molecular data types, or modalities for disease subtyping, predicting biomarkers, or uncovering cross-modality correlations [23, 24]. Here, integration methods aim to merge individual layers of single-cell data...
Run(adatas=[adata1, adata2], adata_cm=adata_cm) # save global optimal transport matrix: adata, OT = up.Run(adatas=[adata1, adata2], adata_cm=adata_cm, save_OT=True) # integration with only common genes: adata = up.Run(adata_cm=adata_cm) Citation @Article{Cao2022, author={Cao...
Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analy...