Understanding the intracellular spatial distribution of RNA for a particular cell type thus not only improves the characterization of cell identity but also is of paramount importance in elucidating unique subcellular regulatory mechanisms specific to the cell type. However, current cell type annotation ...
Cell type annotation is a fundamental task for cell and tissue biology17that can help characterize the biological process of tissues at single-cell level. This task is conventionally performed by single-cell transcriptome analysis on the data acquired by scRNA-seq technology. Facing the exponential g...
Cell type annotation Our cell type annotation is based on the imputed gene activity of known liver cell marker genes from CellAtlas75. To calculate the imputed gene activities, fragments mapping to gene bodies or promoter regions of genes (up to 2 kb upstream of a gene) were summed up us...
1c). We also used specific cell-type gene markers known from the literature to perform manual annotation, and the annotation result was consistent with the result obtained from MIA (Additional file 1: Fig. S1e). We mapped transcriptomic signatures of scRNA-seq clusters directly upon the 18 ...
1): (1) cell type annotation, (2) calculation of SPatially Adjacent Cell type Embedding (SPACE), (3) Jensen–Shannon divergence (JSD)-based hierarchical clustering, and finally (4) multi-slice spatial domain analysis. Fig. 1 Workflow of SpaDo. a Calculating SPACE for both single-cell and...
Numerous bioinformatics tools employed for scRNA-seq analysis can be readily adapted for spatial analysis, encompassing deconvolution, clustering, cell type annotation [195, 196], and other essential processing steps [197]. However, it ignores spatial location and structural characteristics; therefore, ...
(9) metadata_celltype:column name for single-cell annotation data in metadata (default = 'celltype') Potential errors when installing new conda environment by R wrap function (env.select='conda') GLIBCXX_3.4.26 should be already installed ...
To address this, we develop TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. ...
Alt2 - a.h5adfile with cells as rows and genes as columns. Cell type annotations can be read from this file as well, and should then be put in the.obsslot. See Alt2 below for more information. Single Cell Annotation Data Alt1 - a.tsvfile with the same rownames as the count data...
(anchorset = anchors, refdata = ref$celltype, prediction.assay = TRUE, weight.reduction = slide.seq[["pca"]], dims = 1:50) # 添加预测信息 slide.seq[["predictions"]] <- predictions.assay DefaultAssay(slide.seq) <- "predictions" # 数据可视化 SpatialFeaturePlot(slide.seq, features = c...