et al. Cell-type deconvolution analysis identifies cancer-associated myofibroblast component as a poor prognostic factor in multiple cancer types. Oncogene 40, 4686–4694 (2021). https://doi.org/10.1038/s41388-021-01870-x Download citation Received07 October 2020 Revised10 May 2021 Accepted26 May...
aSmoothed scatterplots of the expected quantile of the absolutez-statistic in the meta-analysis, as inferred from a Monte-Carlo permutation analysis (x-axis) vs. the observed quantile (y-axis). CpGs that passed an FDR < 0.05 thresholds are shown in red.bHeatmaps of CellDMCt-statistics...
Schematic view of the STdGCN framework. STdGCN is a cell-type deconvolution method designed for spatial transcriptomics data, using scRNA-seq data as the reference. The workflow of STdGCN involves several key steps. Firstly, STdGCN employs the scRNA-seq reference data to identify cell-type marke...
However, these bulk tissue studies obscure cell type-specific information. Although Single-cell/nuclei (sc/sn) RNA-Seq studies in SCZ exist, they lack the power to characterize minor cell types. Cell deconvolution has emerged as a method to overcome these limitations and leverage large sample ...
Cell-type deconvolution of ST-seq cell clusters Considering the potential overlap of multiple cell types in each spot, we sought to apply Spotlight analysis to define the contribution of each scRNA-seq cluster to the expression profile of each ST spot. After Seurat deconvolution, we obtained the...
Section 5: Rare Component Analysis In the cell type deconvolution problem, cell types with extremely small proportions are often ignored. However, these cell types can play vital roles in certain situations, such as tumor-infiltrating lymphocytes (TILs), which exhibit low fractions in many cancer ...
To sum up, SpatialDecon integrates the latest advances in algorithms and public data resources in order to improve the accuracy and robustness of deconvolution analysis of spatial omics data. With this method, researchers can obtain information about cell type and abundance without relying on scRNA-...
cell type-specific features using cross-cell type differential analysis. These features are then used for the RF deconvolution in step (a) in a new iteration. By iterating steps (a) and (b), the updated feature list can better capture the cell type distinction and improve RF deconvolution ...
Single cell RNA-seq data of ESCC with 41,237 cells from PRJNA777911 [29] were selected for the analysis of cell–cell communication and to derive the deconvolution marker gene reference data source. Specifically, 19,882 ESCC primary tumor and 21,355 matched adjacent nonmalignant esophageal cells...
xCell produces enrichment scores, not percentages. It is not a deconvolution method, but an enrichment method. That means that the main usage is for comparing across samples, not across cell types. xCell does an attempt to make the scores resemble percentages, but it is a hard problem, and ...