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Single-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq.
Inference of cell–cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a...
Single-cell sequencing provides detailed insights into biological processes including cell differentiation and identity. While providing deep cell-specific information, the method suffers from technical constraints, most notably a limited number of expressed genes per cell, which leads to suboptimal clustering...
Despite the advances in antibody-guided cell typing and mass spectrometry-based proteomics, their integration is hindered by challenges for processing rare cells in the heterogeneous tissue context. Here, we introduce Spatial and Cell-type Proteomics (SC
Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell–cell communication inference....
C Thirteen clusters with 11 cell types identified on the UMAP plot. D The dot plot represents the expression level of the representative marker genes (dot color shades) and the percentage of cells expressing marker genes (dot size). E The heatmap illustrates the number of whole lung cells ...
Confidence intervals (color shades) indicate one standard deviation Full size image To further validate the existence of activated TH17 cells in COVID-19 patient blood, we next analyzed the corresponding lung data (covid-lung) of the patients for shared T cell receptor clones (Additional file 1...
In this work, we begin by highlighting the impact of intra-tumor transcriptomic heterogeneity on clinical outcomes based on large-scale re-analysis of TCGA data [24,25]. We then develop a machine learning framework (Cancer Drug Response prediction using a Recommender System for single-cell RNA-...