This center brings together standardized documentation covering the entire process from experimental preparation and sequencing to data analysis, aiming to provide clear and accurate operational guidance and theoretical support to help your research progress efficiently and smoothly.
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The results show that SeekArc platform data has high consistency with 10x platform data, effectively supporting the in-depth development of cross-platform single-cell transcriptome research.
SeekSoul™ Online is a one-stop single-cell multi-omics data mining and visualization online tool developed by SeekBio over two years covering standard and advanced analysis workflows for single-cell transcriptomics single-cell immunology and SeekBio's in-house single-cell spatial transcriptomics products. The analysis results are highly consistent with those from code execution providing users with comprehensive deep data analysis solutions across multiple fields including disease mechanism exploration biological target mining and basic scientific research.
This article will briefly outline common approaches and methods for single-cell annotation of special species, helping you quickly grasp the relevant content.
Comprehensive guide covering common questions and recommendations for single-cell sample preparation transportation quality control library construction and downstream analysis.
Introduces the sources impacts and various handling methods (filtering gene removal and re-clustering etc.) of hemoglobin gene contamination and provides R code examples.
Provides background removal (SoupX) doublet filtering and parameter optimization workflows for FFPE sample background RNA doublets and quality control issues.
Locates the reasons for GFP not being quantified in featureCounts (missing gene/transcript lines in GTF annotations) and provides example commands to fix GTF.
Explains the compatibility issues of samtools with oversized chromosome indices and provides splitting scripts and usage instructions for subsequent alignment and indexing.
Introduces the workflow and considerations for converting DD platform data to 10x format and using Cell Ranger to analyze Antibody/CRISPR data.
Coverage of converting DD ATAC/GE data to 10x ARC supported format fastp quality control barcode conversion and Cell Ranger ARC running examples.
Usage instructions for barcode conversion tool example commands and parameter explanations applicable to converting DD barcodes to 10x barcodes.
Integrating scRNA-seq and scATAC-seq multimodal data using Seurat and Signac
Ref preparation workflow for SeekSoul Tools v1.3.0 and TRUST4 including species sequence extraction from IMGT and formatting script examples
Explanation of the principles result interpretation and common issues regarding mixed-species data analysis
Assessment of the impact of diffuse active immunoglobulin (IG) gene expression related to plasma cells in the sample along with recommendations for downstream analysis
The purpose, standards, and meanings of key quality control indicators for dual-omics data of scRNA-seq and scATAC-seq
Strategies for dimensionality reduction and clustering of dual-omics data of scRNA-seq and scATAC-seq, including dimensionality reduction and clustering based on RNA data, dimensionality reduction and clustering based on ATAC data, and joint dimensionality reduction and clustering based on both RNA and ATAC
Methods and strategies for cell annotation of dual-omics data of scRNA-seq and scATAC-seq, by simultaneously considering two information sources of gene expression (RNA) and chromatin accessibility (ATAC) to improve the accuracy of cell annotation
Based on scATAC-seq data, differential accessibility analysis is used to identify specific open regions in each cell population/state, followed by motif enrichment analysis to identify core regulatory transcription factors. Gene annotation and functional enrichment are performed on related peaks to reveal potential cell-specific regulatory mechanisms.
Motif analysis is a key step in interpreting transcription factor regulatory networks from single-cell ATAC+RNA multi-omics data. By identifying transcription factor binding sites (motifs) enriched in chromatin open regions (peaks), it can be inferred which transcription factors may be involved in regulating specific cell types or states.
In single-cell ATAC-seq data analysis, gene activity analysis is an important method for quantifying gene activity by assessing chromatin accessibility. The analysis tallies ATAC-seq signal intensity within each gene region (including the gene body and upstream 2 kb regulatory region), which reflects the openness of DNA and thus indicates the potential transcriptional activity of genes.
Peak2Gene (Peak-Gene linking) analysis is a method designed for single-cell multi-omics (ATAC+RNA) data. Its core objective is to identify significant regulatory relationships between gene expression and nearby chromatin accessibility peaks (peaks). The method calculates the correlation between gene expression levels and ATAC signal intensity of nearby peaks in each cell, and uses a generalized linear model to correct for technical biases such as GC content, peak length, and distance, thereby inferring which peaks may regulate which genes.
Pseudotime analysis is used to reconstruct the trajectory of cell development or differentiation, revealing dynamic changes in chromatin accessibility. Monocle3 selects a root cell, calculates the distance of each cell from the starting point, and obtains pseudotime, which is used to order the differentiation process of cells. In scATAC-seq analysis, pseudotime can intuitively reflect changes in chromatin accessibility and the dynamic process of gene regulatory networks.
epiAneuFinder is an algorithm used to detect copy number variations (CNVs) from single-cell ATAC (scATAC) data. Single-cell multi-omics data includes scATAC-seq information, and epiAneuFinder can be used to perform cnv analysis on the scATAC-seq in multi-omics, revealing tumor cell heterogeneity.
CopyscAT can infer copy number variations (CNV) based on scATAC-seq data, assisting in the identification of cancer cells. It helps study the relationship between chromosomal changes and epigenomic states within different subclones of complex tumors, such as glioblastoma, and analyzes how genetic variations affect the molecular phenotype of cells. CopyscAT is particularly suitable for exploring the interaction between genetics and epigenetics in highly heterogeneous tumors, as well as the mechanisms of action between tumor cells and the microenvironment.
AtaCNV is a copy number variation (CNV) detection tool developed specifically for single-cell ATAC-seq (scATAC-seq) data. By processing single-cell chromatin accessibility sequencing data, AtaCNV can reveal the genetic heterogeneity within complex tissues like tumor cells with high resolution. It is applicable to the scATAC-seq channel in multi-omics single-cell data, enabling automatic inference and visualization of cell copy number states in complex samples such as tumors.
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