SeekGene Bio Documentation Center

Author: Liu Xin

Time: 104 min

Words: 20.8k words

Updated: 2026-04-13

Reads: 0 times

document_library

Overview

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.

Document Browsing

Use the filters on the left and the search box on the right to quickly locate documents; browse the list with pagination, and click a title to open the corresponding guide.

Filter

Products

• SeekSoul™ Online
• scATAC + RNA-seq
• Spatial-seq
• 3' scRNA-seq
• 5' + Immune Profiling
• scFAST-seq
• FFPE scRNA-seq
• scMethyl + RNA-seq

Topic

• Analysis Guide
• Notebooks
• Method
• FAQ

Analysis Type

• Data Integration
• Batch Effect
• Basic Analysis
• Cell Annotation
• Peak-Gene Links
• SNP Enrichment
• Differential Enrichment
• Co-localization
• Plotting
• Activity
• Copy Number Variation
• Pseudotime
• Niche
• Mutation
• Cell Communication
• Correlation

List

SeekSoul™ Online platform overview and entry

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.

Tag：SeekSoul™ Online

2025/8/28

DD ATAC + RNA multi-omics to 10x and Cell Ranger ARC analysis guide

Coverage of converting DD ATAC/GE data to 10x ARC supported format fastp quality control barcode conversion and Cell Ranger ARC running examples.

Tag：scATAC + RNA-seq , Analysis Guide , FAQ

2025/8/7

ATAC + RNA Multi-omics Quality Control Report Documentation

Systematically explains the definitions, calculation methods, and interpretation guidelines for various metrics in the single-cell multi-omics quality control report (scATAC + scRNA) generated by SeekArc Tools software.

Tag：scATAC + RNA-seq , Analysis Guide

2026-03-23

ATAC + RNA multi-omics QC guide and key metrics

The purpose, standards, and meanings of key quality control indicators for dual-omics data of scRNA-seq and scATAC-seq

Tag：scATAC + RNA-seq , Analysis Guide , Data Integration

2025/12/23

ATAC + RNA multi-omics dimensionality reduction and clustering (RNA ATAC WNN)

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

Tag：scATAC + RNA-seq , Analysis Guide , Data Integration

2025/12/23

ATAC + RNA multi-omics cell annotation (RNA expression plus ATAC accessibility)

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

Tag：scATAC + RNA-seq , Analysis Guide , Data Integration

2025/12/23

ATAC + RNA multi-omics differential accessibility peaks with motif and functional enrichment

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.

Tag：scATAC + RNA-seq , Analysis Guide , Data Integration

2025/12/23

ATAC + RNA multi-omics motif enrichment and TF regulation inference

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.

Tag：scATAC + RNA-seq , Analysis Guide , Data Integration

2025/12/23

ATAC + RNA multi-omics gene activity calculation and applications

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

ATAC + RNA multi-omics Peak2Gene peak-gene linking analysis

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

ATAC + RNA multi-omics Monocle3 pseudotime and chromatin dynamics

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

ATAC + RNA multi-omics epiAneuFinder CNV detection from scATAC

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

ATAC + RNA multi-omics CopyscAT CNV inference and tumor cell identification

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

ATAC + RNA multi-omics AtaCNV CNV detection and visualization

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.

Tag：scATAC + RNA-seq , Analysis Guide

2025/12/23

scATAC-seq Differential Accessibility and Functional Enrichment Analysis Tutorial

ATAC_DiffEnrich is a workflow for differential accessibility and enrichment analysis of scATAC-seq in single-cell multi-omics data.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Differential Enrichment⬇ Export ipynb

2026-01-29

scATAC-seq Pseudotime Analysis: Chromatin Dynamic Trajectory Resolution

The ATAC_Monocle3 analysis pipeline is designed for trajectory inference and developmental dynamics research of single-cell ATAC-seq data.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Pseudotime⬇ Export ipynb

2026-01-29

scATAC-seq Advanced Analysis: Peak-Gene Regulatory Association Analysis

ATAC_Peak2Gene is a Peak-to-Gene association analysis pipeline for integrating scATAC-seq and scRNA-seq in single-cell multi-omics data.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Peak-Gene Links⬇ Export ipynb

2026-01-29

scATAC-seq Variation Analysis: SNP Enrichment in Chromatin Open Regions

Genome-Wide Association Studies (GWAS) are key tools for resolving the genetic basis of complex traits.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , SNP Enrichment⬇ Export ipynb

2026-01-29

ataCNV Analysis Tutorial: Tumor Copy Number Variation Resolution Based on scATAC-seq

Chromosomal Copy Number Variation (CNV) is a hallmark of tumor genomic instability, characterized by large-scale amplification or deletion of chromosomal segments.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Copy Number Variation⬇ Export ipynb

2026-01-29

CopyscAT Analysis: scATAC-seq-based CNV Inference and Tumor Identification

Chromosomal Copy Number Variation (CNV) is a typical hallmark of tumor genomic instability, characterized by large-scale amplification or deletion of chromosomal segments.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Copy Number Variation⬇ Export ipynb

2026-01-29

epiAneufinder Analysis: Aneuploidy Detection Based on scATAC-seq

Chromosomal Copy Number Variation (CNV) is a hallmark of tumor genome instability, characterized by large-scale amplification or deletion of chromosomal segments.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Copy Number Variation⬇ Export ipynb

2026-01-29

scATAC-seq Gene Activity Analysis: Chromatin Accessibility and Transcriptional Potential Assessment

Gene Activity Analysis is a core technique in single-cell ATAC-seq data analysis.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Activity⬇ Export ipynb

2026-01-29

scATAC-seq Multi-Sample Integration Tutorial (Signac / LSI)

Please select the common_r environment for this integration tutorial

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

scRNA-seq + scATAC-seq Multimodal Integration Tutorial (WNN / Label Transfer)

This tutorial demonstrates how to integrate scRNA-seq and scATAC-seq data, covering the complete workflow from data loading, quality control, dimensionality reduction, and clustering to Weighted Nearest Neighbor (WNN) analysis and Label Transfer, enabling deep analysis of multimodal data.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekArc Single-cell Multi-omics (RNA+ATAC) Multi-sample Integration Tutorial

This tutorial focuses on SeekArc multi-omics data (RNA+ATAC), presenting the multi-sample integration workflow, including data loading, QC, batch effect removal using CCA/Harmony, WNN-based multimodal integration, and dimensionality reduction clustering.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekArc Single-Cell Multi-Omics (RNA+ATAC) Single Sample Analysis Tutorial (WNN)

Based on the Seurat framework, this tutorial introduces the standard analysis workflow for SeekArc single-sample multi-omics data, including joint analysis of RNA and ATAC data, Weighted Nearest Neighbor (WNN) graph construction, and cell type annotation.

Tag：scATAC + RNA-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

Single-Cell Spatial Transcriptomics Quality Control Report Documentation

This document details the definitions, calculation methods, and interpretation guidelines for various metrics and charts in the spatial transcriptomics quality control report generated by the SeekSpace Tools software. It aims to help users understand the sequencing data and library quality, and provide a reference for subsequent data analysis.

Tag：Spatial-seq , Analysis Guide

2026-04-01

Single-Cell Spatial Transcriptomics: H&E and DAPI Image Registration Guide

A standardized operating guide using GIMP to spatially register H&E brightfield images with DAPI fluorescent images, covering DAPI preprocessing, layer stacking and opacity settings, move/scale/rotate registration, canvas and background fixing, layer merging and export, and an optional tissue extraction step.

Tag：Spatial-seq , Analysis Guide

2026-03-30

Single-Cell Spatial Transcriptomics: Spatial Visualization Guide

This tutorial is specifically designed for handling the underlying spatial geometric transformations of SeekGene single-cell spatial transcriptomics products, aiming to help you deeply understand and achieve precise alignment between chip coordinates and tissue images.

Tag：Spatial-seq , Analysis Guide , Notebooks⬇ Export ipynb

2026-04-10

Single-cell Spatial Transcriptomics: Spatial Clustering Analysis

This module is based on the Banksy algorithm and is designed for spatial clustering and domain segmentation of spatial transcriptomics data.

Tag：Spatial-seq , Analysis Guide , Notebooks , Niche⬇ Export ipynb

2026-04-13

CellCharter Spatial Transcriptomics Analysis: Spatial Domain Identification and Cellular Microenvironment Resolution

CellCharter identifies spatial domains and resolves microenvironments by constructing spatial networks for neighborhood enrichment analysis supporting differential analysis across conditions to reveal complex cellular spatial interactions.

Tag：Spatial-seq , Analysis Guide , Notebooks , Cell Communication⬇ Export ipynb

2026-01-29

COMMOT Spatial Cell Communication: Signal Inference Based on Optimal Transport Algorithm

COMMOT utilizes the optimal transport algorithm with spatial distance constraints and multi-species distribution transport to achieve high-precision spatial cell communication signal inference, revealing complex ligand-receptor interactions in tissues.

Tag：Spatial-seq , Analysis Guide , Notebooks , Cell Communication⬇ Export ipynb

2026-01-29

SeekSpace Advanced Analysis: Cell Density and Co-localization Based on scider

scider models global cell density in spatial transcriptomics to enable co-localization, boundary detection, ROI identification, and differential density analysis, revealing cellular spatial patterns and interactions within tissues.

Tag：Spatial-seq , Analysis Guide , Notebooks , Co-localization⬇ Export ipynb

2026-01-29

Single-cell Spatial Neighborhood Statistics: Spatial Distribution and Neighborhood Analysis

Based on the phenoptr package, this tutorial introduces how to perform cell spatial distribution statistics and neighborhood analysis using spatial transcriptomics coordinates, calculating the cellular composition around specific cell types to reveal spatial interactions.

Tag：Spatial-seq , Notebooks , Niche⬇ Export ipynb

2026-01-29

Single-Cell Spatial Microenvironment Analysis: Cell Niche Analysis and Clustering Tutorial

Based on the stLearn library, this tutorial covers spatial microenvironment (Niche) analysis, integrating cell spatial locations and neighborhood features to define and cluster different cell niches, resolving tissue spatial structural heterogeneity.

Tag：Spatial-seq , Analysis Guide , Notebooks , Niche⬇ Export ipynb

2026-01-29

SeekSpace Spatial Transcriptomics Multi-Sample Integration Analysis (Seurat Framework)

Based on the Seurat framework, this tutorial demonstrates the multi-sample integration workflow for SeekSpace spatial transcriptomics data, using the Harmony algorithm to remove batch effects for joint clustering and spatial visualization.

Tag：Spatial-seq , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekSpace Spatial Transcriptomics Data Analysis Tutorial (Scanpy / Mouse Brain)

Using mouse brain data as an example, this tutorial explains how to process SeekSpace spatial transcriptomics data with Scanpy, covering data loading, QC, dimensionality reduction, clustering, and spatial gene expression visualization.

Tag：Spatial-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekSpace Spatial Transcriptomics Data Analysis Tutorial (Seurat / Mouse Brain)

Using mouse brain data as an example, this tutorial explains how to process SeekSpace spatial transcriptomics data with Seurat, covering data loading, QC, dimensionality reduction, clustering, and spatial visualization analysis.

Tag：Spatial-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekSpace Advanced Analysis: stLearn-based Cell Communication Hotspot Identification

stLearn-based cell–cell communication analysis identifying hotspots via ligand–receptor (LR) scoring and grid-based detection; includes parameter overview, 10x data loading, cell type injection, grid partitioning, and custom LR pairs/database selection.

Tag：Spatial-seq , Analysis Guide , Notebooks , Cell Communication⬇ Export ipynb

2026-01-29

SeekSpace Advanced Analysis: Spatial Cell Type Density Plotting

Compute and visualize spatial cell type density using coordinates and annotations via kernel density estimation (KDE), supporting 2D and 3D plots.

Tag：Spatial-seq , Notebooks , Plotting⬇ Export ipynb

2026-01-29

SeekSpace Advanced Analysis: Spatial Gene Expression Density Plotting

Use marker gene expression and spatial coordinates to compute and visualize gene expression density with KDE, locating high-expression regions and LR colocalization patterns.

Tag：Spatial-seq , Notebooks , Plotting⬇ Export ipynb

2026-01-29

SeekSpace Spatial Transcriptomics Multi-Sample Integration and Batch Effect Assessment

Load multiple Seurat objects and merge spatial dimensions; remove batch effects with Harmony, perform joint clustering and spatial visualization, and assess batch impact.

Tag：Spatial-seq , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

Spatial Transcriptomics Data Processing: SeekSpace Sample Splitting Method

Describe SeekSpace tile sample splitting: extract embedded images from Seurat RDS (base64 to PNG), organize metadata, and generate analyzable per-sample objects.

Tag：Spatial-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

SeekSpace Spatial Transcriptomics Visualization: Basic Plotting and Parameter Configuration

Spatial visualization with Seurat and ggplot2, demonstrating parameter setup and common plots; supports metadata-grouped spatial scatter and clustering displays.

Tag：Spatial-seq , Notebooks , Plotting⬇ Export ipynb

2026-01-29

SpaGene Advanced Spatial Transcriptomics Analysis: Spatially Variable Genes and Ligand-Receptor Colocalization

Introduce SpaGene’s method using k‑NN spatial graphs and Earth Mover’s Distance (EMD) to identify spatially variable genes, combined with LR colocalization to reveal communication patterns.

Tag：Spatial-seq , Analysis Guide , Notebooks , Co-localization⬇ Export ipynb

2026-01-29

SeekSpace Advanced Analysis: Neighborhood Enrichment and Co-occurrence Analysis Based on Squidpy

Within the cellcharter‑env, perform Squidpy analyses for neighborhood enrichment, co-occurrence, and highly variable genes; includes 10x loading, QC, DR, and spatial coordinate binding.

Tag：Spatial-seq , Analysis Guide , Notebooks , Niche⬇ Export ipynb

2026-01-29

stLearn Advanced Spatial Transcriptomics Analysis: Ligand-Receptor Interaction Hotspot Identification

Compute stLearn ligand–receptor interaction scores and detect hotspots, integrating cell types and spatial neighborhoods to reveal likely communication regions; long runtime, tune parameters and threads accordingly.

Tag：Spatial-seq , Analysis Guide , Notebooks , Cell Communication⬇ Export ipynb

2026-01-29

Pseudotime-Dependent Gene Screening Tutorial: Exploring Dynamic Changes in Gene Expression Along Trajectories

This tutorial uses the Monocle2 framework to identify genes that change dynamically along pseudotime trajectories and fits gene expression trends using natural spline curves to explore regulatory mechanisms during cell differentiation.

Tag：3' scRNA-seq , Analysis Guide , Notebooks , Pseudotime⬇ Export ipynb

2026-01-29

DD5 VDJ Data Method

Tag：5' + Immune Profiling , Method

2026-01-29

DD5 16S Data Method

Tag：5' + Immune Profiling , Method

2026-01-29

Tumor cell calling by integrating CNV and mutations (threshold-based)

Tag：scFAST-seq , Analysis Guide

2025/12/23

Single-cell alternative splicing with STARsolo SJ and MARVEL quantification and differential analysis

Tag：scFAST-seq , Analysis Guide

2025/12/23

Full-length alternative splicing analysis with rMATS workflow and example scripts

Tag：scFAST-seq , Analysis Guide

2025/12/23

Full-length fusion detection with STAR-Fusion module guide and interpretation

Tag：scFAST-seq , Analysis Guide

2025/12/23

Full-length mutation analysis with freebayes VEP and vartrix pipeline

Tag：scFAST-seq , Analysis Guide

2025/12/23

scFAST-seq Mutation Analysis: Data Merging, UMAP Display and Integration

scFAST-seq mutation analysis tutorial, covering mutation data merging from multiple samples, UMAP dimensionality reduction visualization based on mutation features, and multi-sample integration workflow.

Tag：scFAST-seq , Notebooks , Mutation⬇ Export ipynb

2026-01-29

scFAST-seq Mutation Analysis: Dynamic Display of Mutations on Pseudotime Trajectory

Based on monocle2 pseudotime analysis results, visualize the dynamic evolution of mutations along cell development trajectories to help identify key mutations related to development or differentiation.

Tag：scFAST-seq , Notebooks , Pseudotime⬇ Export ipynb

2026-01-29

Single-cell Fine Resolution: Cell Clustering Strategy Based on LncRNA Expression Features

This article explores a supplementary strategy for cell clustering in single-cell RNA sequencing analysis using long non-coding RNAs (LncRNAs) in addition to traditional highly variable genes.

Tag：scFAST-seq , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

FFPE single-cell data processing: ambient RNA removal doublet filtering parameter tuning

Provides background removal (SoupX) doublet filtering and parameter optimization workflows for FFPE sample background RNA doublets and quality control issues.

Tag：FFPE scRNA-seq , FAQ

2025/7/25

scMethyl + RNA Multi-omics Quality Control Report Documentation

Details the definitions, calculation methods, and interpretation guidelines for various metrics in the SeekMethyl QC report, aiming to help users understand sequencing data and library quality.

Tag：scMethyl + RNA-seq , Analysis Guideline

2026-03-16

scMethyl + RNA Multi-omics Analysis Guideline

Provides systematic downstream analysis pipelines and tutorial guidelines for single-cell methylation multi-omics, covering key steps such as doublet detection, data QC and integration, and differential and functional enrichment analysis.

Tag：scMethyl + RNA-seq , Analysis Guideline

2026-03-16

scMethyl + RNA Multi-omics - Basic Analysis

This module is designed for the basic analysis of single-cell methylation multi-omics data (containing both RNA and methylation info), implementing core workflows such as data integration, quality control, batch effect correction, and cell atlas construction.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - Doublet Detection

This module aims to identify and filter doublets from single-cell methylation sequencing data, providing three complementary detection methods based on transcriptome cell types, methylation read counts, and methylation rates to improve identification accuracy.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - Integration Analysis Tutorial Based on MOFA+ Algorithm

This tutorial details how to use the MOFA+ (Multi-Omics Factor Analysis v2) framework for multi-omics integration analysis of single-cell methylation (DNA Methylation) and transcriptome (RNA) data.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-30

scMethyl + RNA Multi-omics - Integration Tutorial Using WNN

This tutorial explains how to integrate single-cell DNA methylation and transcriptome (RNA) data using the Scanpy and Muon frameworks. You will build a joint embedding, perform clustering, and visualize results in a unified UMAP space.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-30

scMethyl + RNA Multi-omics - Differential and Functional Enrichment Analysis

This module integrates single-cell methylation differential analysis and functional enrichment analysis, aiming to identify differentially methylated regions/genes and differentially expressed genes from multi-omics data, and further analyze their biological functions via GO and KEGG.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - Inter-group Cell Type Differential Enrichment Analysis

Designed for inter-group differential and functional enrichment analysis of single-cell methylation multi-omics data, this module performs differential expressed gene (DEG) and differentially methylated gene (DMG) analysis for each cell type between groups.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - MethSCAn Differential Methylation Analysis

Based on MethSCAn, this module supports variably methylated region (VMR) detection, cell clustering, and the identification of differentially methylated regions (DMRs) between different cell populations in single-cell methylation data.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - DMR Functional Enrichment Analysis

Based on rGREAT, this module performs functional enrichment analysis on differentially methylated regions (DMRs), linking DMRs to adjacent genes and assessing their enrichment in functional categories like GO and MSigDB to reveal potential biological functions.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - DMR Motif Enrichment Analysis

Based on HOMER, this module is used for DNA sequence motif analysis in genomic regions such as DMRs. It supports de novo motif finding and known motif enrichment analysis to identify transcription factors that may be involved in regulation.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

scMethyl + RNA Multi-omics - CNV Analysis

Based on CopyKit, this module is designed to infer genome-wide copy number variations (CNVs) from single-cell sequencing data, supporting variant identification, differentiation between normal and tumor cells, and exploration of intra-tumor heterogeneity and clonal evolution.

Tag：scMethyl + RNA-seq , Notebooks⬇ Export ipynb

2026-03-16

Cell Composition Analysis: Bar Plot of Cell Type Proportions Across Samples and Groups

Cell Composition Analysis: Bar Plot of Cell Type Proportions Across Samples and Groups

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Plotting⬇ Export ipynb

2026-01-29

Cell Type Enrichment Analysis: Heatmap Plotting Based on Chi-Square Test and R.oe Values

Cell Type Enrichment Analysis: Heatmap Plotting Based on Chi-Square Test and R.oe Values

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Plotting⬇ Export ipynb

2026-01-29

Cell Proportion Difference Analysis: Box Plot of Cell Proportions Between Groups

Demonstration using ESCC article reproduction - Myeloid subset reproduction workflow data

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Cell Annotation⬇ Export ipynb

2026-01-29

Single-cell Host-Virus Interaction: Correlation between Viral and Host Gene Expression

By observing the correlation between viral gene expression and key host genes, we explore the interaction between viral and host genes to understand how viruses affect host cell functions.

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , Analysis Guide , Notebooks , Correlation⬇ Export ipynb

2026-01-29

Single-Cell Host-Virus Interaction: Correlation Study between Gene Expression Abundance and Viral Load

Gene Expression Abundance: Number of genes expressed in cells Viral Load: Sum of normalized viral gene expression values (refer to the article above)

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , Analysis Guide , Notebooks , Correlation⬇ Export ipynb

2026-01-29

scRNA-seq Cell Annotation Tutorial: Type Identification Based on Marker Genes

Introduces how to use the SingleR automated annotation tool, combined with reference datasets like HumanPrimaryCellAtlas, to perform high-precision cell type identification and annotation for single-cell transcriptomics data.

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Cell Annotation⬇ Export ipynb

2026-01-29

Single-cell Visualization Customization: Custom Color and Order t-SNE and Proportion Plot

Single-cell Visualization Customization: Custom Color and Order t-SNE and Proportion Plot

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Notebooks , Cell Annotation⬇ Export ipynb

2026-01-29

Single-cell Differential Expression and Functional Enrichment Analysis Tutorial

Introduces how to perform single-cell differential expression (DE) and functional enrichment analysis (ORA/GSEA) using Seurat and clusterProfiler, covering inter-cluster and group comparisons, GO/KEGG annotations, and visualizations like volcano plots and heatmaps.

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Differential Enrichment⬇ Export ipynb

2026-01-29

Visualization of Single-cell Gene Expression Correlation Scatter Plots

Visualization of Single-cell Gene Expression Correlation Scatter Plots

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Notebooks , Plotting⬇ Export ipynb

2026-01-29

Single-cell Gene Expression Dynamics: Pseudotime Gene Expression Curve Fitting

Single-cell Gene Expression Dynamics: Pseudotime Gene Expression Curve Fitting

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Notebooks , Pseudotime⬇ Export ipynb

2026-01-29

scRNA-seq Multi-sample Integration Tutorial (Seurat / Harmony)

This tutorial covers the scRNA-seq multi-sample integration workflow, including two data loading methods (Cloud RDS and standard 10x matrix), Seurat object creation, QC metrics calculation (mitochondrial % / gene counts), and Harmony-based integration for batch effect removal.

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Data Integration⬇ Export ipynb

2026-01-29

Seurat scRNA-seq Analysis Workflow Tutorial (QC / Integration / Clustering)

Seurat is a comprehensive R package designed for quality control, analysis, and exploration of single-cell RNA sequencing data. This tutorial provides a complete analysis workflow from data loading to marker finding, covering quality control, DoubletFinder doublet removal, data integration (Harmony/CCA/Merge), dimensionality reduction, clustering, and differential expression analysis. **Important**: This tutorial should be run using the **"common_r"** kernel.

Tag：scATAC + RNA-seq , Spatial-seq , 3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , scMethyl + RNA-seq , Analysis Guide , Notebooks , Basic Analysis⬇ Export ipynb

2026-01-29

DD3 Data Method

Tag：3' scRNA-seq , 5' + Immune Profiling , scFAST-seq , FFPE scRNA-seq , Method

2026-01-29

Hemoglobin gene contamination: sources impact and mitigation (with R examples)

Introduces the sources impacts and various handling methods (filtering gene removal and re-clustering etc.) of hemoglobin gene contamination and provides R code examples.

Tag：Analysis Guide , FAQ

2025/7/25

Exogenous GFP not detected: troubleshooting and GTF annotation fix

Locates the reasons for GFP not being quantified in featureCounts (missing gene/transcript lines in GTF annotations) and provides example commands to fix GTF.

Tag：Analysis Guide

2025/7/25

Oversized chromosome reference splitting and indexing for non-mouse species (samtools compatibility)

Explains the compatibility issues of samtools with oversized chromosome indices and provides splitting scripts and usage instructions for subsequent alignment and indexing.

Tag：Analysis Guide , FAQ

2025/8/7

DD Antibody and CRISPR QC and Cell Ranger analysis guide

Introduces the workflow and considerations for converting DD platform data to 10x format and using Cell Ranger to analyze Antibody/CRISPR data.

Tag：Analysis Guide

2025/8/7

Barcode-Converter: convert DD barcodes to 10x format

Usage instructions for barcode conversion tool example commands and parameter explanations applicable to converting DD barcodes to 10x barcodes.

Tag：Analysis Guide , FAQ

2025/8/7

SeekSoul Tools v1.3.0 special-species VDJ ref preparation workflow (IMGT TRUST4)

Ref preparation workflow for SeekSoul Tools v1.3.0 and TRUST4 including species sequence extraction from IMGT and formatting script examples

Tag：Analysis Guide , FAQ

2025/8/7

Mixed-species scRNA-seq analysis guide: principles interpretation and common issues

Explanation of the principles result interpretation and common issues regarding mixed-species data analysis

Tag：Analysis Guide , FAQ

2025/11/21

Plasma cell immunoglobulin gene contamination: detection impact and downstream recommendations

Assessment of the impact of diffuse active immunoglobulin (IG) gene expression related to plasma cells in the sample along with recommendations for downstream analysis

Tag：Analysis Guide , FAQ

2025/11/26

Single-cell FAQ overview

Comprehensive guide covering common questions and recommendations for single-cell sample preparation transportation quality control library construction and downstream analysis.

Tag：FAQ

2025/8/28

SeekArc × 10x cross-platform scRNA-seq integration and batch-effect assessment (Monocle2 hdWGCNA)

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.

Tag：Batch Effect

2025/12/23

Single-cell annotation for special animal and plant species: markers orthologs enrichment

This article will briefly outline common approaches and methods for single-cell annotation of special species, helping you quickly grasp the relevant content.

Tag：Cell Annotation

2025/8/28

Troubleshooting guide for undetected specific genes (endogenous / exogenous insertion) in scRNA-seq

A structured checklist to identify why a target gene is not detected in scRNA-seq

Tag： covering biological

and computational causes (including exogenous insertions).

Page size5 / page10 / page20 / page30 / page40 / page50 / page100 / pageAllPage 1 / 1 · Total 0

Let's explore the infinite possibilities of life science together!

Built with ❤️ by SeekSoul™ Online Team

---