Single-cell RNA-seq Analysis Platform

This platform provides a comprehensive pipeline for analyzing single-cell RNA sequencing data. From quality control to cell type annotation and functional analysis, our integrated workflow helps you uncover cellular heterogeneity and biological insights from your scRNA-seq experiments.

Streamlining single-cell data analysis for researchers.

Data Input

Upload your single-cell RNA-seq data and associated metadata files.

scRNA-seq Expression Data

Supported formats: .h5ad (AnnData), .csv, .mtx, .loom

Select File

Cell Metadata (Optional)

Supported formats: .csv, .tsv, .txt

Select File

Gene List (Optional)

Genes of interest for focused analysis

Select File

Sample Information (Optional)

Experimental conditions, batches, etc.

Select File

Data Structure Preview

Quality Control Parameters

Set thresholds for filtering low-quality cells and genes.

Cell Filtering

Minimum Number of Genes per Cell

Maximum Number of Genes per Cell

Maximum Mitochondrial Genes Ratio (%)

Maximum Ribosomal Genes Ratio (%)

Minimum UMIs per Cell

Maximum UMIs per Cell

Gene Filtering

Minimum Number of Cells Expressing Gene

Minimum Expression Threshold

Advanced Options

Filter ribosomal genes

Filter mitochondrial genes

Filter housekeeping genes

Remove potential doublets (using scrublet)

Normalization & Feature Selection

Choose methods for data normalization and highly variable gene selection.

Normalization Method

Select Normalization Approach

Feature Selection

Number of Highly Variable Genes

HVG Selection Method

Batch Correction (if applicable)

Batch Correction Method

Batch Variable (from metadata)

Dimensionality Reduction & Clustering

Set parameters for dimensionality reduction and cell clustering.

Dimensionality Reduction

Number of PCA Components

UMAP Parameters: n_neighbors

UMAP Parameters: min_dist

Additional Reduction Methods

Hold Ctrl/Cmd to select multiple

Clustering Parameters

Clustering Method

Resolution (for Louvain/Leiden)

Higher values = more clusters

Use Neighborhood Graph from

Cell Type Annotation

Annotate cell clusters with known cell type identities.

Annotation Method

Select Annotation Approach

Reference Database (for reference-based methods)

Reference Dataset

Tissue Type

Marker Genes (for manual annotation)

Upload Marker Gene File

CSV/TSV with columns: cell_type, gene, weight

Select File

Format: One row per gene with associated cell type and confidence weight

Differential Expression Analysis

Identify genes with significant expression differences between cell clusters or groups.

Comparison Settings

Compare By

Metadata Group (if applicable)

Test Type

Multiple Comparison Correction

Significance Thresholds

Adjusted P-value Cutoff

Minimum Log2 Fold Change

Minimum Expression Level

Minimum Detection Rate (%)

Advanced Options

Test each cluster against all others

Find markers for all cluster pairs

Use raw counts for differential expression tests

Functional Enrichment Analysis

Perform enrichment analysis on gene sets derived from your scRNA-seq data.

Enrichment Parameters

Gene Sets to Test

Enrichment Database

Hold Ctrl/Cmd to select multiple

Species

Adjusted P-value Cutoff

Custom Gene Sets (Optional)

Upload Custom Gene Set File

GMT, CSV, or TSV format gene sets

Select File

Data Visualization

Select and view interactive visualizations of your single-cell analysis results.

Dimensionality Reduction

UMAP/t-SNE with cluster coloring

Cell Type Annotation

UMAP with cell type coloring

Gene Expression

Feature plots for marker genes

Differential Genes

Volcano plots & MA plots

Enrichment Results

GO/KEGG enrichment bar plots

Cell Composition

Stacked bar plots by group

QC Metrics

Violin plots of QC measures

Expression Heatmap

Cluster-specific marker genes

Pseudotime Trajectory

Cell differentiation paths

Analysis Results

View and download your single-cell RNA-seq analysis results.

No results available yet. Please upload data and run analysis first.

Total Cells

Total Genes

Cell Clusters

Cell Types

Top Differentially Expressed Genes (Cluster 0 vs Others)

Gene	Log2FC	Adjusted P-value	Percent Expressed (Cluster)	Percent Expressed (Others)

Cell Type Distribution

Cell Type	Number of Cells	Percentage	Major Cluster	Top Marker