What does this module do?

Performs quality control checks on RNA-Seq count data, including distribution analysis, library size comparison, PCA visualization, and outlier detection.

Required Inputs

• Count matrix (genes × samples)
• First column: gene names
• Remaining columns: numeric counts

How to use

1. Upload count matrix CSV
2. Check distribution (boxplot)
3. Compare library sizes
4. Inspect PCA clustering
5. Identify outlier samples

Sample Dataset

Sample Output

---

What does this module do?

Runs DESeq2 on count data with flexible metadata handling.

Required Inputs

• Count matrix (CSV )
• Metadata (any column names)

How to use

1. Upload counts + metadata
2. Select sample & condition columns
3. Click Run

Sample Dataset

Sample Output

---

What does this module do?

Displays log fold change vs mean expression to assess differential expression patterns.

Required Inputs

• DE results with log2FoldChange and padj
• Optional: baseMean column
• Optional uploaded CSV

How to use

1. Run DE Analysis or upload DE results
2. Set thresholds (padj and log2FC)
3. Click 'Generate MA Plot'
4. Interpret up/down regulated genes

Sample Dataset

Sample Output

---

What does this module do?

Performs Principal Component Analysis (PCA) to visualize variation between samples.

Required Inputs

• Normalized expression data (VST)
• Optional uploaded expression matrix

How to use

1. Upload dataset OR run DE Analysis
2. Enable metadata coloring (optional)
3. Click 'Run PCA'
4. Interpret clustering patterns

Sample Dataset

Sample Output

---

What does this module do?

Visualizes gene expression patterns across samples.

Required Inputs

• Normalized expression data (VST)
• Optional uploaded CSV

How to use

1. Upload dataset OR run DE Analysis
2. Select number of top variable genes
3. Enable scaling
4. Click 'Generate Heatmap'

Sample Dataset

Sample Output

---

What does this module do?

Calculates similarity between samples using correlation metrics.

Required Inputs

• Expression Matrix (CSV) or normalized expression matrix generated from DE Analysis
• Optional uploaded dataset

How to use

1. Upload dataset OR run DE Analysis
2. Select correlation method
3. Click 'Generate Correlation'
4. Interpret similarity between samples

Sample Dataset

Sample Output

---

What does this module do?

Displays differential expression results by plotting fold change against statistical significance.

Required Inputs

• DESeq2 results (log2FoldChange & padj)
• Optional uploaded dataset

How to use

1. Run DE Analysis
2. Adjust thresholds
3. Click 'Generate Plot'
4. Interpret significant genes

Sample Dataset

Sample Output

---

What does this module do?

t-SNE visualizes similarity between samples using dimensionality reduction.

Required Inputs

• Normalized data (VST) OR expression matrix
• Genes × Samples format

Steps

1. Upload dataset OR run DE
2. Set perplexity
3. Click Run t-SNE

Sample Dataset

Sample Output

---

What does this module do?

UMAP reduces high-dimensional data to visualize sample similarity.

How to use

1. Upload expression matrix OR use uploaded dataset
2. Ensure first column = gene names
3. Click 'Run UMAP'
4. Wait for result (few seconds)

Sample Dataset

---

What does this module do?

Visualizes expression levels of one or more genes across samples. Useful for comparing expression patterns between conditions.

Required Inputs

• Normalized expression data (VST) from DESeq2 OR
• User-uploaded expression matrix (genes × samples)
• Optional metadata with 'condition' column for coloring

How to use

1. Upload your expression matrix (optional)
2. OR run DE Analysis to use normalized data
3. Search and select one or more genes
4. Enable 'Color by condition' if metadata is available
5. Click 'Plot Expression'
6. Download the plot if needed

Input Format Example

Gene,Sample1,Sample2,Sample3
GeneA,120,130,300
GeneB,50,60,20

Output

• Boxplot of gene expression across samples
• Multiple genes shown side-by-side
• Optional coloring by condition
• Downloadable plot

Sample Dataset

Sample Output

---

What does this module do?

Displays the most significant differentially expressed genes based on fold change and adjusted p-value.

Required Inputs

• DESeq2 results OR uploaded CSV file
• Columns required: log2FoldChange and padj

How to use

1. Run DE Analysis OR upload file
2. Set thresholds
3. Click 'Show Top Genes'

Sample Dataset

Sample Output

---

What does this module do?

Groups samples based on expression similarity using hierarchical clustering or k-means.

Required Inputs

• Normalized expression matrix (VST) or uploaded CSV
• Genes × Samples format

How to use

1. Upload data or run DE Analysis
2. Choose clustering method
3. Set number of clusters (for k-means)
4. Click 'Run Clustering'

Sample Dataset

Sample Output

---