Bioconductor

Import count data using read.table

Create a DESeqDataSet object

Run the DESeq() function

Extract results using results()

Visualize with a Volcano Plot.

Normalize data using scran

Perform PCA for dimensionality reduction

Run UMAP or t-SNE visualization

Execute graph-based clustering

Identify marker genes per cluster.

Read alignment files (BAM)

Calculate signal coverage

Detect enriched regions (peaks)

Annotate peaks to the nearest gene using ChIPseeker

Visualize genomic tracks.

Prepare a MultiAssayExperiment object

Align samples across different assay types

Run integrated analysis using MOFA2

Identify cross-assay correlations

Perform pathway enrichment on combined signatures.

Load VCF files into R

Map variants to reference genomes

Predict amino acid changes using VariantAnnotation

Query external databases (SIFT/Polyphen)

Filter for high-impact mutations.

Load Visium or Slide-seq data into a SpatialExperiment object

Perform spatial clustering

Overlay expression heatmaps onto histology images

Analyze spatially variable genes

Identify cell-cell interactions based on proximity.

Process raw mzML files with MSnbase

Perform peptide-to-protein aggregation

Normalize intensities across samples

Apply statistical tests for differential abundance

Plot heatmaps of protein clusters.