# OmicsQuest

> OmicsQuest is an on-demand bioinformatics services company (CRO) for single-cell, spatial, multi-omics, proteomics and imaging data. A bioinformatician and a cell biologist work on every project, from raw data to publication-quality figures. Pricing starts at $79/hour with capped estimates and unlimited figure customization. For startups and new labs, OmicsQuest builds Nextflow/Snakemake pipelines and maintains them for a flat monthly fee from $1,490/month. Motto: "A quest for the real biology in your data."

Key facts:
- Services: single-cell RNA-seq, snATAC-seq, 10x Multiome, CITE-seq, TCR/BCR, CRISPR and Perturb-seq screens, Visium HD, Xenium, CosMx, MERSCOPE, GeoMx, CODEX, bulk RNA-seq, ChIP-seq, CUT&RUN, ATAC-seq, methylation, WGS/WES, metagenomics, proteomics, protein-protein interactions, Cell Painting, live-cell imaging and tracking, target discovery and drug discovery.
- Model: bioinformatician and cell biologist duo teams; on-demand, reserved monthly hours, or pipeline retainers.
- Pricing: from $79/hour (senior specialist work from $119/hour); typical scRNA-seq project $1,975-$3,555; bulk RNA-seq $632-$1,580.
- Target discovery: TargetQuest, an agentic AI system (coordinating, specialist, falsification, evaluator and experiment-selection agents) supervised by OmicsQuest scientists, built on the Virtual Human: a proprietary substrate of 50 million+ single-cell and spatial profiles.
- Terms: mutual NDA, encrypted transfer, clients own all data, code, figures and IP. Research use only.
- Contact: contact@omicsquest.com | https://omicsquest.com/contact/
- Full text for language models: https://omicsquest.com/llms-full.txt

## Core pages
- [Home](https://omicsquest.com/): What OmicsQuest does, the duo team model, pricing summary.
- [All services](https://omicsquest.com/services/): Every service and analysis, by omics discipline.
- [Pricing](https://omicsquest.com/pricing/): From $79/hour; typical project costs; pipeline retainer from $1,490/month.
- [Pipelines for new labs and startups](https://omicsquest.com/pipelines/): Pipeline build and flat-rate maintenance.
- [Hire a bioinformatician or outsource?](https://omicsquest.com/why-outsource-bioinformatics/): Cost comparison and break-even analysis.
- [Case studies](https://omicsquest.com/case-studies/): Rescued omics analyses.
- [Insights and guides](https://omicsquest.com/insights/): All expert guides.
- [About](https://omicsquest.com/about/): Team model, analysis standards, data security.
- [FAQ](https://omicsquest.com/faq/): Pricing, security, formats, timelines.
- [Contact](https://omicsquest.com/contact/): Book a free scoping call.

## Services
- [Single-cell RNA-seq analysis services](https://omicsquest.com/services/single-cell-rna-seq-analysis/): scRNA-seq and snRNA-seq: QC, integration, annotation, pseudobulk DE, trajectories.
- [snATAC-seq and scATAC-seq analysis services](https://omicsquest.com/services/single-nucleus-atac-seq-analysis/): Chromatin accessibility per cell type: peaks, motifs, footprints, enhancer-gene links.
- [Multiome and CITE-seq analysis services](https://omicsquest.com/services/multiome-cite-seq-analysis/): 10x Multiome, CITE-seq, TEA-seq, DOGMA-seq: joint embeddings and regulatory networks.
- [TCR and BCR immune repertoire analysis services](https://omicsquest.com/services/tcr-bcr-immune-repertoire-analysis/): Clonotypes, clonal expansion, diversity, SHM and lineage trees, linked to cell state.
- [CRISPR screen and Perturb-seq analysis services](https://omicsquest.com/services/crispr-screen-perturb-seq-analysis/): Pooled screens and single-cell perturbation screens: hits, effects, programs.
- [Visium HD and Visium spatial transcriptomics analysis](https://omicsquest.com/services/visium-hd-analysis/): 2 µm bins to cells: segmentation, deconvolution, domains, niches, signaling.
- [Xenium in situ data analysis services](https://omicsquest.com/services/xenium-analysis/): Re-segmentation, transcript QC, annotation, neighborhoods and niches.
- [Spatial omics analysis: CosMx, MERSCOPE, GeoMx, Stereo-seq and CODEX](https://omicsquest.com/services/spatial-transcriptomics-analysis/): CosMx, MERSCOPE, GeoMx, Stereo-seq, CODEX, IMC: one analysis framework.
- [Bulk RNA-seq data analysis services](https://omicsquest.com/services/rna-seq-analysis/): DE, batch handling, pathways, isoforms, splicing, deconvolution, time courses.
- [ChIP-seq, CUT&RUN, CUT&Tag and ATAC-seq analysis](https://omicsquest.com/services/chip-seq-cut-and-run-atac-seq-analysis/): Peak calling, differential binding, motifs, super-enhancers, integration.
- [Whole genome, exome and variant analysis](https://omicsquest.com/services/genomics-variant-analysis/): Germline and somatic variants, CNV, SV, GWAS, long reads.
- [Metagenomics and microbiome analysis](https://omicsquest.com/services/metagenomics-microbiome-analysis/): 16S/ITS, shotgun, MAGs, functional profiles, compositional statistics.
- [Proteomics and phosphoproteomics data analysis](https://omicsquest.com/services/proteomics-analysis/): DDA, DIA, TMT, phospho, kinase activity, Olink and SomaScan.
- [Protein-protein interaction analysis services](https://omicsquest.com/services/protein-protein-interaction-analysis/): AP-MS, proximity labeling, Y2H, networks, AlphaFold-Multimer.
- [Cell Painting and single-cell phenomics analysis](https://omicsquest.com/services/cell-painting-analysis/): Image-based profiling: features, plate correction, MoA, hits.
- [Live-cell imaging and cell tracking analysis](https://omicsquest.com/services/live-cell-imaging-tracking-analysis/): Segmentation, tracking, lineages, migration, division and dynamics.
- [Target discovery with TargetQuest](https://omicsquest.com/services/target-discovery/): Agentic AI on 50M+ cell and spatial profiles: genetics, specificity, safety, falsification.
- [Drug discovery bioinformatics: mechanism, repurposing and biomarkers](https://omicsquest.com/services/drug-discovery-bioinformatics/): MoA, connectivity mapping, repurposing, biomarkers, stratification.

## Guides
- [Single-cell RNA-seq analysis: a best-practice workflow from FASTQ to figures](https://omicsquest.com/insights/single-cell-rna-seq-analysis-best-practices/): A step-by-step scRNA-seq workflow: alignment, empty droplets, ambient RNA, doublets, QC, integration, annotation, pseudobulk DE and trajectories.
- [12 mistakes that ruin single-cell RNA-seq analyses, and how experts avoid them](https://omicsquest.com/insights/single-cell-rna-seq-mistakes/): The scRNA-seq errors we fix most: pseudoreplication, doublets called new cell types, ambient RNA, over-integration, fixed QC cutoffs, UMAP myths.
- [How much does bioinformatics analysis cost? A 2026 pricing guide](https://omicsquest.com/insights/bioinformatics-analysis-cost/): 2026 costs for scRNA-seq, RNA-seq, Visium HD, Xenium, ATAC-seq and Cell Painting analysis, and how hourly, per-sample and salary pricing compare.
- [Visium HD vs Xenium vs CosMx vs MERSCOPE: choosing a spatial transcriptomics platform](https://omicsquest.com/insights/spatial-platforms-visium-hd-xenium-cosmx/): Visium HD, Xenium, CosMx, MERSCOPE and GeoMx compared: resolution, gene coverage, sample types, analysis challenges and which to choose.
- [Pseudobulk differential expression in single-cell RNA-seq: why cells are not replicates](https://omicsquest.com/insights/pseudobulk-differential-expression-single-cell/): Why cell-level tests produce false positives in scRNA-seq, how pseudobulk aggregation works, when to use mixed models, and a practical DESeq2/edgeR workflow.
- [Visium HD data analysis: from 2 µm bins to cells, domains and niches](https://omicsquest.com/insights/visium-hd-analysis-guide/): How to analyze Visium HD: QC and tissue masking, 8 µm bins vs nucleus segmentation, RCTD or cell2location deconvolution, domains and niches.
- [Doublets and ambient RNA in single-cell data: how to detect and remove them](https://omicsquest.com/insights/doublets-ambient-rna-single-cell/): How doublets and ambient RNA distort scRNA-seq and snRNA-seq results, and how to remove them with scDblFinder, DoubletFinder, CellBender, SoupX and EmptyDrops.
- [Batch correction and data integration in single-cell RNA-seq: Harmony, scVI, Seurat and when to use each](https://omicsquest.com/insights/single-cell-batch-correction-integration/): Integrate scRNA-seq samples without erasing biology: choosing Harmony, scVI, scANVI or Seurat, scIB benchmarking and avoiding over-correction.
- [How to design a single-cell experiment so the analysis succeeds](https://omicsquest.com/insights/single-cell-experimental-design/): Plan scRNA-seq studies that succeed: biological replicates, cells per sample, sequencing depth, cells vs nuclei, multiplexing and batch layout.
- [Xenium data analysis: segmentation, QC and the pitfalls that create false biology](https://omicsquest.com/insights/xenium-analysis-guide/): How to analyze 10x Xenium data: transcript QC, re-segmentation with Baysor or Proseg, panel-aware annotation, neighborhoods and niches.
- [snATAC-seq and 10x Multiome analysis: from fragments to regulatory networks](https://omicsquest.com/insights/snatac-seq-multiome-analysis-guide/): snATAC-seq and 10x Multiome analysis: fragment QC, TSS enrichment, iterative LSI, per-cell-type peaks, chromVAR, footprints and SCENIC+ networks.
- [Bulk RNA-seq differential expression done right: DESeq2, edgeR, limma and batch effects](https://omicsquest.com/insights/bulk-rna-seq-differential-expression-guide/): Bulk RNA-seq best practices: QC, Salmon vs STAR, design matrices, DESeq2 vs edgeR vs limma-voom, batch effects, shrinkage and enrichment pitfalls.
- [Cell Painting analysis: from images to phenotypic profiles and mechanism of action](https://omicsquest.com/insights/cell-painting-analysis-pipeline/): A Cell Painting analysis pipeline: illumination correction, segmentation, features, plate effects, normalization, mAP, hit calling and MoA.
- [Cell tracking in live-cell imaging: segmentation, linking, lineages and validation](https://omicsquest.com/insights/cell-tracking-live-imaging-analysis/): How to track cells in time-lapse microscopy: Cellpose segmentation, TrackMate and ultrack linking, lineage trees, accuracy checks, common errors.
- [Protein-protein interaction analysis: scoring AP-MS and BioID, building networks, modeling structures](https://omicsquest.com/insights/protein-protein-interaction-analysis-guide/): Analyzing protein-protein interaction data: SAINT and CRAPome scoring for AP-MS and BioID, quantitative interactomics, networks, AlphaFold-Multimer.
- [Computational target discovery with multi-omics: how to build an evidence-weighted shortlist](https://omicsquest.com/insights/target-discovery-multi-omics/): How biotech teams prioritize drug targets: human genetics, single-cell and spatial specificity, CRISPR dependency, tractability and safety.

## Case studies
- [The novel cell type that was a doublet](https://omicsquest.com/case-studies/novel-cluster-was-doublets/): A reviewer questioned a 'hybrid' population co-expressing T-cell and macrophage markers. Our re-analysis showed it was doublets and ambient RNA, and uncovered a real, smaller exhausted T-cell state.
- [4,000 'significant' genes, then 212 real ones](https://omicsquest.com/case-studies/pseudoreplication-reviewer-rescue/): Differential expression between knockout and wild-type microglia reported more than 4,000 significant genes. A reviewer asked why. The answer was pseudoreplication.
- [Visium HD: when 2 µm bins were analyzed as cells](https://omicsquest.com/case-studies/visium-hd-bins-not-cells/): A first-pass analysis clustered raw 2 µm bins and produced dozens of uninterpretable clusters driven by UMI depth. We moved the analysis to segmented cells and 8 µm bins.
- [Xenium: the ligand-receptor pair that was segmentation leakage](https://omicsquest.com/case-studies/xenium-segmentation-leakage/): T cells appeared to express a myeloid ligand and its receptor at once, suggesting autocrine signaling. It was transcript spillover from adjacent cells.
- [Bulk RNA-seq: when batch was perfectly confounded with treatment](https://omicsquest.com/case-studies/batch-confounded-rna-seq/): A startup's lead program rested on 2,800 differentially expressed genes. The first PCA component was sequencing run, and the run was the treatment.
- [Cell Painting: hits that were well positions](https://omicsquest.com/case-studies/cell-painting-plate-effects/): Most 'active' compounds sat in outer wells, and replicate compounds failed to find each other. The phenotypes were mostly position effects.
- [Live imaging: lineages broken by identity swaps](https://omicsquest.com/case-studies/live-imaging-identity-swaps/): A lab saw unexpectedly variable cell-cycle times in sister cells. Most of the variability came from tracking errors in crowded fields.
- [From laptop scripts to a production pipeline](https://omicsquest.com/case-studies/startup-pipeline-rebuild/): When the only computational scientist left, nobody could reproduce last quarter's results. We rebuilt the analysis as a tested, versioned pipeline the team owns.