IAS CONTRACT DATA ANALYSIS

Turn existing research data into review-ready analysis

We receive existing NGS, imaging, proteomics, spatial multi-omics, and related data, then use IAS for QC, analysis, visualization, and result organization. Scope and deliverables are agreed before work starts.

  • Existing data
  • IAS analysis and visualization
  • Human review
  • Research use only
Existing dataFASTQ · H5AD · TIFF · mzML
IASQC · analysis · visualizationDocumented conditions
Review-ready outputsTables · figures · report

What we can accept today

LifeAnalytics covers the analysis stage after data generation. Wet-lab, imaging, measurement, and other data-acquisition work are excluded; data must fit an IAS analysis workflow.

Included

  • Preflight review of the objective, comparison design, and deliverables
  • Input-format review and basic QC
  • IAS standard analysis, visualization, and result organization
  • Report covering conditions, key results, and limitations
  • Delivery review and scoping of optional follow-up analysis

Not included

  • Specimen receipt, extraction, or sample QC
  • Library preparation or sequencing
  • Microscopy acquisition, mass-spectrometry measurement, or spatial-omics data generation
  • Clinical diagnosis, treatment decisions, or guaranteed conclusions
  • Unapproved third-party licenses, databases, or long-term storage
  • Guaranteed results from data that do not meet quality requirements

ANALYSIS CATALOG

Find the right analysis across seven major fields

You can start with your data type and research question even when the analysis name is unknown. We separate standard and custom work and confirm inputs, comparison design, feasibility, and deliverables before quoting.

57analysis options

01 / 07

Transcriptomics & epigenomics

Connect expression and regulatory changes to study design, QC, candidate discovery, and functional interpretation.

Standard analysis options

  • RNA-seq FASTQ QC, alignment/pseudo-alignment, gene and transcript quantification
  • Normalization, PCA, clustering, differential expression, GO and pathway analysis
  • ChIP-seq library QC, peak calling, peak annotation, and browser tracks
  • Re-statistics and figure regeneration from count matrices or existing expression tables

Custom design / additional quote

  • Isoform, splicing, and fusion-candidate review
  • Motif, IDR, differential peaks, and multi-condition integration
  • Feasibility review for ATAC-seq, miRNA-seq, and non-model organisms
  • Comparison or integration with public datasets

Typical input formatsFASTQ, BAM/CRAM, count matrices, CSV/TSV, sample sheets, reference genome and GTF/GFF

Discuss this field
02 / 07

Genomics & variants

Organize sequence quality, coverage, variant candidates, and annotations into a traceable research-review package.

Standard analysis options

  • WGS/WES read QC, mapping, duplicate and coverage review
  • Germline SNV/indel calling, filtering, and basic annotation
  • VCF re-annotation, cross-sample comparison, and candidate tables
  • Reporting of analysis settings, exclusion rules, and quality metrics

Custom design / additional quote

  • CNV/SV, tumor-normal pairs, and family studies
  • Candidate prioritization using rare variants and phenotype metadata
  • Feasibility review for non-model organisms or custom references
  • Re-analysis of existing VCFs and legacy reports

Typical input formatsFASTQ, BAM/CRAM, VCF/gVCF, PED, phenotype tables, reference genome and annotation

Discuss this field
03 / 07

Single-cell & spatial omics

Visualize cell populations, markers, regional differences, and neighborhoods while preserving sample and spatial context.

Standard analysis options

  • Cell QC, doublet review, normalization, and batch integration
  • PCA/UMAP, clustering, marker discovery, and initial cell-type review
  • Spatial domains/clusters, region or condition comparison, and markers
  • Registration and overlay of tissue images with spot/cell coordinates

Custom design / additional quote

  • Trajectory/pseudotime and cell communication
  • Cell neighborhoods, colocalization, and spatial interaction trends
  • Cross-modality integration of RNA, protein, and images
  • Integration across platforms or legacy analysis outputs

Typical input formatsH5AD, h5, matrix/barcodes/features, CSV/TSV, spatial coordinates, tissue images, sample metadata

Discuss this field
04 / 07

Microbiome & metagenomics

Evaluate community composition, group differences, and functional profiles with explicit batch and comparison design.

Standard analysis options

  • 16S/18S/ITS primer trimming, denoising, chimera removal, and ASV/OTU generation
  • Taxonomy, composition tables, alpha/beta diversity, and ordination
  • Shotgun metagenome host-read removal, classification, and basic functional profiles
  • Basic statistics and visualization using sample metadata

Custom design / additional quote

  • De novo assembly, MAG/binning, and detailed functional pathways
  • Antimicrobial-resistance and virulence-associated gene exploration
  • Multifactor, longitudinal, or covariate-aware comparisons
  • Feasibility review for virome and other specialized datasets

Typical input formatsFASTQ, feature and taxonomy tables, FASTA, sample metadata, and existing diversity results

Discuss this field
05 / 07

Imaging & cellular phenotypes

Quantify regions, morphology, intensity, localization, and phenotype differences in 2D, Cell Painting, 3D, and imaging-flow data.

Standard analysis options

  • 2D image QC, background/illumination correction, and ROI/object segmentation
  • Morphology, intensity, texture, colocalization, and group comparison
  • Cell Painting plate QC, cellular profiles, normalization, and batch review
  • 3D segmentation, volume, surface area, shape, and distance
  • Imaging-flow focus/singlet QC, gating, population ratios, and translocation

Custom design / additional quote

  • Custom object definitions, features, and threshold optimization
  • Integration across plates, channels, and acquisition settings
  • Phenotype clustering, similarity, and candidate prioritization
  • Re-evaluation of existing annotations or classifier outputs

Typical input formatsTIFF/OME-TIFF, OME-Zarr, CZI, Z-stacks, plate maps, ROIs, and analyzable ImageStream data

Discuss this field
06 / 07

Proteomics & molecular structure

Review protein abundance and molecular-structure differences through quality, statistics, function, and interactions.

Standard analysis options

  • Filtering, missingness review, and normalization of protein/peptide tables
  • PCA, clustering, differential proteins, FDR, and multiple testing
  • GO, pathway, and functional-category analysis
  • Structure QC, domains, alignment, RMSD, surfaces/pockets, and residue contacts

Custom design / additional quote

  • Database search and identification from RAW/mzML
  • Comparative protein-ligand or protein-protein interaction review
  • Small-scale comparison of multiple structures or candidate compounds
  • Integrated review with expression or imaging results

Typical input formatsmzML, protein/peptide tables, PDB/mmCIF, SDF/MOL2, FASTA, and structure predictions

Discuss this field
07 / 07

Integration, re-analysis & result review

Combine legacy tables, figures, logs, and new data to clarify reproducibility, condition differences, and next validation steps.

Standard analysis options

  • Inventory of existing tables, figures, analysis logs, and metadata
  • Condition/sample-ID harmonization, re-statistics, and figure regeneration
  • Cross-modality mapping and integrated review
  • Report covering methods, limitations, and follow-up candidates

Custom design / additional quote

  • Comparison with public datasets or prior studies
  • Feasibility of custom comparison designs or statistical models
  • IAS workspace/export design for collaborative review
  • Reconstruction of legacy analyses that cannot be handed over

Typical input formatsCSV/TSV, spreadsheet exports, reports, figures, notebooks/logs, metadata, and processed modality data

Discuss this field

Custom items are accepted only after reviewing data quality, scale, references, and usage conditions. We confirm feasibility and additional cost first. Measurement, imaging acquisition, and library preparation are excluded.

SCIENTIFIC CASE FILES

10 analysis designs built backward from the scientific figure

Each file connects the research question, input data, workflow, visual readout and next decision so you can see what to request and what a review-ready delivery looks like.

10scientific cases

CASE FILE 01RNA-seq / transcriptomics

Resolve a transcriptional signature of drug response

Research question

Which genes and pathways distinguish responders from non-responders well enough to justify a validation panel?

Illustrative simulationEffect size, FDR and sample separation in one decision viewIllustrative volcano plot and expression heatmap− log2FC +−log10(FDR)signature heatmapresponsenon-response
Effect size, FDR and sample separation in one decision view
DEG candidates
1,842
priority pathways
6
validation genes
12
Comparison design
Two groups with six samples each; batch and baseline values are modeled as covariates.
Input data
FASTQ, gene-count matrix, sample sheet, group, batch and covariates

Analysis workflow

  1. 1

    Read and mapping QC

  2. 2

    Covariate-aware differential expression with FDR control

  3. 3

    GSEA, heatmap and candidate prioritization

What the figure resolves

A volcano plot combines effect size and significance, while the heatmap tests whether the candidate signature separates samples consistently.

Next research decision

Select a 12-gene panel for qPCR, perturbation or independent-cohort validation.

Example deliverables

  • QC report
  • DEG table
  • Volcano / heatmap
  • Pathway table
  • Re-runnable code
Discuss this design
CASE FILE 02Single-cell states

Identify treatment-linked cell states and trajectory branches

Research question

Can a rare population and its post-treatment transition be resolved beyond bulk averages?

Illustrative simulationCross-check cell type, subject and time point along the trajectoryIllustrative UMAP with cell clusters and pseudotime pathsUMAP 1UMAP 2state trajectory
Cross-check cell type, subject and time point along the trajectory
cell states
6
branches
2
candidate markers
14
Comparison design
Eight pre/post samples from four subjects, integrated while preserving subject-level variation.
Input data
H5AD / Seurat object, 10x matrix and cell, subject and time-point metadata

Analysis workflow

  1. 1

    Doublet, low-quality-cell and ambient-RNA QC

  2. 2

    Integration, clustering and marker-guided annotation

  3. 3

    Differential abundance, pseudotime and interaction candidates

What the figure resolves

Six states and two trajectory branches are reviewed by subject to determine whether the rare population concentrates at a reproducible endpoint.

Next research decision

Define the population and markers for an expanded staining panel, sorting or functional validation.

Example deliverables

  • QC dashboard
  • Annotated H5AD
  • UMAP / abundance plots
  • Marker table
  • Trajectory plot
Discuss this design
CASE FILE 03Microbiome

Separate intervention effects on abundance, diversity and community structure

Research question

Which microbial groups and community-level shifts respond beyond a compositional artifact?

Illustrative simulationKeep relative abundance, absolute load and diversity distinctIllustrative stacked microbiome bars and diversity trendcommunity compositionprepostpaired diversityprepostq=.018
Keep relative abundance, absolute load and diversity distinct
response genera
5
Shannon change
+0.63
community shift
q=.018
Comparison design
Forty paired pre/post samples from 20 participants, with intake and collection date included in the model.
Input data
ASV / OTU table, taxonomy, absolute load when available and sample metadata

Analysis workflow

  1. 1

    Depth, contamination and prevalence QC

  2. 2

    Alpha/beta diversity and paired statistical models

  3. 3

    Differential taxa, co-occurrence and phenotype association

What the figure resolves

Stacked abundance, paired diversity change and PCoA movement are reviewed together for a consistent biological interpretation.

Next research decision

Advance five reproducible genera to qPCR, culture or metabolite follow-up.

Example deliverables

  • Composition plots
  • Diversity plots
  • PCoA
  • Differential-taxa table
  • Method record
Discuss this design
CASE FILE 04Spatial omics

Overlay morphology and expression to resolve boundary niches

Research question

How do cell composition and pathway activity differ across core, invasive edge and stroma?

Illustrative simulationCompare morphology, coordinates and molecular values in one fieldIllustrative tissue section with spatial expression and niche overlaysmolecular boundary
Compare morphology, coordinates and molecular values in one field
spatial niches
3
boundary zones
2
priority pathways
4
Comparison design
Morphology, spot/cell expression, coordinates and region annotations from the same section are integrated.
Input data
H&E / IF image, spatial matrix, coordinates, segmentation and region annotation

Analysis workflow

  1. 1

    Image-coordinate-expression registration QC

  2. 2

    Spatial domains, deconvolution and regional comparison

  3. 3

    Neighborhood, colocalization and spatial pathway analysis

What the figure resolves

Colored spots and local statistics reveal where morphological and molecular boundaries agree or diverge.

Next research decision

Choose regions and markers for additional IF, ROI profiling or boundary-focused validation.

Example deliverables

  • Registration QC
  • Spatial feature maps
  • Niche plots
  • Regional tables
  • Publication SVG
Discuss this design
CASE FILE 052D / 3D imaging

Quantify single-cell morphology and estimate a dose response

Research question

Can morphology expose a drug response that viability alone misses?

Illustrative simulationTrace image QC through single-cell features to concentration responseIllustrative cell segmentation and dose-response curvesegmentation + phenotype featuresEC50log dose
Trace image QC through single-cell features to concentration response
quantified cells
12,480
stable features
9
estimated EC50
38 nM
Comparison design
Eight concentrations with three replicates; wells and image fields are modeled hierarchically.
Input data
TIFF / OME-TIFF, channel definitions, well map, concentration and replicate metadata

Analysis workflow

  1. 1

    Illumination and focus QC with nuclear/cell/organelle segmentation

  2. 2

    Morphology, texture, intensity and neighborhood features

  3. 3

    Single-cell distributions, mixed models and dose-response fitting

What the figure resolves

Segmentation overlays document object quality while feature distributions and EC50 remain traceable in the same report.

Next research decision

Select confirmatory concentrations, morphology features and live-cell imaging conditions.

Example deliverables

  • Segmentation overlays
  • Cell-level CSV
  • Feature QC
  • Dose-response plots
  • Reproduction steps
Discuss this design
CASE FILE 06Imaging flow cytometry

Resolve rare events with image-backed phenotype evidence

Research question

Is a low-frequency cluster a reproducible cell phenotype rather than an analysis artifact?

Illustrative simulationReturn statistical clusters to source images for biological reviewIllustrative flow-cytometry gate and rare-event clusterrare gate0.74% eventsmorphology scoretranslocation index
Return statistical clusters to source images for biological review
QC-passed events
186k
rare population
2.3%
explanatory markers
4
Comparison design
Three control and three stimulated replicates with a fixed compensation, focus, singlet and morphology QC hierarchy.
Input data
FCS / CIF, compensation matrix, image channels, group and replicate metadata

Analysis workflow

  1. 1

    Compensation, focus, singlet and debris QC

  2. 2

    Cross-check manual gates with UMAP/clusters

  3. 3

    Rare-population abundance, image montage and marker statistics

What the figure resolves

Events inside the density gate are linked back to source images to verify morphology and marker consistency.

Next research decision

Lock the population definition for added antibodies, sorting or functional assays.

Example deliverables

  • Gate hierarchy
  • UMAP
  • Population proportions
  • Image montage
  • Annotated FCS
Discuss this design
CASE FILE 07Proteomics

Prioritize reproducible candidates from a high-dimensional protein panel

Research question

Which candidates survive covariates, missingness and pathway-level consistency checks?

Illustrative simulationPair single-protein significance with pathway-level coherenceIllustrative proteomics volcano plot and candidate moduleeffect sizecandidate moduleabundance → pathway → panel
Pair single-protein significance with pathway-level coherence
differential proteins
32
network modules
5
confirmation targets
8
Comparison design
Two groups of 24 samples with age, sex and batch as covariates; discovery and confirmation are kept separate.
Input data
NPX / intensity table, LOD and QC flags, sample metadata and panel annotation

Analysis workflow

  1. 1

    Missingness, LOD, sample and distribution QC

  2. 2

    Normalization and covariate-aware testing with FDR control

  3. 3

    Pathway, protein-network and robustness review

What the figure resolves

Volcano candidates are checked against network modules to separate isolated changes from coherent pathway effects.

Next research decision

Choose eight proteins for orthogonal confirmation and the next panel design.

Example deliverables

  • QC report
  • Statistical table
  • Volcano plot
  • Network
  • Ranked candidates
Discuss this design
CASE FILE 08Epigenome / ChIP / ATAC

Connect differential peaks to regulatory programs and expression

Research question

Which transcription factors and target genes explain condition-specific chromatin changes?

Illustrative simulationConnect peaks, motifs, nearby genes and expression in genomic contextIllustrative genome tracks and condition-specific peaksATACH3K27acRNAcandidate target gene
Connect peaks, motifs, nearby genes and expression in genomic context
reproducible peaks
8,420
priority motifs
11
enhancer candidates
3
Comparison design
Three replicates per condition with input/IgG controls and FRiP/TSS enrichment used as quality gates.
Input data
BAM / bigWig, peak files, input controls, RNA-seq results and genome annotation

Analysis workflow

  1. 1

    Mapping, duplicate, FRiP, TSS and reproducibility QC

  2. 2

    Peak calling, consensus peaks and differential accessibility/binding

  3. 3

    Motif, peak-gene and expression integration

What the figure resolves

Genome tracks expose reproducible peaks and condition effects, retaining regulatory candidates whose motif and expression directions agree.

Next research decision

Select enhancer candidates for ChIP-qPCR, reporter assays or CRISPRi.

Example deliverables

  • QC metrics
  • Peak set
  • bigWig
  • Motif table
  • Peak-gene link plots
Discuss this design
CASE FILE 09Multi-omics integration

Integrate RNA, protein and metabolites into shared response modules

Research question

Can changes across separate layers be explained by common biological axes?

Illustrative simulationRetain reproducible cross-modality links after sample matchingIllustrative network connecting RNA, protein and metabolitesRNAproteinphenotypecross-validated response model
Retain reproducible cross-modality links after sample matching
shared modules
3
cross-layer links
21
response axes
2
Comparison design
Three modalities measured on matched subjects; sample IDs, time points, missingness and batches are locked before modeling.
Input data
RNA counts, protein abundance, metabolite table, sample map and phenotype

Analysis workflow

  1. 1

    Modality-specific QC, normalization and sample matching

  2. 2

    Latent-factor, correlation and pathway integration

  3. 3

    Module-phenotype association and leave-one-out robustness

What the figure resolves

Cross-layer edges are condensed into factors and pathways, reducing correlations driven by only one modality.

Next research decision

Define a minimal follow-up marker panel and the modules to test mechanistically.

Example deliverables

  • Sample concordance table
  • Integrated factor plots
  • Network
  • Pathway table
  • Marker shortlist
Discuss this design
CASE FILE 10Molecular structure / in silico

Visualize binding modes and prioritize compounds beyond a score

Research question

Can candidates be ranked using explainable residue contacts and binding poses, not only a docking score?

Illustrative simulationReview pose and residue contacts alongside the composite scoreIllustrative ligand bound within a protein pocketpocket + interaction mapC182C268C351C437priority score
Review pose and residue contacts alongside the composite score
priority poses
5
key contacts
7
candidate sites
2
Comparison design
Target and compound structures are standardized; known ligands and decoys provide internal references.
Input data
PDB / mmCIF, SDF / SMILES, activity table, candidate sites and known interactions

Analysis workflow

  1. 1

    Structure, protonation, ligand and pocket preparation

  2. 2

    Docking/rescoring, pose clustering and interaction fingerprints

  3. 3

    Known-activity concordance, selectivity risk and optional MD review

What the figure resolves

Top poses are compared inside the pocket with hydrogen bonds, hydrophobic contacts, clashes and site selectivity shown explicitly.

Next research decision

Select five compounds for synthesis, purchase or biophysical assays and define substituent directions.

Example deliverables

  • Pose files
  • Interaction map
  • Score table
  • Pocket figures
  • Rank and constraints
Discuss this design

Illustrative simulationValues and patterns are simulated to explain the analysis design. They are not customer data, clinical outcomes or performance guarantees. Methods, thresholds and deliverables are agreed for the actual data quality and research objective.

REQUEST EXAMPLES

Fifteen concrete examples to shape your request

Start from the decision you need to make, then trace the expected inputs, workflow, and deliverables. Selecting a similar example opens the consultation form with a structured prompt.

15request examples

CASE 01Transcriptomics

Prioritize genes and pathways altered by treatment

Typical questionEvaluate control-versus-treatment differences while accounting for batch and biological replication.

Inputs reviewed at consultation
FASTQ or count matrix, sample sheet with groups/replicates/batches, and reference information
Illustrative workflow
Read/count QC → normalization → PCA/clustering → differential expression → GO/pathways
Illustrative deliverables
QC summary, expression tables, volcano/heatmap figures, candidate genes/pathways, and report
Discuss a similar request
CASE 02Epigenomics

Compare transcription-factor or histone-mark binding regions

Typical questionIdentify reproducible peaks and linked genes while respecting input/control and replicate design.

Inputs reviewed at consultation
ChIP-seq FASTQ/BAM, input/control map, sample metadata, and reference genome
Illustrative workflow
Library QC → mapping → peak calling → annotation → browser tracks → condition comparison
Illustrative deliverables
QC metrics, peak and annotation tables, tracks, comparison figures, and follow-up options
Discuss a similar request
CASE 03Genomics & variants

Organize research variant candidates from WES data

Typical questionReview coverage and filtering and create a prioritized table that includes existing candidates.

Inputs reviewed at consultation
FASTQ/BAM/VCF, sample and phenotype metadata, candidate genes, reference and annotation
Illustrative workflow
QC/coverage → calling or VCF review → filtering → annotation → cross-sample comparison
Illustrative deliverables
Coverage summary, annotated variant table, filter history, candidate review table, and caveats
Discuss a similar request
CASE 04Genomics & variants

Recheck an existing tumor-normal analysis for research use

Typical questionAudit pairing, depth, and existing filters, then define which parts require re-analysis.

Inputs reviewed at consultation
Paired BAM/VCF, sample map, legacy reports, and pipeline information
Illustrative workflow
Pair/QC review → coverage/artifact review → variant comparison → annotation → delta summary
Illustrative deliverables
Audit results, condition differences, candidate table, and proposed re-analysis scope; no diagnosis
Discuss a similar request
CASE 05Single-cell

Compare cell populations and marker candidates in tissue

Typical questionReview sample quality and batch effects and explore clusters that change by condition.

Inputs reviewed at consultation
H5AD or matrix/barcodes/features with sample, condition, and batch metadata
Illustrative workflow
Cell QC/doublets → normalization/integration → UMAP/clusters → markers/cell-type review
Illustrative deliverables
QC table, UMAP, cluster proportions, marker table, cell-type review, and report
Discuss a similar request
CASE 06Spatial omics

Integrate molecular differences across tissue regions and neighborhoods

Typical questionAlign tissue images, coordinates, expression, and proteins to compare regions and neighborhoods.

Inputs reviewed at consultation
H5AD/matrix, spatial coordinates, tissue image, feature table, ROIs, and sample metadata
Illustrative workflow
Spot/cell/image QC → spatial clusters → cell-type review → region comparison → neighborhoods/cross-modality
Illustrative deliverables
Spatial maps, regional markers, neighborhood/colocalization plots, integrated figures, and report
Discuss a similar request
CASE 07Microbiome

Test whether community composition changed after an intervention

Typical questionEvaluate diversity and candidate taxa while preserving paired design and batch information.

Inputs reviewed at consultation
16S/18S/ITS FASTQ, primers, and metadata with group, time point, subject ID, and batch
Illustrative workflow
Trim/denoise/chimera → ASV/taxonomy → alpha/beta diversity → paired comparison
Illustrative deliverables
QC, ASV/taxonomy tables, diversity and composition plots, candidates, and methods
Discuss a similar request
CASE 08Metagenomics

Compare microbial composition and function across fermentation conditions

Typical questionReview host reads and depth, then organize differences in taxa and functional profiles.

Inputs reviewed at consultation
Shotgun FASTQ, metadata with culture condition/batch/time, and host reference
Illustrative workflow
QC/host removal → taxonomy → diversity → functional profiles → condition comparison
Illustrative deliverables
QC, composition/function tables, ordination, candidate pathways, figures, and report
Discuss a similar request
CASE 092D imaging

Quantify morphology and intensity changes after compound treatment

Typical questionMeasure cell-level morphology and intensity reproducibly under consistent acquisition settings.

Inputs reviewed at consultation
TIFF/OME-TIFF, channels, pixel size, ROIs, groups, doses, and replicates
Illustrative workflow
Image QC/correction → segmentation → feature measurement → segmentation QC → comparison
Illustrative deliverables
Overlay images, object table, morphology/intensity plots, QC, and parameter report
Discuss a similar request
CASE 10Cell Painting

Compare compound phenotype profiles from multichannel images

Typical questionAccount for plate/well/field structure and batch while exploring similar phenotypes and outliers.

Inputs reviewed at consultation
Multichannel images, plate map, well/field/channel map, treatment conditions, and batch metadata
Illustrative workflow
Plate QC → illumination correction → compartment segmentation → feature QC/normalization → UMAP/clusters
Illustrative deliverables
Cell profiles, QC, UMAP, similarity/cluster tables, representative images, and report
Discuss a similar request
CASE 113D imaging

Compare organoid volume, shape, and internal structure

Typical questionPreserve voxel information while comparing 3D object volume, surfaces, and spatial relationships.

Inputs reviewed at consultation
Z-stacks, 3D TIFF/OME-Zarr/CZI, voxel size, channels, ROIs, and groups
Illustrative workflow
3D QC → segmentation → object/surface measures → distance/colocalization → comparison
Illustrative deliverables
3D masks, object tables, section/3D figures, comparison plots, and limitations report
Discuss a similar request
CASE 12Imaging flow cytometry

Evaluate nuclear translocation and population ratios after stimulation

Typical questionReview focus, singlets, and compensation while combining image features with gating.

Inputs reviewed at consultation
Analyzable ImageStream data, compensation matrix, marker/channel map, and groups
Illustrative workflow
Data/focus/singlet QC → compensation review → gating → translocation/morphology features → comparison
Illustrative deliverables
Gating history, population ratios, feature table, representative images, figures, and report
Discuss a similar request
CASE 13Proteomics

Find proteins and pathways that change between conditions

Typical questionReview missingness, batch, and replication and identify candidates with FDR control.

Inputs reviewed at consultation
Protein/peptide quantification table or mzML with sample, condition, and batch metadata
Illustrative workflow
QC/filtering → missingness/normalization → PCA/clusters → differential/FDR → pathways
Illustrative deliverables
QC, normalized table, candidate proteins, volcano/heatmap figures, pathways, and report
Discuss a similar request
CASE 14Molecular structure

Compare structural and binding-site differences between mutant and wild type

Typical questionReview confidence and illustrate changes in alignment, pockets, and residue contacts.

Inputs reviewed at consultation
PDB/mmCIF or predicted structures, FASTA, ligand information, comparators, and objective
Illustrative workflow
Structure QC → domains/secondary structure → alignment/RMSD → surface/pockets → contacts
Illustrative deliverables
Annotated 3D figures, RMSD/contact tables, pocket figures, caveats, and review report
Discuss a similar request
CASE 15Integration & re-analysis

Combine legacy results and new data into one review package

Typical questionInventory outputs with inconsistent IDs or methods and determine what can be compared.

Inputs reviewed at consultation
Existing CSV/TSV, figures, reports, logs, metadata, and newly processed data
Illustrative workflow
Asset inventory → ID/method harmonization → re-statistics/figures → modality mapping → delta review
Illustrative deliverables
Integrated workspace/export, method-difference table, regenerated figures, limits, and next checks
Discuss a similar request

These are illustrative request-design scenarios, not identified customer projects, performance claims, or guarantees of a particular result. The final workflow and deliverables are agreed after data review.

CONSULTATION CHECKLIST

Start a consultation with four items, even without an analysis name

Share only a data overview first. We check feasibility, required metadata, and a secure transfer method before receiving any data files.

  1. 1. Research objectiveWhat should be compared, and what decision or next experiment should the result inform? An incomplete hypothesis is acceptable.
  2. 2. Available dataFormat, instrument or pipeline, reference, volume, and whether the data are raw or processed. Do not send the files yet.
  3. 3. Sample designSample count, groups, replicates, time points, pairing, batches, and known covariates, as far as available.
  4. 4. Expected conditionsRequired tables, figures and report, timing, budget, pseudonymization, retention, and collaboration format.
Enter the four items

IAS contract analysis menu

We confirm study design, sample context, and references before selecting a standard or custom workflow.

01

RNA-seq and expression analysis

Connect expression, group differences, and candidate genes to research review.

Typical inputs
FASTQ, BAM, count matrix, sample attributes, reference genome details
Typical outputs
QC, alignment summary, expression table, PCA/clusters, and design-dependent differential-expression candidates
02

DNA and variant analysis

Organize sequence QC, variant candidates, and annotations in one review unit.

Typical inputs
FASTQ, BAM/CRAM, VCF, reference genome, sample and phenotype context
Typical outputs
QC, mapping summary, variant candidates, annotations, and cross-sample comparison
03

Single-cell analysis

Visualize cell populations, marker candidates, and sample differences.

Typical inputs
H5AD, matrices, barcodes, features, and sample attributes
Typical outputs
QC, normalization, PCA, clustering, UMAP, marker candidates, and cell-type review tables
04

Metagenomics and amplicon analysis

Organize microbial composition and group-level patterns with documented conditions.

Typical inputs
FASTQ, feature tables, taxonomy, and sample attributes
Typical outputs
QC, composition tables, alpha/beta diversity, visualizations, and comparison candidates
05

2D image analysis

Quantify regions, objects, morphology, and intensity in existing microscopy, cell, or pathology images.

Typical inputs
TIFF, OME-TIFF, CZI and related images, acquisition details, ROIs, group information, and existing annotations
Typical outputs
Image QC, region extraction, segmentation, morphology and intensity measurements, comparisons, and visualizations
06

Cell Painting analysis

Derive cell-level morphology profiles and phenotype differences from existing multichannel Cell Painting images.

Typical inputs
Multichannel TIFF or OME-TIFF images, plate maps, well/field/channel mappings, treatment conditions, and batch metadata
Typical outputs
Plate and image QC, illumination correction, nucleus/cell/cytoplasm segmentation, morphology/intensity/texture features, feature QC and normalization, batch-effect review, PCA/UMAP/clustering, and phenotype comparison tables
07

3D image structural analysis

Quantify 3D structure, volume, surface, shape, and spatial relationships in existing Z-stacks and volumetric microscopy images.

Typical inputs
Z-stacks, 3D TIFF, OME-TIFF, OME-Zarr or CZI, voxel size, channel metadata, ROIs, and group information
Typical outputs
3D image QC, 3D segmentation, object counts, volume, surface area and shape, distance and colocalization, comparisons, and orthogonal or 3D visualization
08

Imaging flow cytometry analysis

Quantify cell populations and image-derived features together from existing imaging flow cytometry data.

Typical inputs
Analysis-ready ImageStream or equivalent data, compensation matrix, channel/marker map, group information, and analysis objective
Typical outputs
Data, focus and singlet QC; compensation review; gating; population frequencies; morphology, intensity, texture, localization, colocalization and translocation features; comparisons; representative images and visualizations
09

Molecular structure analysis

Review structure quality, similarity, binding sites, and molecular interactions in existing or predicted molecular structures.

Typical inputs
PDB/mmCIF, SDF/MOL2, FASTA, structure-prediction outputs, molecule or ligand information, reference structures, and analysis objective
Typical outputs
Structure QC and confidence review, domains and secondary structure, superposition and RMSD, surfaces and pockets, residue contacts, protein–ligand or protein–protein interactions, annotated 3D figures and report
10

Proteomics analysis

Organize existing mass-spectrometry data or quantitative tables from QC through comparison and functional interpretation.

Typical inputs
Analysis-ready mass-spectrometry data such as mzML, peptide/protein intensity tables, and sample attributes
Typical outputs
QC, identification and quantification review, normalization, PCA/clusters, differential candidates, and pathway analysis
11

Spatial multi-omics analysis

Integrate spatial coordinates, tissue images, expression, protein, and other feature layers for review.

Typical inputs
H5AD, matrices, spatial coordinates, tissue images, gene/protein feature tables, and sample attributes
Typical outputs
QC, spatial clusters, region comparison, marker candidates, cross-modality mapping, and integrated visualization
12

Reanalysis and integrated review

Bring existing tables and figures into IAS for reproducibility and decision-focused review.

Typical inputs
CSV/TSV, reports, analysis logs, metadata, and related modality outputs
Typical outputs
Reanalysis results, comparison figures, condition differences, an integrated review workspace, and report

PRICE POLICY

At least 44% below analysis-only market references

We normalized publicly listed analysis-only prices for equivalent scopes and set IAS starting prices at no more than 56% of those market references. Specimen handling, image acquisition, mass-spectrometry measurement, spatial-omics data generation, library preparation, and sequencing are excluded from both the price and service scope.

IAS contract data-analysis reference prices

The market references use pre-tax public prices rechecked on July 25, 2026. Every displayed amount is a pre-tax starting price per specimen. Provider names and external price pages are intentionally omitted from the public page. A formal quote depends on input quality, data volume, accepted specimen count, study design, and deliverables.

44% OFF

Basic RNA-seq analysis

FASTQ QC, trimming, alignment or pseudoalignment, quantification, normalization, PCA, differential expression, GO and pathway analysis

Market reference
$92
LifeAnalytics price
$52before tax · USD

per specimen; two groups with ≥3 replicates recommended

Updated to the current domestic public analysis-only rate. Differential expression is omitted when the comparison design is not valid.

44% OFF

Basic DNA variant analysis

QC, mapping, duplicate and coverage review, SNV/indel calling, filtering, and basic annotation for human germline WGS/WES

Market reference
$92
LifeAnalytics price
$52before tax · USD

per specimen, basic analysis

CNV/SV, tumor-normal, family analysis, and non-model organisms are quoted as separate workflows.

44% OFF

Basic ChIP-seq analysis

FASTQ QC, mapping, duplicate/library-complexity and FRiP review, input normalization, peak calling and annotation, browser tracks

Market reference
$166
LifeAnalytics price
$93before tax · USD

per specimen (one library), basic analysis

Motif, IDR, and differential-peak analysis require controls and replicates and are quoted separately.

44% OFF

Basic shotgun metagenome analysis

FASTQ QC, host-read removal, taxonomy and composition, alpha/beta diversity, ordination, and basic functional profiling

Market reference
$185
LifeAnalytics price
$103before tax · USD

per specimen, basic analysis

De novo assembly, MAG/binning, resistome, detailed pathways, and group statistics are separate workflows.

44% OFF

Basic 16S, 18S, or ITS amplicon analysis

Primer trimming, denoising, chimera removal, ASV/OTU, taxonomy, composition, alpha/beta diversity, and ordination

Market reference
$12
LifeAnalytics price
$7before tax · USD

per specimen in one analysis batch

This is a high-throughput batch rate. At least three biological replicates per group are recommended; sample and comparison counts are confirmed first.

44% OFF

Basic single-cell RNA-seq analysis

Cell QC, doublet review, normalization, batch integration, PCA/UMAP, clustering, markers, and initial cell-type annotation

Market reference
$862
LifeAnalytics price
$483before tax · USD

per specimen; standard projects start at 4 specimens

Two conditions with at least two replicates are the standard project. FASTQ primary processing, trajectory, communication, and detailed condition analysis are separate.

44% OFF

Basic 2D image analysis

Image QC, background or illumination correction, ROI/object segmentation, segmentation QC, morphology/intensity quantification, basic statistics and visualization

Market reference
$308
LifeAnalytics price
$172before tax · USD

per specimen (up to 4 standard images under one acquisition condition)

Up to four standard 2D images acquired under one condition are counted as one specimen. Cell Painting is quoted after reviewing wells, fields, channels, plates, and workflow; 3D image structural analysis is quoted after reviewing z-slices, voxels, channels, total data volume, and workflow.

44% OFF

Basic imaging flow cytometry analysis

Data, focus and singlet QC; compensation review; standard gating; population frequencies; morphology, intensity, texture, localization, colocalization and translocation features; representative images and basic comparisons

Market reference
$493
LifeAnalytics price
$276before tax · USD

per specimen (one acquisition file, up to 12 standard channels)

This is analysis of existing data. Acquisition, staining, panel design, compensation-control generation, multi-file integration, high-dimensional analysis, and new classifier development are quoted separately.

44% OFF

Basic molecular structure analysis

Structure QC and confidence review, domains and secondary structure, reference superposition and RMSD, surfaces and pockets, residue contacts, protein–ligand or protein–protein interactions, and annotated 3D figures

Market reference
$739
LifeAnalytics price
$414before tax · USD

per specimen (one molecule or complex; one standard structure set)

This covers an existing or predicted structure. Experimental structure determination, cryo-EM reconstruction, long molecular-dynamics runs, free-energy calculations, large virtual screens, and new model development are quoted separately.

44% OFF

Basic proteomics analysis

Quantitative-table filtering and missingness review, normalization, PCA/clustering, differential proteins, FDR/multiple testing, and functional/pathway analysis

Market reference
$277
LifeAnalytics price
$155before tax · USD

per specimen (one batch, analysis-ready quantitative table)

This is a per-specimen rate within one analysis batch. Differential proteins, FDR, and pathway analysis require a valid comparison cohort. Database search, identification, and contaminant handling from RAW/mzML are separate.

44% OFF

Full spatial multi-omics integration package

Input, metadata, spot, cell and image QC; normalization; dimension reduction; spatial domains and clustering; cell-type review; regional or condition-level RNA/protein comparison; integration with image registration or segmentation; neighborhood, colocalization, pathway and cross-modality visualization; interpretation report and walkthrough

Market reference
$3,696
LifeAnalytics price
$2,056before tax · USD

per specimen (one spatial-omics, microscopy-image and NGS data set)

This is a full integration package for existing data from one specimen. Measurement, image acquisition, library preparation, sequencing, large-scale manual annotation, new model development, and data volume or comparisons beyond the standard scope are quoted separately.

44% OFF

44% OFF reference calculator

For custom work not listed above, enter a pre-tax quote for an equivalent analysis-only scope.

Market reference × 56%
LifeAnalytics IAS reference price$34444% OFF
  • Basic-menu amounts are converted from JPY, while the fixed spatial plan uses its published USD base. Conversions use the ECB reference rates of July 17, 2026; the contract currency is confirmed in the formal quote.
  • Every displayed amount is per specimen. Formal pricing starts from the per-specimen rate multiplied by the accepted specimen count and assumes a common batch or standard comparison project. Biological replicates are required for comparative analysis.
  • Foreign-currency source prices were normalized using the ECB reference rates published on July 17, 2026 and rounded before applying 56%.
  • Tax, external licenses, specialist databases, media, extended storage, and out-of-scope reanalysis are separate.

Conditions for the 56% comparison

  1. The comparison quote must cover data analysis only, without specimen handling or sequencing
  2. Input format, data volume, samples, references, and analysis steps must be equivalent
  3. Deliverables, turnaround, storage, and security terms must be equivalent
  4. For a reference outside the standard menu, an equivalent analysis specification or quote must be reviewable; unrelated confidential fields may be redacted

From inquiry to delivery

We confirm feasibility and a safe transfer method before any data is sent.

  1. 01

    Inquiry

    Share the research objective, data format, sample design, and expected outputs.

  2. 02

    Preflight

    Review volume, quality information, references, comparisons, and confidentiality.

  3. 03

    Scope and quote

    Agree on steps, turnaround, deliverables, price, and retention period.

  4. 04

    Secure transfer

    Transfer only the required data and metadata using the agreed method.

  5. 05

    IAS analysis and review

    Run QC, standard analysis, visualization, and human review.

  6. 06

    Delivery

    Deliver results, conditions, and limitations with an optional walkthrough.

Standard deliverables

Outputs are organized for reproducible review according to the agreed scope.

  • Input-data and basic-QC summary
  • Analysis conditions, references, and key parameters
  • Result tables such as CSV/TSV and review figures
  • Report covering key findings, limitations, and follow-up candidates
  • Agreed IAS review outputs or exports

Frequently asked questions

Can I send a specimen and request data generation?

No. LifeAnalytics currently provides analysis of existing data. Extraction, library preparation, sequencing, microscopy acquisition, mass-spectrometry measurement, and spatial-omics data generation are outside scope.

Why is the reference price not a fixed public fee?

Standard menus start at no more than 56% of a comparable public analysis-only price. Data volume, quality, sample design, analysis steps, and deliverables still change the workload, so we provide a formal quote after preflight review.

Which formats can I discuss?

FASTQ, BAM/CRAM, VCF, H5AD, count matrices, CSV/TSV, TIFF, OME-TIFF, OME-Zarr or CZI 2D/3D images, Cell Painting images, analysis-ready ImageStream or equivalent data, PDB/mmCIF/SDF/MOL2 molecular structures, mzML or peptide/protein intensity tables, spatial coordinates, and tissue images can be reviewed. Feasibility depends on the objective, quality, and available references.

Can you analyze Cell Painting and 3D microscopy images?

Yes. For existing multichannel Cell Painting images, we can provide segmentation, morphology profiling, normalization, batch-effect review, and phenotype comparison. For Z-stacks and other 3D images, we support 3D segmentation, volume, surface, shape, distance, colocalization, and 3D visualization. Image acquisition is excluded; plate maps, voxel size, channel metadata, and data volume are reviewed before a project quote.

Can you analyze imaging flow cytometry and molecular structures?

Yes. For existing imaging flow cytometry data, we support focus and singlet QC, gating, morphology, intensity, localization, colocalization, and translocation analysis. For PDB/mmCIF and related structures, we review structure QC, superposition, pockets, residue contacts, and molecular interactions. Data acquisition and experimental structure determination are excluded.

What is the turnaround time?

It depends on data volume, samples, workflow, and QC. The quote states prerequisites and an estimated turnaround after preflight.

Can you analyze clinical data?

This service is research-use-only. Data that do not satisfy privacy, ethics, contract, pseudonymization, and storage requirements cannot be accepted. We do not make diagnostic or treatment decisions.

Start with the data format and research objective

A comparison quote is not required for the first discussion. Share the format, sample count, objective, and expected outputs.

Discuss contract data analysis
IAS Contract Data Analysis | NGS, Imaging, Omics and Molecular Structure