Transcriptomics Analysis Services — Version-Pinned Workflows Across Bulk, Single-Cell, Spatial, and Long-Read Data

Key facts

What is transcriptomics?

Transcriptomics is the genome-wide measurement of RNA—messenger RNA (mRNA), long non-coding RNA, and other transcript classes—to quantify which genes are expressed, at what level, and in which cells or tissue regions. Unlike targeted assays such as qPCR, RNA sequencing (RNA-seq) captures novel splice junctions and unannotated transcripts when depth permits (Conesa et al., 2016). The core question is which transcripts differ between conditions, cell types, or spatial domains, and what pathways they implicate. Adoption is substantial: GTEx v8 profiled 17,382 RNA-seq samples across 52 tissues and two cell lines from 948 donors (GTEx Consortium, 2020).

What transcriptomics analysis can answer

Published examples of biological questions transcriptomics can address:

• Which genes differ between disease and control tissue? Sun et al. (2019) analyzed TCGA bulk RNA-seq from 103 primary and 368 metastatic melanoma samples, identifying 246 differentially expressed lncRNAs and 856 mRNAs between stages.
• Which T cell populations expand during anti-PD-1 therapy in lung cancer? Liu et al. (2022) profiled 47 NSCLC biopsies from 36 patients before and after PD-1 therapy, showing precursor exhausted T cells accumulate in responders through clonal revival rather than reinvigoration of terminally exhausted cells.
• How is gene expression organized across tissue architecture? Arora et al. (2023) applied spatial transcriptomics to oral squamous cell carcinoma, identifying conserved tumor core and leading-edge domains linked to survival and therapy response.
• Which full-length isoforms switch between conditions? Amarasinghe et al. (2020) reviewed long-read RNA-seq applications showing that full-length reads resolve isoform structures that short-read fragments cannot reconstruct reliably.
• Which genetic variants alter tissue-specific expression? GTEx v8 linked cis-expression quantitative trait loci (eQTLs) across 52 tissues and two cell lines, enabling tissue-context interpretation of regulatory variants (GTEx Consortium, 2020).

Services included in this category

Pepkio's transcriptomics category covers bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and long-read RNA-seq—each with a dedicated spoke page for inputs, tools, and deliverables.

What Pepkio delivers

Pepkio returns reproducible, analysis-ready outputs—not summary slides alone:

Non-standard contrasts, cross-modality integration, or client-specified figures are scoped at kickoff.

How the analysis works — step by step

1. 1. Scope study design and select modality

   Confirm biological question, replicates, platform, and modality; flag confounded batch or modality mismatch before analysis (Conesa et al., 2016; Luecken & Theis, 2019).

   Tools and outputs

   Output: signed scope with contrasts

2. 2. Validate inputs and record metadata

   Verify FASTQ integrity, chemistry, and sample IDs; check vendor outputs against annotation builds.

   Tools and outputs

   Tools used: FastQC 0.12.1

   Output: sample_manifest.csv

3. 3. Run quality control and preprocessing

   Assess adapter contamination and library quality per sample; aggregate metrics for review (Ewels et al., 2016).

   Tools and outputs

   Tools used: fastp 0.24.0; MultiQC 1.25.1; NanoPlot 1.42.0 (long-read)

   Output: multiqc_report.html

4. 4. Quantify expression by modality

   Bulk: STAR 2.7.11b + featureCounts 2.0.8. scRNA-seq: Cell Ranger 10.0.0. Spatial: Space Ranger 3.1.3. Long-read: minimap2 2.31 + IsoQuant 3.13.0 (Dobin et al., 2013; Liao et al., 2014).

   Tools and outputs

   Output: count matrices and alignment summaries

5. 5. Audit sample and batch integrity

   Compute library sizes, detection rates, and read-distribution metrics; PCA separates condition from batch when possible (Love et al., 2014; Wang et al., 2024).

   Tools and outputs

   Tools used: DESeq2 1.52.0; RSeQC 5.0.3 (Wang et al., 2012)

   Output: sample_qc_summary.csv; PCA plots

6. 6. Model differential expression or cell states

   Bulk: DESeq2 Wald tests with batch covariates (Love et al., 2014). scRNA-seq: clustering and cluster-wise DE. Spatial: Squidpy domains and pseudobulk statistics. Long-read: SQANTI3 filtering plus DESeq2 or DRIMSeq when replicates support isoform testing.

   Tools and outputs

   Output: deg_results.csv; annotated objects

7. 7. Run functional and cell-type interpretation

   GO/KEGG enrichment; cell-type annotation; cell2location deconvolution when a reference atlas exists (Yu et al., 2012; Moses & Pachter, 2022).

   Tools and outputs

   Tools used: clusterProfiler 4.20.0; SingleR 2.12.0

   Output: enrichment and annotation tables

8. 8. Generate publication figures and package deliverables

   Export figures at publication resolution; package scripts, environment locks, README, and Methods draft.

   Tools and outputs

   Output: PDF/SVG figures; Git repository or file bundle

9. 9. Deliver results and support reviewer requests

   Transfer deliverables via agreed secure channels. Post-delivery support covers methods clarification and minor revisions; substantial new analyses are scoped separately.

Tools and standards we use

Pepkio pins software versions at kickoff and cites primary references in the Methods draft. Representative tools across modalities:

Reference builds follow GENCODE v44 (human) and GENCODE vM33 (mouse) unless a project requires a custom annotation. Bulk depth follows ENCODE: ≥30 million aligned reads per replicate (ENCODE Consortium, 2016). Inferential designs typically require ≥3 biological replicates per condition where feasible (Conesa et al., 2016).

Common challenges — and how we handle them

Researchers often struggle with pipeline choice, batch confounding, incomplete reporting, QC imbalances, and modality mismatch. Pepkio addresses each with version-pinned workflows and checkpoint reviews.

Pipeline and tool choice overload.

192 pipeline combinations on 18 samples yielded materially different quantification and DE results (Corchete et al., 2020). Pepkio selects version-pinned tool chains at kickoff and documents choices in the Methods draft.

Batch effects and confounded processing.

Technical variation can mask biological signal (Luecken & Theis, 2019; Deshpande et al., 2023). Pepkio includes batch in design formulas where not confounded with condition and reviews PCA before modeling.

Insufficient methodological reporting.

Only 25% of RNA-seq articles describe all essential computational steps (Simoneau & Scott, 2021). Pepkio delivers scripts, parameter logs, and a Methods draft with exact versions.

Hidden quality imbalances between groups.

35% of 40 clinical RNA-seq datasets had significant quality imbalances between groups (Sprang et al., 2024). Pepkio audits per-sample QC and flags asymmetry before testing.

Modality mismatch.

Bulk RNA-seq averages across cell types; heterogeneous tissues may need scRNA-seq or spatial resolution (Conesa et al., 2016; Moses & Pachter, 2022). Pepkio advises on modality at kickoff.

Common questions

Related services

• Genomics & variant analysis — Integrate expression with variant calls, eQTL mapping, or allele-specific expression when genotype data are available.
• Proteomics — Validate transcript-level findings at the protein level in multi-omics studies.
• Machine learning — Build predictive models from expression features for biomarker discovery or patient stratification.
• Statistical analysis — Experimental design, power estimation, and cohort modeling before library prep.
• Bioinformatics consulting — Modality and depth selection, feasibility assessment, and pipeline planning before committing to a full analysis project.