Transcriptomics
scRNA-seq from FASTQs to annotated UMAPs and cell-type labels when bulk pools hide rare populations, with pinned Cell Ranger or Scanpy workflows and a Methods draft [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | Primary: 10x Genomics Chromium (3′ v3.1/v4, 5′ v2/v3, GEM-X, Flex). Illumina or MGI DNBSEQ-sequenced FASTQs when scoped at kickoff. BD Rhapsody WTA, Parse Biosciences Evercode WT, and plate-based SMART-seq2/3 on request |
| Input requirements | ≥20,000 read pairs/cell (3′/5′ standard); ≥10,000 read pairs/cell (Flex); 500-20,000 cells recovered per 10x library; >90% viability recommended [2] |
| Reference builds supported | Human GRCh38-2024-A (GENCODE v44 / Ensembl 110); mouse GRCm39-2024-A (GENCODE vM33 / Ensembl 110); custom references on request |
| Primary tools (with versions) | Cell Ranger 10.0.0; Scanpy 1.12.1; Seurat 5.2.1; scvi-tools 1.4.3; SoupX 1.6.2; scDblFinder 1.18.0; SingleR 2.12.0; harmonypy 1.2.3 |
| Typical turnaround time | 3-5 weeks (standard single-cohort project); 5-8 weeks (multi-cohort integration or >100,000 cells). Confirmed at kickoff |
| Deliverable formats | .h5ad, .rds, .csv matrices; PDF/SVG figures; HTML QC report; documented R/Python scripts; Methods draft |
| Key cited best-practice reference | Luecken & Theis [1], Molecular Systems Biology; Heumos et al. [3], Nature Reviews Genetics |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, for example custom matrices, client-specified figures/tables, trajectory, or cross-modality extensions |
scRNA-seq assigns sequenced transcripts to individual cell barcodes, producing a UMI count matrix rather than a tissue-level average. Droplet platforms such as 10x Chromium typically recover 500-10,000 cells per lane, and up to ~20,000 with GEM-X [2], which can separate rare cell populations that bulk RNA-seq averages together [1]. Some reference atlases now exceed 100,000 cells per study; Tabula Sapiens profiled nearly 500,000 cells across 24 human tissues [4]. Pepkio starts from FASTQs or count matrices and returns annotated objects with documented QC at each step. Custom inputs, deliverable formats, and analyses outside the standard workflow are agreed at kickoff. See the scRNA-seq glossary.
scRNA-seq is a fit when the variation of interest is at cell-type or cell-state resolution, such as heterogeneous tumors, inflamed tissue, developing organs, or immune repertoires. The table contrasts scRNA-seq with bulk RNA-seq and spatial transcriptomics.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| scRNA-seq (droplet) | Cell-type discovery, rare populations, trajectory inference, multi-condition composition shifts | Dissociation stress and ambient RNA artifacts; no native spatial context; higher per-cell cost than bulk | Library prep, sequencing, and bioinformatics analysis vary by cell count, depth, and integration scope |
| Bulk RNA-seq | Condition contrasts (treated vs. control) when tissue is homogeneous or deconvolution is sufficient | Averages across cell types; rare populations may fall below detection | Lower per-sample cost than scRNA-seq for modest cohorts |
| Spatial transcriptomics (e.g., 10x Visium, Xenium) | Tissue architecture, cell-cell niches, region-specific expression | Lower depth per cell; platform-specific capture; sectioning constraints | Higher per-study cost than dissociated scRNA-seq |
Pepkio confirms FASTQ integrity, read structure (Read 1: 28 bp barcode/UMI; Read 2: ≥90 bp cDNA for 10x 3′), and metadata. Chemistry, expected recovery, depth, and covariates are recorded in sample_manifest.csv. Sub-threshold depth is flagged before alignment [2].
Tools used: fastqc / fastp as needed
Output: sample_manifest.csv with library IDs, chemistry, read counts, and QC flags
For 10x data, Pepkio runs cellranger count or cellranger multi with GRCh38-2024-A or GRCm39-2024-A references [2]. Saturation, median genes per cell, and fraction of reads in cells are compared against vendor expected ranges.
Tools used: Cell Ranger 10.0.0
Output: filtered_feature_bc_matrix/, raw_feature_bc_matrix/, metrics_summary.csv, web_summary.html
Count matrices are imported with raw UMI counts kept in a dedicated layer. Pepkio audits cell calling, saturation curves, and gene detection distributions. Non-10x count matrices (BD Rhapsody, Parse Evercode, SMART-seq2) are imported via anndata or Seurat::CreateSeuratObject when provided.
Tools used: Scanpy 1.12.1 or Seurat 5.2.1
Output: Per-sample .h5ad or .rds with counts layer and initial metadata
Cell-free RNA contaminates droplet matrices and can misassign marker genes [3, 7]. When raw and filtered Cell Ranger matrices are available, Pepkio estimates contamination with SoupX and produces background-corrected counts. Elevated estimated soup fractions are flagged for review before clustering.
Tools used: SoupX 1.6.2
Output: Corrected count matrix; per-sample soup_fraction in metadata; SoupX diagnostic plots
Shared barcodes create hybrid transcriptomes that distort clustering [8]. Pepkio runs scDblFinder or Scrublet per sample, not on merged objects [3, 9]. Expected multiplet rates are ~0.8% per 1,000 cells on Next GEM and ~0.4% on GEM-X [2]. Predicted doublets are flagged in metadata.
Tools used: scDblFinder 1.18.0 or Scrublet 0.2.3
Output: doublet_score, predicted_doublet columns; doublet score histograms
Cells with extreme mitochondrial fractions, low gene complexity, or empty-droplet profiles are removed using sample-adaptive thresholds, because useful QC boundaries vary by tissue and dissociation protocol [1]. Retained and excluded counts are documented per filter rule.
Tools used: Scanpy 1.12.1 or Seurat 5.2.1
Output: Filtered object; QC plots for nCount_RNA, nFeature_RNA, percent.mt, percent.ribo
For R workflows, Pepkio applies SCTransform v2, modeling sequencing depth and returning Pearson residuals for PCA [10]. Python workflows use sc.pp.normalize_total and HVG selection with the seurat_v3 flavor [11]. HVG sets and parameters are recorded for reproducibility.
Tools used: sctransform 0.4.3 (via Seurat 5.2.1) or Scanpy 1.12.1
Output: Normalized layers; highly_variable_genes.csv
Harmony integrates same-modality batches with shared cell types [12]. scVI is used for atlas-level integration where compositional differences can confound linear methods [13, 14, 15]. Marker-gene checks are used to see whether biological states were over-merged.
Tools used: harmonypy 1.2.3 or scvi-tools 1.4.3
Output: X_harmony or X_scVI embedding; before/after UMAP by batch and condition
Pepkio builds a neighbor graph, runs Leiden community detection at a data-driven resolution (validated with marker genes), and computes UMAP. Cell types are assigned by reference mapping with SingleR [6], then reviewed against markers. Ambiguous clusters receive provisional labels with supporting evidence.
Tools used: Scanpy 1.12.1 or Seurat 5.2.1; SingleR 2.12.0; leidenalg 0.10.2
Output: Cluster assignments; UMAP/t-SNE plots; marker_gene_table.csv
Cluster-wise or condition-wise DE uses Wilcoxon rank-sum tests with Benjamini-Hochberg FDR correction [1]. Seurat workflows use the presto implementation on large objects when it is installed [16]. Results export as ranked gene lists with log2 fold-change, detection rate, and adjusted p-values. Pseudotime trajectory analysis is scoped separately when requested.
Tools used: Scanpy rank_genes_groups or Seurat FindMarkers with presto
Output: deg_by_cluster.csv; volcano plots; final .h5ad/.rds; scripts; Methods draft
For standard 10x Chromium 3′ or 5′ libraries, Pepkio recommends ≥20,000 read pairs per cell [2]. Chromium Flex typically requires ≥10,000 read pairs per cell. Hundreds of high-quality cells per biological condition usually support more stable clustering. Fewer cells can be analyzed, but power for rare populations drops. Exact targets are confirmed at kickoff.
Yes, with caveats documented in the QC report. Samples below recommended viability often show elevated mitochondrial fractions and stress genes (FOS, JUN) that can affect clustering [1]. Cells with very low UMI counts are typically excluded. Re-sequencing or pooling is discussed before committing to full downstream analysis.
Yes. 10x Chromium 3′, 5′, GEM-X, and Flex are processed via Cell Ranger 10.0.0 from Illumina or MGI DNBSEQ-sequenced FASTQs when scoped at kickoff. BD Rhapsody, Parse Evercode, and SMART-seq2/3 count matrices can be imported into Scanpy or Seurat on request, with barcode and feature handling matched to the platform chemistry.
A standard single-cohort project (roughly 4-8 samples, one tissue, no cross-study integration) typically completes in 3-5 weeks from data receipt. Multi-cohort integration, atlas-scale datasets (>100,000 cells), or CITE-seq extensions may take 5-8 weeks. Weekly milestone check-ins occur during the project. Exact timelines are confirmed at kickoff.
Harmony is used for within-study technical batches when shared cell types are present [12]. Cross-study integration uses scVI [13, 15]. Donor, age, and sex covariates stay in metadata and are stratified or regressed per design. Integration quality is checked by marker-gene preservation.
Yes. You retain full ownership of all code, scripts, and results. Pepkio delivers commented R/Python scripts and environment lock files (sessionInfo(), conda, or pip). Objects use standard .h5ad and .rds formats readable in Scanpy or Seurat. R Markdown or Jupyter delivery is available on request.
Yes. Checkpoint reviews occur after QC, clustering, and before final delivery. You can review annotations, adjust cluster resolution, and request contrasts within agreed scope. A PhD-level scientific contact leads the project and incorporates your tissue-specific knowledge.
Clarification of computational methods, QC thresholds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Pepkio drafts Methods and Supplementary text for analyses we performed. Substantial new analyses requested by reviewers are scoped separately.
No. Pepkio operates as a fee-for-service provider and does not require co-authorship unless that is discussed in advance. Acknowledgment of bioinformatics support in the Acknowledgments section is the usual practice. Co-authorship is considered only when Pepkio scientists make substantial intellectual contributions beyond routine analysis.
For 10x data, Pepkio defaults to Cell Ranger for chemistry-specific barcode handling and vendor QC metrics [2]. STARsolo and kallisto|bustools suit non-10x chemistries or open-source requirements. The Methods draft states the aligner and reference build used.
Pepkio detects doublets with scDblFinder or Scrublet per sample after ambient RNA correction [8, 9]. Expected multiplet rates are about 0.8% per 1,000 cells on Next GEM and about 0.4% on GEM-X [2], scaling to roughly 5-8% at 10,000 recovered cells before removal. Predicted doublets are flagged. Exclusion counts are documented.
Cross-modality work such as pseudobulk cluster aggregation, deconvolution validation, or label transfer to Visium/Xenium spots is available as a separately scoped milestone [3]. Scope, timeline, and deliverables are defined at kickoff based on your reference datasets and biological questions.
Yes. Beyond the standard FASTQ-to-UMAP workflow, Pepkio scopes bespoke work at kickoff: custom inputs (e.g., preprocessed matrices), output formats, uncommon methods, or analyses outside typical clustering and DE. Milestone pricing and timelines are confirmed before work begins.
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