SciFlow lab notebook · Shared read-only

Single-Cell RNA-seq — PBMC Stim vs Control (SciFlow)

Take quantified single-cell counts through the full analysis arc — normalization, integration, clustering, cell-type labels, marker genes, and a real differential expression test between conditions.

Overview Methods & provenance Results Raw artifacts

Run date

2026-05-30 → 2026-06-12

Stages

16 16 completed

Pipeline

scrna_qc → scrna_normalize → scrna_integrate → scrna_cluster → scrna_annotate → scrna_ma…

Software

not recorded

Outputs

105 files 2.3 GB

Overview

Take quantified single-cell counts through the full analysis arc — normalization, integration, clustering, cell-type labels, marker genes, and a real differential expression test between conditions. Drop in 10x count matrices (the demo combines pbmc3k and pbmc6k for batch integration, then layers on the Kang 2018 IFN-β stimulated vs control PBMCs as the conditional contrast), get back a labeled UMAP, per-cluster markers, and a pseudobulk DE table you can actually publish. Built for immunology and translational labs running stim/ctrl, drug/vehicle, or disease/healthy designs, and for core facilities wanting a defensible default workflow they do not have to reinvent for every project.

Counts go in for DE; never TPM, never FPKM. Normalization is SCTransform v2 — variance-stabilized, with mitochondrial percent regressed out. Integration is Harmony on the SCT-corrected embedding, clustering is Leiden on the integrated graph, and cell-type calls come from Azimuth against the PBMC reference so labels are reproducible across runs. Marker detection uses presto for fast Wilcoxon ranks at the cell level, and the cross-condition test is pseudobulk DESeq2 — aggregating to sample-level counts before the model, which is the only DE approach with calibrated false-positive rates on this data type.

Methods & per-stage provenance

16 stages, 16 completed. Each stage below lists the parameters it ran with, the software the run recorded, and the files it produced.

Not recorded by this run
  • Software: no stage in this run recorded the container image it executed
  • no figures rendered (no PNG/SVG outputs and no Plotly HTML figure could be extracted)
01 scRNA QC — pbmc3k (10X v1) scrna_qc 31 s completed
counts path: pbmc3ksample id: pbmc3kmad factor: 5.0drop doublets: True

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  • doublet_calls.csvtable · 124 KB
  • filtered_matrix/barcodes.tsv.gzGZ · 6 KB
  • filtered_matrix/features.tsv.gzGZ · 95 KB
  • filtered_matrix/matrix.mtx.gzGZ · 6.2 MB
  • input_data_summary.htmlreport · 130 KB
  • qc_summary.jsondata · 512 B
  • sce_filtered.rdsRDS · 4.7 MB
  • scrna_qc_report.htmlreport · 51 KB
  • summary.jsondata · 657 B
02 scRNA QC — pbmc6k (10X v2) scrna_qc 46 s completed
counts path: pbmc6ksample id: pbmc6kmad factor: 5.0drop doublets: True

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  • doublet_calls.csvtable · 252 KB
  • filtered_matrix/barcodes.tsv.gzGZ · 12 KB
  • filtered_matrix/features.tsv.gzGZ · 100 KB
  • filtered_matrix/matrix.mtx.gzGZ · 10.7 MB
  • qc_summary.jsondata · 511 B
  • sce_filtered.rdsRDS · 7.8 MB
  • scrna_qc_report.htmlreport · 102 KB
  • summary.jsondata · 656 B
03 scRNA QC — Kang 2018 ctrl (4 donors) scrna_qc 49 s completed
counts path: kang_ctrlsample id: kang_ctrlmad factor: 5.0drop doublets: True

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  • doublet_calls.csvtable · 348 KB
  • filtered_matrix/barcodes.tsv.gzGZ · 14 KB
  • filtered_matrix/features.tsv.gzGZ · 93 KB
  • filtered_matrix/matrix.mtx.gzGZ · 10.6 MB
  • qc_summary.jsondata · 510 B
  • sce_filtered.rdsRDS · 7.7 MB
  • scrna_qc_report.htmlreport · 132 KB
  • summary.jsondata · 655 B
04 scRNA QC — Kang 2018 stim (4 donors, IFN-β) scrna_qc 1 min completed
counts path: kang_stimsample id: kang_stimmad factor: 5.0drop doublets: True

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  • doublet_calls.csvtable · 325 KB
  • filtered_matrix/barcodes.tsv.gzGZ · 13 KB
  • filtered_matrix/features.tsv.gzGZ · 90 KB
  • filtered_matrix/matrix.mtx.gzGZ · 10.3 MB
  • qc_summary.jsondata · 510 B
  • sce_filtered.rdsRDS · 7.4 MB
  • scrna_qc_report.htmlreport · 124 KB
  • summary.jsondata · 655 B
05 SCTransform v2 — PBMC merged scrna_normalize 247.2 h completed
backend: seuratmethod: sct_v2n hvg: 3000regress mt: True

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  • normalize_summary.jsondata · 200 B
  • scrna_normalize_report.htmlreport · 933 B
  • seurat_object.rdsRDS · 213.8 MB
  • summary.jsondata · 345 B
06 SCTransform v2 — Kang merged scrna_normalize 247.1 h completed
backend: seuratmethod: sct_v2n hvg: 3000regress mt: True

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  • normalize_summary.jsondata · 207 B
  • scrna_normalize_report.htmlreport · 940 B
  • seurat_object.rdsRDS · 323.8 MB
  • summary.jsondata · 352 B
07 Harmony integration — PBMC cross-chemistry (v1 vs v2) scrna_integrate 46 s completed
method: harmonybatch key: sample_idtheta: 2.0

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  • harmony_embeddings.csvtable · 6.5 MB
  • integrate_summary.jsondata · 412 B
  • scib_metrics.jsondata · 246 B
  • scrna_integrate_report.htmlreport · 1 KB
  • seurat_integrated.rdsRDS · 217.8 MB
  • summary.jsondata · 561 B
08 Harmony integration — Kang cross-donor scrna_integrate 2.0 h completed
method: harmonybatch key: sample_idtheta: 2.0

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  • harmony_embeddings.csvtable · 10.6 MB
  • integrate_summary.jsondata · 413 B
  • scib_metrics.jsondata · 246 B
  • scrna_integrate_report.htmlreport · 1 KB
  • seurat_integrated.rdsRDS · 328.8 MB
  • summary.jsondata · 562 B
09 Leiden sweep + UMAP — PBMC integrated scrna_cluster 3 min completed
reduction: harmonyresolutions: 0.4,0.6,0.8,1.0,1.2

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  • cluster_summary.jsondata · 400 B
  • scrna_cluster_report.htmlreport · 2 KB
  • seurat_clustered.rdsRDS · 220.9 MB
  • summary.jsondata · 569 B
  • umap_clusters.csvtable · 458 KB
10 Leiden sweep + UMAP — Kang integrated scrna_cluster 1.0 h completed
reduction: harmonyresolutions: 0.4,0.6,0.8,1.0,1.2

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  • cluster_summary.jsondata · 401 B
  • scrna_cluster_report.htmlreport · 2 KB
  • seurat_clustered.rdsRDS · 333.9 MB
  • summary.jsondata · 570 B
  • umap_clusters.csvtable · 789 KB
11 Azimuth PBMC reference mapping scrna_annotate 1.0 h completed
method: azimuthreference: pbmcref

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  • annotate_summary.jsondata · 286 B
  • annotations.csvtable · 639 KB
  • scrna_annotate_report.htmlreport · 1 KB
  • seurat_annotated.rdsRDS · 228.8 MB
  • summary.jsondata · 431 B
12 Azimuth PBMC reference mapping (Kang) scrna_annotate 1.0 h completed
method: azimuthreference: pbmcref

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  • annotate_summary.jsondata · 285 B
  • annotations.csvtable · 1.2 MB
  • scrna_annotate_report.htmlreport · 1 KB
  • seurat_annotated.rdsRDS · 347.0 MB
  • summary.jsondata · 430 B
13 presto markers — PBMC clusters scrna_markers 1.0 h completed
group by: predicted.celltype.l2top n: 5min auc: 0.6

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  • markers.csvtable · 41.9 MB
  • markers_summary.jsondata · 263 B
  • scrna_markers_report.htmlreport · 1 KB
  • summary.jsondata · 408 B
  • top_markers.csvtable · 14 KB
14 Pseudobulk DE — IFN-β vs Ctrl (Squair 2021) scrna_de 1.0 h completed
design formula: ~ donor + conditioncontrast name: condition_stim_vs_ctrlcell type column: predicted.celltype.l2sample id column: sample_idmin cells per pb: 10fdr method: BHlfc shrink type: apeglmmetadata csv: cell_metadata.csv

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  • diagnostics.htmlreport · 1.1 MB
  • lib/crosstalk-1.2.2/css/crosstalk.min.cssCSS · 1 KB
  • lib/crosstalk-1.2.2/js/crosstalk.jsJS · 48 KB
  • lib/crosstalk-1.2.2/js/crosstalk.js.mapMAP · 54 KB
  • lib/crosstalk-1.2.2/js/crosstalk.min.jsJS · 18 KB
  • lib/crosstalk-1.2.2/js/crosstalk.min.js.mapMAP · 49 KB
  • lib/crosstalk-1.2.2/scss/crosstalk.scssSCSS · 2 KB
  • lib/htmltools-fill-0.5.8.1/fill.cssCSS · 585 B
  • lib/htmlwidgets-1.6.4/htmlwidgets.jsJS · 32 KB
  • lib/jquery-3.5.1/jquery-AUTHORS.txttext · 13 KB
  • lib/jquery-3.5.1/jquery.jsJS · 281 KB
  • lib/jquery-3.5.1/jquery.min.jsJS · 87 KB
  • lib/jquery-3.5.1/jquery.min.mapMAP · 135 KB
  • lib/plotly-binding-4.12.0/plotly.jsJS · 33 KB
  • lib/plotly-htmlwidgets-css-2.25.2/plotly-htmlwidgets.cssCSS · 173 B
  • lib/plotly-main-2.25.2/plotly-latest.min.jsJS · 3.4 MB
  • lib/typedarray-0.1/typedarray.min.jsJS · 22 KB
  • pseudobulk_metadata.csvtable · 25 KB
  • results.csvtable · 2.7 MB
  • scrna_de_report.htmlreport · 124 KB
  • summary.jsondata · 2 KB
15 GO Enrichment -- IFN-b vs Ctrl (kang-de) go_enrichment 2 min completed
padj cutoff: 0.05lfc cutoff: 1.0go mapping file: /databases/go/human_gene2go.tsvgo mapping format: tsvcell type filter: CD14 Mono

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  • enrichment_plot.htmlreport · 3.6 MB
  • enrichment_results.csvtable · 407 KB
  • summary.jsondata · 1 KB
16 GSEA -- IFN-b vs Ctrl (kang-de) gsea_enrichment 7 s completed
ranked gene col: gene_idranked stat col: log2FoldChangemin size: 15max size: 500gene2term file: /databases/go/human_gene2go.tsvgene2term format: tsvcell type filter: CD14 Mono

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  • gsea_plot.htmlreport · 3.6 MB
  • gsea_results.csvtable · 133 KB
  • summary.jsondata · 867 B

Results

No static figure could be embedded in this notebook — this run recorded no PNG/SVG output, and no interactive plot could be exported to a static image. Any figures this run produced are listed among the raw artifacts below and remain viewable in the project itself.

Raw artifacts

105 output files produced across all stages (2.3 GB total). This report is self-contained; structures and trajectories are listed below for reference.