Paper · S7568
SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis
SPACEc combines spatial proteomics processing, cell annotation and neighbourhood analysis. Nolan is credited with supervision. The supplied article, static supplement and reporting form were reviewed; peer-review correspondence is partly read. Reused tissues, expert labels and nested cell counts limit independent replication. Computational comparisons depend on dataset, platform, normalization and metric definitions; funding does not establish a UAP programme.
Read the paper at ebi.ac.ukWhat we checked
The supplied main article text, 55 references and nine-page PDF were reviewed. All 44 supplementary pages were text-read, including fourteen figures and five tables; meaningful figure/table pixels were inspected, while 23 text-only layouts remain unviewed. The three-page reporting form was read in text and pixels. All 74 supplied peer-review pages are now read and viewed across 37 bounded returns and four preserved packets. Root matched every native page text and image receipt before granting supplied correspondence completion. Earlier criticism, collective replies, proposed revisions and the final article remain separate versions. The source-data README and three literal region lists (66, 33 and one entries) were fully read; the 33-entry list is the first half of the larger list, with selection rationale unknown. All 2,025 explicit cell values and native types across six benchmark workbooks and 21 worksheets were read in six full returns. Original workbook layouts were not viewed. Raw-data replication and receipt of all original study materials are not established.
Complete supplied main and static supplement text read; peer review partial
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- Supplied peer correspondence is complete, but references 32–33 are missing, a distance radical is ambiguous and small native labels remain unresolved. Final reviewer satisfaction does not erase continued benchmarking concerns. No systematic platform-normalization comparison or quantified universal channel-selection test was provided; proposed corrections are distinct from final published text.
- The received source-data archive has six XLSX files, README and region lists still unread; its h5ad body is unextracted. Raw repositories, code, original patient forms and independent expert labels were not acquired or run.
- One healthy and one inflamed tonsil, reused tissue arrays and nested cells do not establish population replication. Manual annotation, selected parameters, label permutations and unspecified multiplicity or direction limit inference; proximity is not demonstrated functional interaction.
- Hardware, profilers, software modes and computational repeats limit comparisons. Workbook numeric zeros differ from absent cells and do not establish measured zero cost. Metric decimals stored as strings, a literal 4645 memory entry, repeated method triples, non-monotone core scaling and differing STELLAR platform values remain preserved and unreconciled. Some alternatives perform better; acceptance is not replication or clinical validation.
- The h5ad dataset, underlying raw measurements, code execution, original workbook layouts and 23 text-only supplement layouts remain unreviewed. Coded region prefixes do not establish independent donor counts. Source version, metric, caption, axis, selection and reporting-form discrepancies remain unresolved. No UAP cohort, government programme or clinical validity follows from this biomedical paper.
Cite this record
Yuqi Tan, Tim N Kempchen, Martin Becker, Maximilian Haist, Dorien Feyaerts, Jiaqi Liu, Marieta Toma, Yang Xiao, Graham Su, Andrew J Rech, Michael Hölzel, Rong Fan, John W Hickey, Garry P Nolan. SPACEc: a streamlined, interactive Python workflow for multiplexed image processing and analysis. November 27, 2025. BioParity S7568. https://bioparity.io/sources/S7568Record data (JSON)