{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36063455", "kind": "science-record", "revision": 8, "content_hash": "sha256:92a6a4efe45cf81e890453987384d077580ca835389f77d0981cdd0a5e78c2a7", "created_at": "2026-11-01T18:15:16Z", "updated_at": "2026-11-01T23:48:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.", "doi": "10.1093/bioinformatics/btac605", "pmid": "36063455", "authors": ["Haoyang Li", "Hanmin Li", "Juexiao Zhou", "Xin Gao"], "journal": "Bioinformatics (Oxford, England)", "year": 2022, "abstract": "MOTIVATION: Unveiling the heterogeneity in the tissues is crucial to explore cell-cell interactions and cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profile which has revolutionized our biological understanding, but variations in cell-type proportions of each spot with dozens of cells would confound downstream analysis. Therefore, deconvolution of ST has been an indispensable step and a technical challenge toward the higher-resolution panorama of tissues.\nRESULTS: Here, we propose a novel ST deconvolution method called SD2 integrating spatial information of ST data and embracing an important characteristic, dropout, which is traditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-based genes as informative features from ST and scRNA-seq data by fitting a Michaelis-Menten function. After synthesizing pseudo-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover low-dimensional and non-linear representation of the real- and pseudo-ST spots. Next, we create a graph containing embedded profiles as nodes, and edges determined by transcriptional similarity and spatial relationship. Given the graph, a graph convolutional neural network is used to predict the cell-type compositions for real-ST spots. We benchmark the performance of SD2 on the simulated seqFISH+ dataset with different resolutions and measurements which show superior performance compared with the state-of-the-art methods. SD2 is further validated on three real-world datasets with different ST technologies and demonstrates the capability to localize cell-type composition accurately with quantitative evidence. Finally, ablation study is conducted to verify the contribution of different modules proposed in SD2.\nAVAILABILITY AND IMPLEMENTATION: The SD2 is freely available in github (https://github.com/leihouyeung/SD2) and Zenodo (https://doi.org/10.5281/zenodo.7024684).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "Through the comparison with ground truth, we observed that SD2 outperformed the other three methods in root mean square error (RMSE) and Jensen–Shannon Divergence (JSD).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36063455", "locator": "sections[8] \"3.2 Benchmark evaluation\" paragraph 5", "quote": "Through the comparison with ground truth, we observed that SD2 outperformed the other three methods in root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36063455", "locator": "sections[14] \"Data Availability\" paragraph 2", "quote": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079."}], "state": "complete"}, {"id": "claim-target-result", "statement": "SD2 outperformed SPOTLight, DSTG, and cell2location on the simulated seqFISH+ benchmark, achieving an RMSE of 0.06 and a JSD of 0.21.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36063455", "locator": "sections[8] \"3.2 Benchmark evaluation\" paragraph 5", "quote": "Through the comparison with ground truth, we observed that SD2 outperformed the other three methods in root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)."}, {"asset": "source:pmid:36063455", "locator": "sections[8] \"3.2 Benchmark evaluation\" paragraph 5", "quote": "In particular, SD2 has RMSE of 0.06 and JSD of 0.21 which shows the most similar pattern compared with ground truth (Fig. 2A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "SD2 outperformed SPOTLight, DSTG, and cell2location on the simulated seqFISH+ benchmark, achieving an RMSE of 0.06 and a JSD of 0.21.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36063455"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE111672"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE111672"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36063455", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bioinformatics/btac605", "sha256": "sha256:ada04be3a74dcb50ee6ffe70b9c4d5ba7a908266817be89b0c66289c0a52ca39", "bytes": 33627, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bioinformatics/btac605", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9789790", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE111672", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111672", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE111672", "version": null, "note": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3036909", "GSM3036910", "GSM3036911", "GSM3405527", "GSM3405528", "GSM3405529", "GSM3405530", "GSM3405531", "GSM3405532", "GSM3405533", "GSM3405534", "GSM4100717", "GSM4100718", "GSM4100719", "GSM4100720", "GSM4100721", "GSM4100722", "GSM4100723", "GSM4100724", "GSM4100725", "GSM4100726", "GSM4100727", "GSM4100728"], "sample_count": 23, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE157079", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157079", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE157079", "version": null, "note": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4752160", "GSM4752161", "GSM4752162", "GSM4752163", "GSM4752164", "GSM4752165", "GSM4752166", "GSM4752167", "GSM4752168", "GSM4752169", "GSM4752170", "GSM4752171", "GSM4752172"], "sample_count": 13, "group_sizes": null, "group_sizes_basis": null, "platform": "21493;21103", "taxon": "Mus musculus", "title": "Single cell resolution regulatory landscape of the mouse kidney highlights cellular differentiation programs and renal disease targets", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE98674", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98674", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98674", "version": null, "note": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2609303", "GSM2609304", "GSM2609305", "GSM2609306", "GSM2609307", "GSM2609308", "GSM2609309", "GSM2609310", "GSM2609311", "GSM2609312", "GSM2609313"], "sample_count": 11, "group_sizes": null, "group_sizes_basis": null, "platform": "17021", "taxon": "Mus musculus", "title": "Combinatorial Signal Preception in the BMP Pathway", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE6036", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6036", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE6036", "version": null, "note": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-11-01T23:38:31Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE111672 not downloaded", "asset:data:geo:GSE111672 licence not read", "asset:data:geo:GSE157079 not downloaded", "asset:data:geo:GSE157079 licence not read", "asset:data:geo:GSE98674 not downloaded", "asset:data:geo:GSE98674 licence not read", "asset:data:geo:GSE6036 not downloaded", "asset:data:geo:GSE6036 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Through the comparison with ground truth, we observed that SD2 outperformed the other three methods in root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE98674", "GSE6036", "GSE111672", "GSE157079"], "resolved_accessions": ["GSE98674", "GSE111672", "GSE157079"], "statements": ["The seqFISH+ dataset is collected from GSE98674 and corresponded scRNA-seq dataset is collected from GSE6036. The MERFISH datasets are collected from https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/ and scRNA-seq data is from https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets. ST and scRNA-seq data from PDAC are collected from GSE111672. ST data from adult mouse brain are collected from 10X Genomics and the corresponding scRNA-seq data are collected from allen institute which are sequenced by smart-seq. The ST data of mouse kidney are collected from 10X Genomics and scRNA-seq data are from GSE157079."], "basis": "The paper names GSE98674, GSE111672, GSE157079 in GSE and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:36063455", "data:geo:GSE111672"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE111672"]}, "gaps": ["method:not_determinable"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:03:43Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9191, "completion_tokens": 2489, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36063455", "type": "paper", "label": "pmid:36063455"}, {"id": "source:pmid:36063455", "type": "manuscript", "label": "PMID 36063455 full text"}, {"id": "data:geo:GSE111672", "type": "dataset", "label": "GEO GSE111672"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36063455", "type": "paper", "label": "pmid:36063455"}, {"id": "source:pmid:36063455", "type": "manuscript", "label": "PMID 36063455 full text"}, {"id": "data:geo:GSE111672", "type": "dataset", "label": "GEO GSE111672"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36063455", "type": "extraction", "used": ["source:pmid:36063455"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE111672", "type": "accession_resolution", "used": ["data:geo:GSE111672"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36063455", "parent_hash": "sha256:c99305879c59e328e1e4d3b31e27e66f802ab444d6ac187ec085508acb4d1e4c", "fork_of": null, "generation": 7}, "gaps": ["asset:data:geo:GSE111672 licence not read", "asset:data:geo:GSE111672 not downloaded", "asset:data:geo:GSE157079 licence not read", "asset:data:geo:GSE157079 not downloaded", "asset:data:geo:GSE6036 licence not read", "asset:data:geo:GSE6036 not downloaded", "asset:data:geo:GSE98674 licence not read", "asset:data:geo:GSE98674 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["SD2: spatially resolved transcriptomics deconvolution through integration of dropout", "cellular targets of human diseases. Spatial transcriptomics (ST) supplies spatial gene expression profil", "considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["raditionally considered as an obstruction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract", "truction in single-cell RNA sequencing data (scRNA-seq) analysis. First, we extract the dropout-bas", "ed genes as informative features from ST and scRNA-seq data by fitting a Michaelis-Menten function.", "do-ST spots by randomly composing cells from scRNA-seq data, auto-encoder is applied to discover lo"], "order_basis": "paper_text_order:797", "id": "assay:2"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T03:05:42+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}