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Systematic benchmarking and optimal strategy selection of cross-species integration methods.

Code ↔ Paper

9 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 9 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Dimension reduction and visualization ↔ core_method/ComBat.R, lines 75–143 · score 0.82 · RunPCA, FindClusters, FindNeighbors, DimPlot, RunUMAP, npcs
  2. [2] § Materials and methods › Dimension reduction and visualization ↔ core_method/R_for_all_tasks.R, lines 79–122 · score 0.82 · RunPCA, FindClusters, FindNeighbors, DimPlot, RunUMAP, npcs
  3. [3] § Results › Construction and application of machine learning evaluation model ↔ model/Scripts/predict_overall_score.py, lines 2–61 · score 0.67 · Batch.ASW, kBET, Random Forest, predicting, training, NMI
  4. [4] § Materials and methods › Evaluated metrics › Local Inverse Simpson’s Index ↔ evaluation/ARI_LISI/evaluation_LISI_knn.py, lines 60–125 · score 0.60 · nearest neighbors, unique batches, Simpson, vectors, matrices, LISI
  5. [5] § Materials and methods › Evaluated metrics ↔ model/Scripts/predict_overall_score.py, lines 2–61 · score 0.59 · graph connectivity, kBET, PCR, ARI, NMI, ASW
  6. [6] § Results › Multidimensional cross-species single-cell transcriptome integration framework ↔ evaluation/ARI_LISI/ARI_LISI_rds.R, lines 1–58 · score 0.56 · FastMNN, JointPCA, ComBat, Conos, RPCA, Harmony
  7. [7] § Results › Systematic assessment of factors affecting cross-species data integration ↔ evaluation/ARI_LISI/ARI_LISI_rds.R, lines 1–58 · score 0.54 · FastMNN, JointPCA, ComBat, RPCA, Harmony, CCA
  8. [8] § Materials and methods › Cross-species data integration methods › scVI and scANVI ↔ core_method/scANVI.py, the whole file · a weak match · score 0.53 · labels_key, latent, AnnData, scANVI, training, scVI
  9. [9] § Materials and methods › Evaluated metrics › NMI ↔ evaluation/ASW_NMI/evaluation_ASW_NMI.py, lines 138–211 · score 0.53 · Louvain clustering, scIB, NMI

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 122 lines · 2.4 KB · CC-BY-4.0 · 2 matches

  2. import pandas as pd
  3. import numpy as np
  4. import joblib
  5. import argparse
  6. import sys
  8. parser = argparse.ArgumentParser(
  9. description="Predict Overall.Score for new integration methods using a trained Random Forest model"
  10. )
  12. parser.add_argument(
  13. "--model",
  14. required=True,
  15. help="Path to trained RF model (.joblib)"
  16. )
  18. parser.add_argument(
  19. "--input",
  20. required=True,
  21. help="CSV file with scIB metrics for new methods"
  22. )
  24. parser.add_argument(
  25. "--output",
  26. default="predicted_overall_score.csv",
  27. help="Output CSV file (default: predicted_overall_score.csv)"
  28. )
  30. parser.add_argument(
  31. "--train-data",
  32. required=True,
  33. help="Training data CSV used to fit the RF model (for OOD check)"
  34. )
  36. args = parser.parse_args()
  39. FEATURES = [
  40. "Graph.iLISI",
  41. "Batch.NMI",
  42. "Batch.ARI",
  43. "Batch.ASW",
  44. "PCR.batch",
  45. "kBET",
  46. "Graph.connectivity",
  47. "Graph.cLISI",
  48. "Cell.type.NMI",
  49. "Cell.type.ARI",
  50. "Cell.type.ASW",
  51. ]
  55. model = joblib.load(args.model)
  57. scaler = model.named_steps["scaler"]
  58. rf = model.named_steps["model"]
  61. new_data = pd.read_csv(args.input)
  63. missing = [f for f in FEATURES if f not in new_data.columns]
  64. if missing:
  65. print("Missing required features:")
  66. for f in missing:
  67. print(" -", f)
  68. sys.exit(1)
  70. X_new = new_data[FEATURES]
  73. new_data["Predicted.Overall.Score"] = model.predict(X_new)
  76. X_scaled = scaler.transform(X_new)
  78. tree_preds = np.array(
  79. [tree.predict(X_scaled) for tree in rf.estimators_]
  80. )
  82. new_data["Prediction.Std"] = tree_preds.std(axis=0)
  83. new_data["Prediction.Lower"] = (
  84. new_data["Predicted.Overall.Score"] - 2 * new_data["Prediction.Std"]
  85. )
  86. new_data["Prediction.Upper"] = (
  87. new_data["Predicted.Overall.Score"] + 2 * new_data["Prediction.Std"]
  88. )
  92. train_data = pd.read_csv(args.train_data)
  94. train_stats = train_data[FEATURES].describe().T[
  95. ["min", "max", "mean", "std"]
  96. ]
  98. ood_flags = []
  100. for i, row in X_new.iterrows():
  101. out_of_range = []
  102. for f in FEATURES:
  103. v = row[f]
  104. lo, hi = train_stats.loc[f, ["min", "max"]]
  105. if v < lo or v > hi:
  106. out_of_range.append(f)
  107. ood_flags.append(",".join(out_of_range))
  109. new_data["OOD.Features"] = ood_flags
  110. new_data["Is.OOD"] = new_data["OOD.Features"] != ""
  113. new_data.to_csv(args.output, index=False)
  115. cols_preview = [
  116. "Predicted.Overall.Score",
  117. "Prediction.Std",
  118. "Prediction.Lower",
  119. "Prediction.Upper",
  120. "Is.OOD",
  121. ]
  122. print(new_data[cols_preview].head())

predict_overall_score.py, under CC-BY-4.0 · at the source

Overview

Authors: Ruolin Wang1,2, Junjuan Zheng1,2, Chuning Mao1,2, Ya-Ping Zhang1,2,3, Zhaoli Ding1,2, Guo-Dong Wang1,2
ORCID iDs: Ya-Ping Zhang, Guo-Dong Wang
  1. State Key Laboratory of Genetic Evolution and Animal Models, Kunming Institute of Zoology, Chinese Academy of Sciences, 17 Longxin Road, Kunming, Yunnan, 650201, China
  2. Kunming College of Life Science, University of Chinese Academy of Sciences, 19 Qingsong Road, Kunming, Yunnan, 650201, China
  3. Bio-X Center for Interdisciplinary Innovation, Yunnan University, 2 Cuihu North Road, Kunming, Yunnan 650091, China
Institutions: Kunming Institute of Zoology (China); Chinese Academy of Sciences (China); University of Chinese Academy of Sciences (China); Yunnan University (China)
Journal: Briefings in bioinformatics, volume 27, issue 5, article bbag490
Dates: received 27 April 2026; accepted 7 August 2026; published online 10 September 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/bib/bbag490 · PMID 42721443 · PMCID PMC13561306 · OpenAlex W7212163362
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, Statistics, Connectivity
Keywords: single-cell transcriptome, cross-species comparison, integrated evaluation
MeSH: Computational Biology*, Single-Cell Analysis*, Transcriptome*, Animals, Benchmarking, Gene Expression Profiling, Hippocampus, Humans, Machine Learning, Mice, Sequence Analysis, RNA, Single-Cell Gene Expression Analysis, Species Specificity (* major topic)
Journal subjects: Problem Solving Protocol
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: STI2030-Major Projects (2021ZD020-3900); Major Science and Technology Program of Yunnan (202502AU-100002, 2025AB053); Biological Resources Program, Chinese Academy of Sciences (KFJ-BRP-004)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Single-cell RNA sequencing provides an unprecedented resolution for cellular heterogeneity and gene regulation, fostering cross-species comparative analyses with increasing interspecies data. However, integrating single-cell transcriptomic data faces challenges, including gene selection, evolutionary distance, and batch effects, with varying method performances. We utilized single-cell transcriptomic data from hippocampal tissues of seven mammals (e.g. mouse, human), evaluating 13 mainstream integration methods across 27 tasks with 11 metrics. To compare the performance of different methods, we developed a machine learning-based scoring model that assesses the contribution of each metric in a data-driven manner, thereby addressing the oversimplified assumptions of traditional manual weighting approaches. Our findings show that selecting highly variable one-to-one orthologous genes best balances species differences and commonalities. Most methods integrated closely related species, whereas scVI, a probabilistic model with distributions specified by deep neural networks, and its semi‑supervised extension scANVI, as well as the Seurat v5 method, which uses reciprocal principal component analysis (RPCAv5), effectively mapped distantly related species. Increased species numbers reduce gene overlap and heighten heterogeneity, increasing integration difficulty. The scANVI best maintained quality by balancing the biological signals and batch effect removal. Furthermore, we established an evaluation website to guide researchers in selecting the optimal integration methods for cross-species single-cell transcriptomic data analysis. Collectively, our findings provide a systematic, evidence-based framework that can assist researchers in rapidly selecting appropriate integration methods for cross-species single-cell transcriptomic studies.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

figshare 31976259

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (21 files), Scanpy (18 files), NumPy (17 files), Seurat (10 files), ggplot2 (7 files), patchwork (6 files), SciPy (6 files), tidyverse (6 files), data.table (5 files), Matplotlib (4 files), scikit-learn (4 files), anndata (3 files), seaborn (2 files), igraph (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
37 files
At the source: doi.org/10.6084/m9.figshare.31976259

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 36 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

Datasets were collected from published studies as described in Supplementary Tables S1 and S2. The preprocessed datasets for each task are available at https://doi.org/10.6084/m9.figshare.31968882. The source code and corresponding step-by-step manual are available at https://doi.org/10.6084/m9.figshare.31976259. ScCross is freely available online at https://sccross.pythonanywhere.com/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 13 MeSH terms, 3 funders, 65 references.

Cite

This paper

Wang, R., Zheng, J., Mao, C., Zhang, Y.-P., Ding, Z., & Wang, G.-D. (2026). Systematic benchmarking and optimal strategy selection of cross-species integration methods. Briefings in bioinformatics, 27(5), bbag490. https://doi.org/10.1093/bib/bbag490

BibTeX

@article{wang2026systematic,
author = {Wang, Ruolin and Zheng, Junjuan and Mao, Chuning and Zhang, Ya-Ping and Ding, Zhaoli and Wang, Guo-Dong},
title = {{Systematic benchmarking and optimal strategy selection of cross-species integration methods}},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
volume = {27},
number = {5},
pages = {bbag490},
publisher = {Oxford University Press},
issn = {1467-5463},
doi = {10.1093/bib/bbag490},
url = {https://doi.org/10.1093/bib/bbag490},
pmid = {42721443},
pmcid = {PMC13561306}
}

RIS

TY - JOUR
AU - Wang, Ruolin
AU - Zheng, Junjuan
AU - Mao, Chuning
AU - Zhang, Ya-Ping
AU - Ding, Zhaoli
AU - Wang, Guo-Dong
TI - Systematic benchmarking and optimal strategy selection of cross-species integration methods
T2 - Briefings in bioinformatics
J2 - Brief Bioinform
PY - 2026
DA - 2026/09/01
VL - 27
IS - 5
SP - bbag490
SN - 1467-5463
PB - Oxford University Press
DO - 10.1093/bib/bbag490
UR - https://doi.org/10.1093/bib/bbag490
LA - en
ER -

CSL-JSON

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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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