OSCR

Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment.

Code ↔ Paper

10 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 10 matches
  1. [1] § Methods › Data analysis › Selection and ligand-receptor analysis of the Niche signal ↔ R_preprocess_update_selecte-targetCell_NicheCell.R, lines 100–164 · score 0.70 · r_dist_sqrt, radial distance, target cells, Niche
  2. [2] § Methods › Data analysis › Deconvolution analysis ↔ cell2location/models/_cell2location_WTA_module.py, lines 17–73 · score 0.68 · negative binomial distribution, Cell2location, reference cell, trained, signatures, modeling
  3. [3] § Methods › Data analysis › Deconvolution analysis ↔ cell2location/models/_cell2location_module.py, lines 17–73 · score 0.68 · negative binomial distribution, Cell2location, reference cell, trained, signatures, modeling
  4. [4] § Methods › Data analysis › Sequencing reads mapping and normalization ↔ Visium_split_tissue.R, lines 1–74 · score 0.60 · Load10X_Spatial, Seurat, barcode, transformation, tissue, positioned
  5. [5] § Methods › Data analysis › COMMOT spatial ligand-receptor analysis of Visium data ↔ Commot_on_Stage_P2.ipynb, lines 49–51 · score 0.58 · CellChat, ligand receptor, COMMOT, database, signal
  6. [6] § Methods › Data analysis › Section splitting ↔ Visium_split_tissue.R, lines 1–74 · score 0.57 · filter range, Seurat, split, tissue, positions, Visium
  7. [7] § Results › Generation of a spatial transcriptomic atlas using Visium and Slide-seq ↔ Deconvolution_Visium_spatial_P2.ipynb, lines 207–237 · score 0.56 · vas_ec, Epi, endocardial, vascular, fibroblasts, deconvolved
  8. [8] § Results › Identification of the signaling communications between conduction cells and their niche cells ↔ Deconvolution_Visium_spatial_P2.ipynb, lines 207–237 · score 0.53 · endo_ec, vas_ec, Slide
  9. [9] § Methods › Data analysis › Sequencing reads mapping and normalization ↔ cut_curio_SlideSeq_src.R, lines 1–49 · score 0.52 · curio Slide seq, Seurat, bead, filter
  10. [10] § Methods › Data analysis › Section splitting ↔ cut_curio_SlideSeq_src.R, lines 1–49 · score 0.52 · filter range, Seurat, cut, Curio, seq

Paper

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The authors' code

R · 121 lines · 5.3 KB · CC-BY-4.0 · 2 matches

  1. library(Seurat)
  2. library(SeuratData)
  3. library(ggplot2)
  4. library(patchwork)
  5. library(dplyr)
  6. library(spacexr)
  7. library(jsonlite)
  8. library(png)
  9. VisiumData<-read.VisiumSpatialRNA("location")
  10. spatial = Load10X_Spatial(data.dir = paste0("location"))
  11. barcodes <- colnames(VisiumData@counts)
  12. plot1 <- VlnPlot(spatial, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
  13. plot2 <- SpatialFeaturePlot(spatial, features = "nCount_Spatial") + theme(legend.position = "right")
  14. wrap_plots(plot1, plot2)
  17. orig_count = slide_seq@assays$RNA@counts
  19. ## read location file
  20. tissue.positions.path = "~/tissue_positions.csv"
  21. image.dir = "~/"
  22. tissue.positions <- read.csv(
  23. file = tissue.positions.path,
  24. col.names = c('barcodes', 'tissue', 'row', 'col', 'imagerow', 'imagecol'),
  25. header = ifelse(
  26. test = basename(tissue.positions.path) == "tissue_positions.csv",
  27. yes = TRUE,
  28. no = FALSE
  29. ),
  30. as.is = TRUE,
  31. row.names = 1
  32. )
  33. tissue.positions <- tissue.positions[which(x = tissue.positions$tissue == 1), , drop = FALSE]
  34. scale.factors <- fromJSON(txt = file.path(image.dir,'scalefactors_json.json'))
  35. image <- readPNG(source = file.path(image.dir, 'tissue_lowres_image.png'))
  36. unnormalized.radius <- scale.factors$fiducial_diameter_fullres * scale.factors$tissue_lowres_scalef
  37. spot.radius <- unnormalized.radius / max(dim(x = image))
  38. ## set filter range
  39. ## note SPATIAL_1 for Y axis SPATIAL_2 for X axis
  40. ## example for cutoff E12.5
  41. loc = locations[locations$SPATIAL_2 > 1590 & locations$SPATIAL_2 < 3990 & locations$SPATIAL_1>2000 & locations$SPATIAL_1 < 3940,]
  42. newcounts = slide_seq@assays$RNA@counts[,colnames(slide_seq@assays$RNA@counts) %in% rownames(loc)]
  43. object <- CreateSeuratObject(counts = data, assay = assay)
  44. image = new(
  45. Class = 'VisiumV1',
  46. image = image,
  47. scale.factors = scalefactors(
  48. spot = scale.factors$spot_diameter_fullres,
  49. fiducial = scale.factors$fiducial_diameter_fullres,
  50. hires = scale.factors$tissue_hires_scalef,
  51. scale.factors$tissue_lowres_scalef
  52. ),
  53. coordinates = tissue.positions,
  54. spot.radius = spot.radius
  55. )
  57. image <- image[Cells(x = object)]
  58. DefaultAssay(object = image) <- "SPATIAL"
  59. object[[slice]] <- image
  62. plot1 <- VlnPlot(spatial, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
  63. plot2 <- SpatialFeaturePlot(spatial, features = "nCount_Spatial") + theme(legend.position = "right")
  64. wrap_plots(plot1, plot2)
  66. spatial <- SCTransform(spatial, assay = "Spatial", verbose = FALSE)
  67. spatial <- RunPCA(spatial, assay = "SCT", verbose = FALSE)
  68. spatial <- FindNeighbors(spatial, reduction = "pca", dims = 1:30)
  69. spatial <- FindClusters(spatial, verbose = FALSE)
  70. spatial <- RunUMAP(spatial, reduction = "pca", dims = 1:30)
  72. spatial <- FindSpatiallyVariableFeatures(spatial, assay = "SCT", features = VariableFeatures(spatial)[1:1000],
  73. selection.method = "moransi")
  74. top.features <- head(SpatiallyVariableFeatures(spatial, method = "moransi"), 6)
  76. ##filter scales for other stages
  77. # SpatialFeaturePlot(spatial, features = top.features, ncol = 3, alpha = c(0.1, 1),pt.size.factor = 4)
  78. # SpatialDimPlot(spatial,cells.highlight = WhichCells(spatial, expression = slice1_imagerow > 305 & slice1_imagecol < 324))
  79. # group1 = subset(spatial, slice1_imagerow > 305 & slice1_imagecol < 324, invert = TRUE)
  80. # group2 = subset(spatial, slice1_imagerow > 305 & slice1_imagecol < 324, invert = FALSE)
  81. #
  82. # saveRDS(group1,"C:/Users/Haoting/Downloads/E17_E11/E17.rds")
  83. # saveRDS(group2,"C:/Users/Haoting/Downloads/E17_E11/E11.rds")
  84. #
  85. # SpatialDimPlot(spatial,cells.highlight = WhichCells(spatial, expression = slice1_imagerow > 300 & slice1_imagecol < 300))
  86. # group1 = subset(spatial, slice1_imagerow < 306 & slice1_imagecol > 265, invert = FALSE)
  87. # group2 = subset(spatial, slice1_imagerow < 306 & slice1_imagecol > 265, invert = TRUE)
  88. # saveRDS(group1,"C:/Users/Haoting/Downloads/E16/E16_tissue1.rds")
  89. # saveRDS(group2,"C:/Users/Haoting/Downloads/E16/E16_tissue2.rds")
  90. #
  91. #
  92. # SpatialDimPlot(spatial,cells.highlight = WhichCells(spatial, expression = slice1_imagerow > 269))
  93. # group1 = subset(spatial, slice1_imagerow > 269, invert = FALSE)
  94. # group2 = subset(spatial, slice1_imagerow < 269, invert = FALSE)
  95. # saveRDS(group1,"C:/Users/Haoting/Downloads/P0/P0_tissue1.rds")
  96. # saveRDS(group2,"C:/Users/Haoting/Downloads/P0/P0_tissue2.rds")
  97. #
  98. #
  99. # group1 = subset(spatial, slice1_imagerow < 287 & slice1_imagecol > 65, invert = FALSE)
  100. # group2 = subset(spatial, slice1_imagerow > 287 & slice1_imagecol > 65, invert = FALSE)
  101. # saveRDS(group1,"C:/Users/Haoting/Downloads/E11_5/E11_tissue1.rds")
  102. # saveRDS(group2,"C:/Users/Haoting/Downloads/E11_5/E11_tissue2.rds")
  103. #
  104. # group1 = subset(spatial, slice1_imagerow > 300 & slice1_imagecol < 300, invert = FALSE)
  105. # group2 = subset(spatial, slice1_imagerow > 300 & slice1_imagecol < 300, invert = TRUE)
  106. # saveRDS(group1,"C:/Users/Haoting/Downloads/E14_5/E14_tissue1.rds")
  107. # saveRDS(group2,"C:/Users/Haoting/Downloads/E14_5/E14_tissue2.rds")
  108. #
  109. #
  110. # group1 = subset(spatial, slice1_imagerow > 305 & slice1_imagecol < 265, invert = FALSE)
  111. # group2 = subset(spatial, slice1_imagerow > 265 & slice1_imagecol > 265, invert = FALSE)
  112. # group3 = subset(spatial, slice1_imagerow < 265, invert = FALSE)
  113. #
  114. # saveRDS(group1,"C:/Users/Haoting/Downloads/E14/E14_tissue1.rds")
  115. # saveRDS(group2,"C:/Users/Haoting/Downloads/E14/E14_tissue2.rds")

Visium_split_tissue.R, under CC-BY-4.0 · at the source

Overview

Authors: Junqi Hu1,2, Haoting He1, Juan Xu1, William A. MacDonald3, Yuanhang He1,2, Tianhao Liu2,4, Wei Chen4, Guang Li1
ORCID iDs: Juan Xu, William A. MacDonald, Guang Li
  1. Department of Cell Biology, University of Pittsburgh School of Medicine,Pittsburgh, PA USA
  2. Tsinghua Medicine, Tsinghua University,Beijing, China
  3. Health Sciences Sequencing Core, University of Pittsburgh School of Medicine,Pittsburgh, PA USA
  4. Department of Pediatrics, University of Pittsburgh,Pittsburgh, PA USA
Institutions: University of Pittsburgh (United States); Tsinghua University (China)
Journal: Communications biology, volume 9, issue 1, article 1032
Dates: received 19 December 2025; accepted 5 May 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10259-z · PMID 42141139 · PMCID PMC13429582 · OpenAlex W7161294153
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Evoked potentials
Keywords: Cell growth, Transcription
MeSH: Gene Expression Profiling*, Heart*, Signal Transduction*, Transcriptome*, Animals, Gene Expression Regulation, Developmental, Mice, Myocardium, Myocytes, Cardiac, Spatial Transcriptomics (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: HHS | NIH | National Heart, Lung, and Blood Institute (NHLBI) (R00HL133472)
Citations: cited by 1 paper (Europe PMC); 70 references in the paper
Research resources: RRID:SCR_022735, Rangos Research Center RRID:SCR_023116

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

BayraktarLab/cell2location

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: afb07fda7f89458e44a4afdf21c26aeb7251ebfa, 22 February 2026
Languages: Python (50), Jupyter (7), Shell (1)
Size: 107 files, 58 scripts
Software Heritage: not archived
Found in: the text, “Deconvolution analysis”
Holds: README, license file, environment (Dockerfile, environment.yml, pyproject.toml, setup.cfg, setup.py, docs/environment.yml), tests, continuous integration, documentation, 7 notebooks
Not found: CITATION.cff
Tools: NumPy (36 files), pandas (22 files), Matplotlib (20 files), Pyro (14 files), PyTorch (14 files), Scanpy (11 files), anndata (10 files), SciPy (10 files), seaborn (3 files), scikit-learn (2 files), PyTorch Lightning (1 file), OpenCV (1 file), UMAP (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
56 files
At the source: github.com/BayraktarLab/cell2location

Zenodo 14341430

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (5), Jupyter (3)
Size: 8 files, 8 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: 3 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (5 files), patchwork (4 files), Seurat (4 files), tidyverse (4 files), anndata (3 files), NumPy (3 files), pandas (3 files), Scanpy (3 files), data.table (2 files), Matplotlib (2 files), SciPy (1 file), seaborn (1 file), Squidpy (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
8 files
At the source:

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: Zenodo 14341430

Read it in the paper: doi.org/10.1038/s42003-026-10259-z.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 62 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-10259-z.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 2 keywords, 10 MeSH terms, 1 funder, 70 references, 2 RRIDs.

Cite

This paper

Hu, J., He, H., Xu, J., MacDonald, W. A., He, Y., Liu, T., Chen, W., & Li, G. (2026). Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment. Communications biology, 9(1), 1032. https://doi.org/10.1038/s42003-026-10259-z

BibTeX

@article{hu2026spatial,
author = {Hu, Junqi and He, Haoting and Xu, Juan and MacDonald, William A. and He, Yuanhang and Liu, Tianhao and Chen, Wei and Li, Guang},
title = {{Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1032},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10259-z},
url = {https://doi.org/10.1038/s42003-026-10259-z},
pmid = {42141139},
pmcid = {PMC13429582}
}

RIS

TY - JOUR
AU - Hu, Junqi
AU - He, Haoting
AU - Xu, Juan
AU - MacDonald, William A.
AU - He, Yuanhang
AU - Liu, Tianhao
AU - Chen, Wei
AU - Li, Guang
TI - Spatial transcriptomic profiling of developing mouse hearts reveals a spatially patterned signaling environment
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/15
VL - 9
IS - 1
SP - 1032
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10259-z
UR - https://doi.org/10.1038/s42003-026-10259-z
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Hu",
"given": "Junqi"
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15
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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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