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RESCUE: recovery of unattributed expression patterns in spatial transcriptomics.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Tuning parameter selection ↔ R/RESCUE.R, lines 34–147 · score 0.60 · tuning procedure, Poisson, surrogate, rescaled, thresholding, error
  2. [2] § Methods › Optimization problem ↔ R/helper.R, lines 1–71 · score 0.58 · weight matrix, tuning parameter, iterative, residual, genes
  3. [3] § Methods › Platform effect normalization ↔ R/helper.R, lines 201–229 · score 0.58 · log scale, pseudo bulk, rescales, platform, cell, gene

Paper

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The authors' code

R · 229 lines · 8.4 KB · no license · 2 matches

  1. #' Weighted Non-negative Sparse Error Recovery
  2. #'
  3. #' Y_\{G x J\} ~= X_\{G x K\} alpha_\{K x J\} + R_\{G x J\}
  4. #'
  5. #' @param Y Gene x Grid (or Spot) cpm-normalized count matrix
  6. #' @param X Gene x Feature matrix: either (1) average expression matrix or (2) NMF feature matrix
  7. #' @param Y.nUMI vector of UMI counts of unnormalized Y
  8. #' @param C tuning parameter
  9. #' @param max.iter maximum number of iterations
  10. #' @param eps convergence tolerance
  11. #'
  12. #' @return
  13. #' \item{loss}{vector of loss function values at each iteration}
  14. #' \item{w}{estimated weight matrix (Gene x Grid)}
  15. #' \item{alpha.hat}{estimated cell type proportion matrix (Feature x Grid)}
  16. #' \item{R.hat}{estimated residual matrix (Gene x Grid)}
  17. #' \item{L.hat}{estimated cellness matrix (Gene x Grid)}
  18. #' @export
  19. wnn.ser <- function(Y, X, Y.nUMI, R.init = NULL, C=2, max.iter = 100, eps = 1e-4, verbose = FALSE){
  21. F2norm <- function(Y) {sqrt(sum(Y^2))}
  22. thr.op <- function(x,thr) {sign(x)*pmax(abs(x)-thr,0)}
  23. thresh.l1 <- function(x,thr) {thr.op(x,thr)}
  25. G <- nrow(Y); J <- ncol(Y); K <- ncol(X)
  26. R <- matrix(0,nrow=G,ncol=J); alpha <- matrix(0,nrow=K,ncol=J)
  27. mu = prod(dim(Y))/(C*sum(abs(Y))); imu <- 1/mu
  28. XtX <- crossprod(X)
  30. if(!is.null(R.init)) R <- R.init
  32. i <- 0; obj.cache <- 0; rel.cache <- c()
  34. while(TRUE){
  35. i <- i+1
  37. # weight update
  38. if(i==1){
  39. W = matrix(1, nrow=G, ncol=J)
  40. } else{
  41. W = pmax(sqrt(sweep(R, 2, Y.nUMI, "/") * 10^6), 1)
  42. }
  44. # alpha update
  45. YR <- Y-R; XtYR <- crossprod(X, YR)
  46. alpha <- sapply(1:J, function(j) RcppML::nnls(a=XtX, b=as.matrix(XtYR[,j]), fast_nnls=TRUE))
  48. # R update
  49. wimu <- ( (1/mu) / W ) # (1/mu) * (1/sqrt(Var(R_gj)))
  50. R <- thresh.l1((Y-X%*%alpha), wimu)
  51. R[Y==0] <- 0
  52. R <- pmax(R, 0)
  54. # results
  55. obj1 <- sum(abs(R/W))
  56. obj2 <- (mu/2) * F2norm((Y - X%*%alpha - R))^2
  57. obj <- obj1 + obj2
  59. obj.cache = c(obj.cache, obj)
  60. rel <- abs(obj - obj.cache[i]) / obj.cache[i]
  61. rel.cache <- c(rel.cache,rel)
  63. if (verbose) print(c(iter=i, obj=obj))
  64. if ((i > max.iter) || rel < eps)
  65. break;
  67. }
  69. result = list(loss=obj.cache[-1], W=W, alpha.hat=alpha, R.hat=R, L.hat=Y-R)
  70. return(result)
  71. }
  73. #' Naive Non-negative Least Squares (NNLS)
  74. #'
  75. #' Y_\{G x J\} ~= X_\{G x K\} alpha_\{K x J\} + R_\{G x J\}
  76. #'
  77. #' @param Y Gene x Grid (or Spot) cpm-normalized count matrix
  78. #' @param X Gene x Feature matrix: either (1) average expression matrix or (2) NMF feature matrix
  79. #' @param ncores number of cores to use
  80. #' @param chunk.size number of chunk to split
  81. #'
  82. #' @import parallel
  83. #' @import data.table
  84. #'
  85. #' @return
  86. #' \item{alpha.hat}{estimated cell type proportion matrix (Feature x Grid)}
  87. #' \item{resid}{estimated residual value matrix (Gene x Grid)}
  88. #' \item{resid.truncated}{non-negative truncated residual value matrix (Gene x Grid)}
  89. #'
  90. #' @export
  91. nnls.naive <- function(Y, X, ncores=NULL, chunk.size=NULL){
  93. # Fit NNLS per grids (parallel)
  94. if(is.null(ncores)) ncores <- detectCores()-1
  95. if(is.null(chunk.size)) chunk.size = round(ncol(Y) / 100)
  97. chunks = data.table(t(Y))
  98. chunks[, chunk := floor(.I / chunk.size)]
  99. chunks = split(chunks, by = "chunk", keep.by = FALSE)
  100. chunks = lapply(chunks, data.matrix)
  102. cl = makeCluster(ncores)
  103. clusterExport(cl, c("X"), envir = environment())
  105. f0 <- function(chunk) {
  106. apply(chunk, 1, function(y) {
  107. nnls::nnls(A=X, b=y)$x
  108. })
  109. }
  111. environment(f0) <- environment(nnls.naive)
  112. out = parLapply(cl, chunks, f0)
  113. stopCluster(cl)
  116. # results
  117. alpha <- matrix(unlist(out), nrow=ncol(X), ncol=ncol(Y))
  119. resid <- Y - X %*% alpha
  120. resid.truncated <- pmax(Y - X %*% alpha, 0)
  122. result = list(alpha.hat=alpha, resid=resid, resid.truncated=resid.truncated)
  123. return(result)
  125. }
  127. #' Modified get_cell_type_info() function from RCTD (Cable et al., Nature Biotechnology, 2022)
  128. #'
  129. #' @param raw.data a Digital Gene Expression matrix, with gene names as rownames and single cells as columns (barcodes for colnames)
  130. #' @param cell_types a named list of cell type assignment for each cell in \code{raw.data}
  131. #' @param nUMI a named list of total UMI count for each cell in \code{raw.data}
  132. #' @param cell_type_names a named list of cell types
  133. #'
  134. #' @return
  135. #' \item{cell_type_info}{a list of three elements: (1) \code{cell_type_means} (a data_frame (genes by cell types) for mean normalized expression) (2) \code{cell_type_names} (a list of cell type names) and (3) the number of cell types}
  136. #'
  137. #' @export
  138. mod.get_cell_type_info <- function(raw.data, cell_types, nUMI, cell_type_names = NULL) {
  139. if(is.null(cell_type_names))
  140. cell_type_names = levels(cell_types)
  142. n_cell_types = length(cell_type_names)
  144. get_cell_mean <- function(cell_type) {
  145. cell_type_data = raw.data[,cell_types == cell_type]
  146. cell_type_umi = nUMI[cell_types == cell_type]
  147. normData = sweep(cell_type_data,2,cell_type_umi,`/`)
  148. return(rowSums(normData) / dim(normData)[2])
  149. }
  151. cell_type = cell_type_names[1]
  152. cell_type_means <- data.frame(get_cell_mean(cell_type))
  153. colnames(cell_type_means)[1] = cell_type
  154. for (cell_type in cell_type_names[2:length(cell_type_names)]) {
  155. cell_type_means[cell_type] = get_cell_mean(cell_type)
  156. }
  157. return(list(cell_type_means, cell_type_names, n_cell_types))
  158. }
  160. #' Modified get_de_genes() function from RCTD (Cable et al., Nature Biotechnology, 2022)
  161. #'
  162. #' @param cell_type_info cell type information and profiles of each cell, calculated from the scRNA-seq reference (see \code{\link{get_cell_type_info}})
  163. #' @param Y.count Gene x Grid (or Spot) raw count matrix
  164. #' @param MIN_OBS the minimum number of occurrences of each gene in Y.count (Default: 10)
  165. #' @param fc_thresh minimum \code{log_e} fold change required for a gene (Default: 0.75)
  166. #' @param expr_thresh minimum expression threshold, as normalized expression (Default: 2e-04)
  167. #'
  168. #' @return
  169. #' \item{total_gene_list}{a list of differntially expressed gene names}
  170. #'
  171. #' @export
  172. mod.get_de_genes <- function(cell_type_info, puck, fc_thresh = 0.75, expr_thresh = 2e-04, MIN_OBS = 10) {
  173. total_gene_list = c()
  174. epsilon = 1e-9
  175. bulk_vec = rowSums(puck)
  176. gene_list = rownames(cell_type_info[[1]])
  177. prev_num_genes <- min(length(gene_list), length(names(bulk_vec)))
  178. gene_list = intersect(gene_list,names(bulk_vec))
  179. if(length(gene_list) == 0)
  180. stop("get_de_genes: Error: 0 common genes between SpatialRNA and Reference objects. Please check for gene list nonempty intersection.")
  181. gene_list = gene_list[bulk_vec[gene_list] >= MIN_OBS]
  182. if(length(gene_list) < 0.1 * prev_num_genes)
  183. stop("get_de_genes: At least 90% of genes do not match between the SpatialRNA and Reference objects. Please examine this. If this is intended, please remove the missing genes from the Reference object.")
  184. for(cell_type in cell_type_info[[2]]) {
  185. if(cell_type_info[[3]] > 2)
  186. other_mean = rowMeans(cell_type_info[[1]][gene_list,cell_type_info[[2]] != cell_type])
  187. else {
  188. other_mean <- cell_type_info[[1]][gene_list,cell_type_info[[2]] != cell_type]
  189. names(other_mean) <- gene_list
  190. }
  191. logFC = log(cell_type_info[[1]][gene_list,cell_type] + epsilon) - log(other_mean + epsilon)
  192. type_gene_list = which((logFC > fc_thresh) & (cell_type_info[[1]][gene_list,cell_type] > expr_thresh)) #| puck_means[gene_list] > expr_thresh)
  193. message(paste0("get_de_genes: ", cell_type, " found DE genes: ",length(type_gene_list)))
  194. total_gene_list = union(total_gene_list, type_gene_list)
  195. }
  196. total_gene_list = gene_list[total_gene_list]
  197. message(paste0("get_de_genes: total DE genes: ",length(total_gene_list)))
  198. return(total_gene_list)
  199. }
  201. #' Modified remove_platform_effect() function from RCTD (Cable et al., Nature Biotechnology, 2022)
  202. #'
  203. #' @param Y.count Gene x Grid (or Spot) raw count matrix
  204. #' @param X.count Gene x Cell raw count matrix from reference data
  205. #' @param pseudocount pseudo-counts to be added (Default: 1)
  206. #'
  207. #' @return
  208. #' \item{X.count_corrected}{Gene x Cell platform-effect-normalized count matrix}
  209. #'
  210. #' @export
  211. mod.remove_platform_effect <- function(
  212. Y.count,
  213. X.count,
  214. pseudocount = 1
  215. ) {
  216. # 1) Compute a pseudo-bulk sum for each gene in the ST data
  217. S_j <- rowSums(Y.count)
  219. # 2) Compute a pseudo-bulk sum for each gene in the reference
  220. R_j <- rowSums(X.count)
  222. # 3) Estimate a per-gene log-scale factor gamma_j.
  223. gamma_j <- log(S_j + pseudocount) - log(R_j + pseudocount)
  225. # 4) Rescale the reference by exp(gamma_j) on each gene
  226. X.count_corrected <- sweep(X.count, 1, exp(gamma_j), `*`)
  228. return(X.count_corrected)
  229. }

helper.R at commit 41e3e6b, no license · at the source

Overview

Authors: Young Joo Lee1, Seokjin Yeo2, Alex W Schrader3, JuYeon Lee3, Ian M Traniello4, Marisa Asadian3, Amy Cash Ahmed5, Gene E Robinson5,6,7, Hee-Sun Han2,3,5,6, Sihai Dave Zhao1,5
ORCID iDs: Alex W Schrader, Marisa Asadian, Gene E Robinson, Hee-Sun Han, Sihai Dave Zhao
  1. Department of Statistics, University of Illinois Urbana-Champaign, Urbana, IL USA
  2. Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL USA
  3. Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL USA
  4. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ USA
  5. Carl R. Woese Institute of Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL USA
  6. Neuroscience Program, University of Illinois Urbana-Champaign, Urbana, IL USA
  7. Department of Entomology, University of Illinois Urbana-Champaign, Urbana, IL USA
Institutions: University of Illinois Urbana-Champaign (United States); Princeton University (United States); Carl R. Woese Institute for Genomic Biology (United States)
Journal: Nature communications, volume 17, issue 1, article 5096
Dates: received 17 September 2025; accepted 26 March 2026; published online 10 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71720-5 · PMID 41963343 · PMCID PMC13247165 · OpenAlex W7152968373
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), other (organism), cellular / molecular (subfield)
Methods: Smoothing, state filtering, decompositions, Statistics
Keywords: Statistical methods, Computational models
MeSH: Computational Biology*, Gene Expression Profiling*, Spatial Transcriptomics*, Transcriptome*, Animals, Bees, Brain, In Situ Hybridization, Fluorescence (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: U.S. Department of Health &amp; Human Services | National Institutes of Health (R21HG013180, R01AT013189, R35GM147420); NCCIH NIH HHS (R01 AT013189); U.S. Department of Health & Human Services | National Institutes of Health (NIH) (R35GM147420, R01AT013189, R21HG013180); NIGMS NIH HHS (R35 GM147420); NHGRI NIH HHS (R21 HG013180)
Citations: cited by 1 paper (Europe PMC); 89 references in the paper

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 3 matches between paragraphs and lines of code.

brunoyjlee/RESCUE

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 41e3e6b56b25e31945200eaf0f005812d5ceaf90, 11 March 2026
Languages: R (2)
Size: 18 files, 2 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (DESCRIPTION), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: data.table (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
3 files
At the source: github.com/brunoyjlee/RESCUE

LieberInstitute/spatialLIBD

License: Artistic-2.0
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Commit: aeac846c90bb81d09b97838913194c25bffec876, 28 September 2026
Languages: R (104)
Size: 216 files, 104 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 6 notebooks
Not found: CITATION.cff
Tools: tidyverse (8 files), SingleCellExperiment (7 files), limma (4 files), cowplot (3 files), circlize (2 files), ComplexHeatmap (2 files), ggplot2 (2 files), edgeR (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
106 files
At the source: github.com/LieberInstitute/spatialLIBD

Zenodo 18965449

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
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Size: 1 file
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Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
3 files
At the source:

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-71720-5.

Tracing map

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  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 108 scripts, each with its path and the digest of its content;
  • 3 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

Code and data availability statement

The paper has a code and 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/s41467-026-71720-5.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 8 MeSH terms, 5 funders, 84 references.

Cite

This paper

Lee, Y. J., Yeo, S., Schrader, A. W., Lee, J., Traniello, I. M., Asadian, M., Ahmed, A. C., Robinson, G. E., Han, H.-S., & Zhao, S. D. (2026). RESCUE: recovery of unattributed expression patterns in spatial transcriptomics. Nature communications, 17(1), 5096. https://doi.org/10.1038/s41467-026-71720-5

BibTeX

@article{lee2026rescue,
author = {Lee, Young Joo and Yeo, Seokjin and Schrader, Alex W and Lee, JuYeon and Traniello, Ian M and Asadian, Marisa and Ahmed, Amy Cash and Robinson, Gene E and Han, Hee-Sun and Zhao, Sihai Dave},
title = {{RESCUE: recovery of unattributed expression patterns in spatial transcriptomics}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5096},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71720-5},
url = {https://doi.org/10.1038/s41467-026-71720-5},
pmid = {41963343},
pmcid = {PMC13247165}
}

RIS

TY - JOUR
AU - Lee, Young Joo
AU - Yeo, Seokjin
AU - Schrader, Alex W
AU - Lee, JuYeon
AU - Traniello, Ian M
AU - Asadian, Marisa
AU - Ahmed, Amy Cash
AU - Robinson, Gene E
AU - Han, Hee-Sun
AU - Zhao, Sihai Dave
TI - RESCUE: recovery of unattributed expression patterns in spatial transcriptomics
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/10
VL - 17
IS - 1
SP - 5096
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71720-5
UR - https://doi.org/10.1038/s41467-026-71720-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-71720-5",
"type": "article-journal",
"title": "RESCUE: recovery of unattributed expression patterns in spatial transcriptomics",
"container-title": "Nature communications",
"author": [
{
"family": "Lee",
"given": "Young Joo"
},
{
"family": "Yeo",
"given": "Seokjin"
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{
"family": "Schrader",
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"given": "Amy Cash"
},
{
"family": "Robinson",
"given": "Gene E"
},
{
"family": "Han",
"given": "Hee-Sun"
},
{
"family": "Zhao",
"given": "Sihai Dave"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "5096",
"DOI": "10.1038/s41467-026-71720-5",
"PMID": "41963343",
"PMCID": "PMC13247165",
"ISSN": "2041-1723",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
10
]
]
}
}

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