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SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data.

Code ↔ Paper

12 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 12 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] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 197–259 · score 0.72 · regulatory networks, scSeqComm, transcription factor, TF, cell interactions, downstream
  2. [2] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 197–259 · score 0.72 · regulatory networks, scSeqComm, transcription factor, TF, cell interactions, downstream
  3. [3] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 262–332 · score 0.68 · scSeqComm_analyze, intracellular signal, cellular responses, go, ligand receptor, interactions
  4. [4] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 262–328 · score 0.68 · scSeqComm_analyze, intracellular signal, cellular responses, go, ligand receptor, interactions
  5. [5] § Results › Overview of SpaNiche ↔ R/spaniche_nmf_celltype_and_gene.R, lines 1–57 · score 0.63 · graph regularized joint, abundance matrix, matrix factorization, expression matrix, computational, SpaNiche
  6. [6] § Results › Overview of SpaNiche ↔ R/spaniche_nmf_celltype_and_gene.R, lines 1–57 · score 0.63 · graph regularized joint, abundance matrix, matrix factorization, expression matrix, computational, SpaNiche
  7. [7] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 262–332 · score 0.62 · intracellular signaling, TF association, PPR, quantifying, GO, genes
  8. [8] § Methods › Inference of pathways regulated by cell–cell interactions ↔ R/spaniche_nmf_comm.R, lines 262–328 · score 0.62 · intracellular signaling, TF association, PPR, quantifying, GO, genes
  9. [9] § Results › Overview of SpaNiche ↔ R/spaniche_nmf_celltype_and_gene.R, lines 1–57 · score 0.59 · graph regularized joint, abundance matrix, expression matrix, Visium, computational, view1
  10. [10] § Results › Overview of SpaNiche ↔ R/spaniche_nmf_celltype_and_gene.R, lines 1–57 · score 0.59 · graph regularized joint, abundance matrix, expression matrix, Visium, computational, view1
  11. [11] § Methods › Joint NMF with graph regularization ↔ R/nmf_modified.R, lines 1–27 · score 0.58 · spatial smoothness, objective function, modified, optimization, components, weights
  12. [12] § Methods › Joint NMF with graph regularization ↔ R/calculate_laplacian_matrix_with_gausskernel.R, the whole file · a weak match · score 0.56 · graph Laplacian, weighted adjacency matrix

Paper

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The authors' code

R · 332 lines · 16 KB · other · 3 matches

  1. #' spaniche_nmf_comm: Spatial integrative NMF of cell-type abundance
  2. #' and ligand–receptor interaction matrices, with downstream pathway inference
  3. #'
  4. #' @param spatial.seu ST data is processed as a Seurat object with 'Spatial' as the assay name. Seurat object is normalized by using the 'NormalizeData' function. The order of spots must be consistent with spatialdf.
  5. #' @param spatialdf a data frame or matrix that contains two columns, "col" and "row". Row names must be
  6. #' spot barcodes and consistent with \code{spot_by_celltype}.
  7. #' @param spot_by_celltype a data frame or matrix representing the abundance of cell types, with spots as rows and cell types as columns. Its row names should be consistent with the row names of 'spatialdf'.
  8. #' @param smoothing_type Character vector specifying the spatial smoothing
  9. #' levels to include. Supported values are:
  10. #' \itemize{
  11. #' \item \code{"view0"}: no smoothing (original matrix)
  12. #' \item \code{"view1"}: first-order spatial neighbors
  13. #' \item \code{"view2"}: second-order spatial neighbors
  14. #' }
  15. #' Valid combinations are \code{c("view0")},
  16. #' \code{c("view0","view1")}, and
  17. #' \code{c("view0","view1","view2")}.
  18. #' As it involves the merging of matrices, the returned matrix's dimension varies depending on the chosen option. The dimensions corresponding to the three options are 1x, 2x, and 3x of the original dimensions, respectively.
  19. #' @param distance_thre This parameter represents distance, defining the central distance from center to loop1 (view1), and from center to loop2 (view2). For view0, the distance is ignored and should be set to NA. The length of the vector is consistent with the smoothing_type parameter. For example, A typical choice for Visium data is: distance_thre = c(NA,round(2/(3^0.5),2),round(4/(3^0.5),2)).
  20. #' @param digits integer indicating the number of decimal places.
  21. #' @param LRDB This parameter represents the reference for cell-cell interactions, which comes from CellChat.
  22. #' @param LR_dm_type This parameter can be one or more values from c('view1', 'view2'), representing the type of adjacency matrix to be returned.
  23. #' @param LR_distance_thre A numeric vector specifying distance thresholds. If only \code{"view1"} is requested, a single value is sufficient, defining the distance from center to first-order neighbors (view1). If \code{"view1"} + \code{"view2"} is requested, two values are required, defining the distance from center to first-order neighbors (view1) and from center to second-order neighbors (view2).
  24. #' @param var_thre Variance threshold
  25. #' @param topic_num An integer specifying the number of components or topics to be extracted. It determines the number of columns in the W matrix and the number of rows in each H matrix.
  26. #' @param defined_weight Either \code{"default"} to use data-driven modality
  27. #' weights, or a numeric vector of length two specifying user-defined weights
  28. #' for the cell-type and LR matrices.
  29. #' @param lambda_v Vector of spatial regularization parameters controlling the
  30. #' strength of Laplacian smoothness.
  31. #' @param sigma_v Gaussian kernel parameter. Used in the computation of the Laplacian matrix, influencing how spatial information is incorporated into the factorization.
  32. #' @param maxiter The maximum number of iterations allowed for the factorization process. This acts as a stopping criterion to prevent the algorithm from running indefinitely.
  33. #' @param st.count Convergence counter. If the change in the reconstruction error remains below epsilon for st.count consecutive iterations, the algorithm stops, assuming it has converged.
  34. #' @param epsilon Threshold on the relative change in the objective function used to assess convergence. If the relative change in error is less than epsilon for st.count consecutive iterations, convergence is assumed.
  35. #' @param topic_lr_quantile_thre A threshold for quantile selection. Only data above this quantile will be considered for visualization. Default is 0.95.
  36. #' @param spot_topic.quantile.thre Quantile threshold defining topic-high and
  37. #' topic-low spot groups (default: top and bottom 20 %).
  38. #' @param S_intra_thre S_intra_thre
  39. #' @param term_topn Number of top enriched functional terms retained for visualization.
  40. #' @param num_core Number of CPU cores used for parallel computation in downstream scSeqComm analysis.
  41. #'
  42. #' @return A list containing:
  43. #' \itemize{
  44. #' \item \code{LRintegratedmatrix}: Filtered spatial LR interaction matrix.
  45. #' \item \code{nmf_res}: Results from spatially regularized integrative NMF.
  46. #' \item \code{go_res}: GO enrichment results for topic-specific signaling. Only terms with a p-value < 0.01 are retained.
  47. #' \item \code{go_plot}: Visualization of enriched functional pathways. No p-value filtering is applied; the top "term_topn" terms are plotted.
  48. #' }
  49. #' @import Seurat
  50. #' @import tidyverse
  51. #' @import IntNMF
  52. #' @import scales
  53. #' @import scSeqComm
  54. #' @export
  55. spaniche_nmf_comm = function(
  56. ## Basic inputs
  57. spatial.seu,
  58. spatialdf,
  59. spot_by_celltype,
  60. smoothing_type = c('view0', 'view1', 'view2'),
  61. distance_thre = c(NA,2/(3^0.5),4/(3^0.5)),
  62. digits = 2,
  63. ## Parameters related to ligand–receptor interactions
  64. LRDB = SpaNiche::CellChatDB.human,
  65. LR_dm_type = c("view1", "view2"),
  66. LR_distance_thre = c(2/(3^0.5),4/(3^0.5)),
  67. var_thre = c(0.2,0.99),
  68. ## Parameters related to joint matrix factorization
  69. topic_num = 15,
  70. defined_weight = "default",
  71. lambda_v =c(0.5, 1, 2),
  72. sigma_v = c(0.5, 1, 1.5),
  73. maxiter = 200,
  74. st.count = 20,
  75. epsilon = 1e-04,
  76. # Downstream cellular responses
  77. topic_lr_quantile_thre = 0.95,
  78. spot_topic.quantile.thre = 0.2,
  79. S_intra_thre = 0.8,
  80. term_topn = 20,
  81. num_core = 1
  82. ){
  83. distance_thre = round(distance_thre,digits)
  84. LR_distance_thre = round(LR_distance_thre,digits)
  87. # Ensure consistent spot ordering
  88. if(identical(rownames(spatialdf),colnames(spatial.seu))){
  89. print("The spot order is consistent between spatialdf and spatial.seu.")
  90. } else {
  91. spatialdf = spatialdf[colnames(spatial.seu),]
  92. print("The spot order between spatialdf and spatial.seu is inconsistent. Adjust the spot order of spatialdf.")
  93. }
  95. if(identical(rownames(spatialdf),rownames(spot_by_celltype))){
  96. print("The spot order is consistent between spatialdf and spot_by_celltype.")
  97. } else {
  98. spot_by_celltype=spot_by_celltype[rownames(spatialdf),]
  99. print("The spot order between spatialdf and spot_by_celltype is inconsistent. Adjust the spot order of spot_by_celltype.")
  100. }
  102. # Construct spatially extended cell-type enrichment matrix
  103. spot.ct.df = get_spot_by_celltype_extended(
  104. spatialdf = spatialdf,
  105. spot_by_celltype = spot_by_celltype,
  106. smoothing_type = smoothing_type,
  107. distance_thre = distance_thre,
  108. digits = digits
  109. )
  110. write.csv(spot.ct.df,file = "spot_by_celltype_extended.csv",quote = F,row.names = T)
  112. ### Part II: Construction of ligand–receptor integrated matrix
  113. step1_res=prepare_for_LRintegratedmatrix(spatial.seu,LRDB)
  114. distance_matrix = make_distance_matrix_LR(spatialdf = spatialdf,dm_type = LR_dm_type,distance_thre = LR_distance_thre,digits = digits)
  115. LRelementmatrix = step1_res[[1]]
  116. cc_interaction = step1_res[[2]]
  118. # Receptor expression within each spot
  119. rmat = LRelementmatrix %>% t() %>% .[,cc_interaction$receptor]
  120. # Ligand expression within each spot
  121. lmat = LRelementmatrix %>% t() %>% .[,cc_interaction$ligand]
  122. # NOTE: This section may be generalized in the future; currently assumes four LR configurations (m1–m4).
  123. m1=generate_LRintegratedmatrix(whoisneighbor = distance_matrix$view1,neighbor_geneexp = rmat,center_geneexp = lmat,center_is_ligand = T)
  124. m2=generate_LRintegratedmatrix(whoisneighbor = distance_matrix$view1,neighbor_geneexp = lmat,center_geneexp = rmat,center_is_ligand = F)
  125. colnames(m1)=paste0(colnames(m1)," (view1)")
  126. colnames(m2)=paste0(colnames(m2)," (view1)")
  128. m3=generate_LRintegratedmatrix(whoisneighbor = distance_matrix$view2,neighbor_geneexp = rmat,center_geneexp = lmat,center_is_ligand = T)
  129. m4=generate_LRintegratedmatrix(whoisneighbor = distance_matrix$view2,neighbor_geneexp = lmat,center_geneexp = rmat,center_is_ligand = F)
  130. colnames(m3)=paste0(colnames(m3)," (view2)")
  131. colnames(m4)=paste0(colnames(m4)," (view2)")
  133. LRintegratedmatrix=m1 %>% cbind(m2) %>% cbind(m3) %>% cbind(m4)
  134. LRintegratedmatrix[is.na(LRintegratedmatrix)] = 0
  135. saveRDS(LRintegratedmatrix,file = "LRintegratedmatrix.rds")
  136. rm(list = paste0("m",1:4))
  138. ### Compute Moran's I and annotate ligand–receptor interactions ###
  139. moransi_output_df = moransi_for_LRintegratedmatrix(t(LRintegratedmatrix),spatial.seu,cc_interaction)
  140. saveRDS(moransi_output_df,file = "LRintegratedmatrix_with_moransi.rds")
  141. print("Moran's index calculation completed")
  143. ### Part III: Joint factorization of cell-type and LR interaction matrices
  144. LRintegratedmatrix = LRintegratedmatrix[,colSums(LRintegratedmatrix) > 0]
  145. colnames(LRintegratedmatrix)=colnames(LRintegratedmatrix) %>% str_replace_all("_","-")
  146. LRintegratedmatrix=LRintegratedmatrix[spot.ct.df %>% rownames(),]
  148. # Variance-based filtering of LR interactions
  149. var.df1 = LRintegratedmatrix %>% apply(2, function(x){var(x)}) %>% as.data.frame()
  150. colnames(var.df1) = "var_value"
  151. var.df1$interaction = rownames(var.df1)
  152. var.df1 = var.df1 %>% arrange(var_value)
  153. var.df1$index = 1:length(var.df1$var_value)
  154. var_thre_1 = quantile(var.df1$var_value,var_thre[1])
  155. var_thre_2 = quantile(var.df1$var_value,var_thre[2])
  156. var.df1 = var.df1 %>% filter(var_value > var_thre_1 & var_value < var_thre_2)
  157. used_interaction = var.df1$interaction
  158. used_interaction = intersect(colnames(LRintegratedmatrix),used_interaction)
  159. LRintegratedmatrix=LRintegratedmatrix[,used_interaction]
  160. print("Filtering the LR integrated matrix based on variance and selecting some LR pairs.")
  162. # to matrix
  163. spot.ct.df = as.matrix(spot.ct.df)
  164. LRintegratedmatrix = as.matrix(LRintegratedmatrix)
  165. # Estimate initial modality weights prior to running IntNMF
  166. dat <- list(spot.ct.df,LRintegratedmatrix)
  167. theta_v = define_initial_weight(dat)
  168. # The function nmf.mnnals requires the samples to be on rows and variables on columns.
  169. fit <- nmf.mnnals(dat=dat,k=topic_num,maxiter=50,st.count=10,n.ini=3,ini.nndsvd=TRUE,seed=TRUE,wt = theta_v)
  170. # Determine refined modality weights
  171. if (length(defined_weight) == 2 & all(defined_weight > 0)) {
  172. rho_v = define_good_weight(X = dat,Wzero = fit$W,Hzero = fit$H,theta_v = theta_v,weight_input = defined_weight)
  173. } else if (length(defined_weight) == 1 & defined_weight == "default") {
  174. rho_v = theta_v
  175. } else {
  176. stop("defined_weight error")
  177. }
  178. print("Weights for different matrices have been determined, now starting joint factorization of the two matrices:")
  180. # Joint matrix factorization
  181. myfit = nmf_modified(
  182. dat = dat,
  183. Wzero = fit$W,
  184. Hzero = fit$H,
  185. wt = rho_v,
  186. k=topic_num,
  187. lambda = lambda_v,
  188. sigma = sigma_v,
  189. maxiter = maxiter,
  190. st.count = st.count,
  191. spatialdf = as.matrix(spatialdf[,c("row","col")]),
  192. epsilon = epsilon
  193. )
  194. saveRDS(myfit,file = "nmf_modified_fit.rds")
  195. print("joint factorization has been completed.")
  197. ### Part IV: Linking latent topics to downstream cellular responses
  198. spot_topic = as.data.frame(myfit$W)
  199. colnames(spot_topic) = paste0("topic",1:topic_num)
  200. allSB = rownames(spot_topic)
  202. gene_expr_matrix = spatial.seu@assays$Spatial@data
  203. gene_expr_matrix = as.matrix(gene_expr_matrix)
  204. gene_expr_matrix = gene_expr_matrix[rowSums(gene_expr_matrix) > 0,]
  206. tmpplot = plot_topic_lr(
  207. topic_lr = t(myfit$H$H2),
  208. topic_lr_quantile_thre = topic_lr_quantile_thre
  209. )
  210. topic_lr_small_file = dir(getwd(),"topic_lr_small_.*csv")
  211. topic_lr_small = read.csv(topic_lr_small_file)
  213. # Color palette for topic visualization
  214. color_cluster = hue_pal()(topic_num)
  215. names(color_cluster) = paste0("topic",1:topic_num)
  217. # Objects to be exported
  218. plot.list=list()
  219. all.go = data.frame()
  221. print("Exploring pathway changes due to cell interactions:")
  223. for (ti in colnames(spot_topic)) {
  224. tmpst = spot_topic[,ti]
  225. tmpquan = quantile(tmpst,c(spot_topic.quantile.thre,1 - spot_topic.quantile.thre))
  226. low.index = which(tmpst <= tmpquan[1])
  227. high.index = which(tmpst > tmpquan[2])
  228. lowSB = allSB[low.index]
  229. highSB = allSB[high.index]
  232. ########## Load scRNA-seq data
  233. tmp_gene_expr_matrix = gene_expr_matrix[,c(highSB,lowSB)]
  234. tmp_anno = data.frame(
  235. "Cell_ID" = c(highSB,lowSB),
  236. "Cluster_ID" = c(
  237. rep(paste(ti,"high",sep = "_"),length(highSB)),
  238. rep(paste(ti,"low",sep = "_"),length(lowSB))
  239. )
  240. )
  241. ########## Ligand-receptor pairs
  242. tmp_topic_lr = topic_lr_small %>% filter(topic == ti)
  243. tmp_topic_lr$lr = str_remove(tmp_topic_lr$LRinteraction," .*$")
  244. tmp_topic_lr$r = ifelse(str_detect(tmp_topic_lr$lr,">"),
  245. str_remove(tmp_topic_lr$lr,"^.*>"),
  246. str_remove(tmp_topic_lr$lr,"<.*$"))
  247. tmp_topic_lr$l = ifelse(str_detect(tmp_topic_lr$lr,">"),
  248. str_remove(tmp_topic_lr$lr,"->.*$"),
  249. str_remove(tmp_topic_lr$lr,"^.*<-"))
  250. tmp_topic_lr=tmp_topic_lr[,c("l","r")] %>% unique()
  251. colnames(tmp_topic_lr) = c("ligand","receptor")
  253. tmp_topic_lr$ligand = str_replace(tmp_topic_lr$ligand,"^HLA-","xxx") %>% str_replace("-",",") %>% str_replace("xxx","HLA-")
  254. tmp_topic_lr$receptor = str_replace(tmp_topic_lr$receptor,"-",",")
  255. LR_db = tmp_topic_lr
  256. ########## Transcriptional regulatory networks
  257. TF_TG_db <- scSeqComm::TF_TG_TRRUSTv2_HTRIdb_RegNetwork_High
  258. ########## Receptor-Transcription factor a-priori association
  259. TF_PPR <- scSeqComm::TF_PPR_KEGG_human
  262. ########## Identify and quantify intercellular and intracellular signaling
  263. scSeqComm_res <- scSeqComm_analyze(gene_expr = tmp_gene_expr_matrix,
  264. cell_metadata = tmp_anno,
  265. inter_signaling = F,
  266. LR_pairs_DB = LR_db,
  267. TF_reg_DB = TF_TG_db,
  268. R_TF_association = TF_PPR,
  269. N_cores = num_core,
  270. DEmethod = "wilcoxon")
  273. ### Downstream cellular responses induced by ligand–receptor interactions
  274. topic_high_comm = dplyr::filter(
  275. scSeqComm_res$comm_results,
  276. cluster == paste(ti,"high",sep = "_") & S_intra >= S_intra_thre
  277. )
  278. topic_high_comm = as.data.frame(topic_high_comm) #20260426
  279. if (dim(topic_high_comm)[1] < 2) {next} #20231018
  281. # GO analysis of topic_high communication
  282. geneUniverse <- unique(unlist(scSeqComm_res$TF_reg_DB_scrnaseq))
  283. cell_functional_response <- scSeqComm_GO_analysis(
  284. results_signaling = topic_high_comm,
  285. geneUniverse = geneUniverse,
  286. method = "general")
  288. # plot
  289. cell_functional_response$pval = as.numeric(cell_functional_response$pval)
  290. cell_functional_response = cell_functional_response %>% arrange(pval)
  291. cell_functional_response$pval_log10_neg = -log10(cell_functional_response$pval)
  292. cell_functional_response$cluster = ti
  293. all.go = rbind(all.go,cell_functional_response %>% filter(pval < 0.01))
  294. tmpres = cell_functional_response %>% slice_head(n = term_topn)
  296. tmpres=tmpres%>%arrange(pval_log10_neg)
  297. tmpres$Term=factor(tmpres$Term,levels = tmpres$Term)
  299. tmpbar = tmpres %>% ggplot(aes(x=Term,y=pval_log10_neg))+
  300. geom_hline(yintercept = -log10(0.01),color = "black",alpha=0.7)+
  301. geom_bar(stat="identity",alpha=0.8,aes(fill=cluster))+
  302. geom_text(mapping = aes(x=Term,y=0,label=Term),hjust=0)+
  303. scale_x_discrete("")+
  304. scale_y_continuous("-log10(p value)",expand = c(0.02,0))+
  305. scale_fill_manual(values = color_cluster)+
  306. coord_flip()+
  307. labs(title = ti)+
  308. theme_bw()+
  309. theme(
  310. panel.grid = element_blank(),
  311. axis.ticks.y = element_blank(),
  312. axis.text.y = element_blank(),
  313. axis.text.x.bottom = element_text(color = "black"),
  314. legend.position = "none",
  315. plot.title = element_text(hjust = 0.5,size = 20)
  316. )
  318. index=which(ti == colnames(spot_topic))
  319. plot.list[[get("index")]]=tmpbar
  320. }
  321. print("Completed.")
  323. output = list(
  324. LRintegratedmatrix=LRintegratedmatrix,
  325. nmf_res=myfit,
  326. go_res=all.go, # p-values filtered with threshold pval < 0.01
  327. go_plot=plot.list # no p-value filtering, only the top "term_topn" terms are plotted
  328. )
  330. print("Output all results.")
  331. return(output)
  332. }

spaniche_nmf_comm.R at commit 84a43cf, under other · at the source

Overview

Authors: Siyuan Huang1, Qinghua Ran1, Junjie Tang2,3, Xiaochen Wang4, Junqing Xi5, Shiyang Ma6,7, Ruibin Xi1,3,4
ORCID iDs: Junjie Tang
  1. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871 China
  2. Present Address: Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA USA
  3. Center for Statistical Science, Peking University, Beijing, 100871 China
  4. School of Mathematical Sciences, Peking University, Beijing, 100871 China
  5. Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021 China
  6. Institute of Clinical Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 China
  7. School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240 China
Institutions: Peking University (China); Chinese Academy of Medical Sciences & Peking Union Medical College (China); Cancer Hospital of Chinese Academy of Medical Sciences (China); Shanghai Jiao Tong University (China)
Journal: Genome biology, volume 27, issue 1, article 182
Dates: received 8 September 2025; accepted 2 April 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s13059-026-04069-z · PMID 42015285 · PMCID PMC13231777 · OpenAlex W7155045354
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Connectivity, Machine learning, fMRI & imaging
MeSH: Spatial Transcriptomics*, Transcriptome*, Alzheimer Disease, Cerebral Cortex, Colorectal Neoplasms, Humans, Prostatic Neoplasms (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Sichuan Science and Technology Program (2025YFHZ0069); National Natural Science Foundation of China (12425110); National Key Research and Development Program of China (2023YFC3603200, 2024YFF0507404); Shanghai Municipal Commission of Education (JWAIYB-3); Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM118); Fundamental Research Funds for the Central Universities (YG2023QNA01); Clinical Research Project of Shanghai Municipal Health Commission in Health Industry (20234Y0285); Chengdu Municipal Science and Technology Program (2024-YF05-01784-SN); Shanghai Rising Star Program (23YF1421000)
Citations: not cited yet (Europe PMC); 72 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.

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SiyuanHuang1/SpaNiche

License: other
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 84a43cf0e77e49f84acf66eb130b45612234bb12, 2 June 2026
Languages: R (21)
Size: 69 files, 21 scripts
Software Heritage: not archived
Found in: the references
Holds: README, license file, environment (DESCRIPTION), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: tidyverse (13 files), Seurat (5 files), patchwork (2 files), reshape2 (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
24 files
At the source: github.com/SiyuanHuang1/SpaNiche

Zenodo 19179447

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (13 files), Seurat (5 files), patchwork (2 files), reshape2 (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
24 files
At the source:

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

Tracing map

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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;
  • 42 scripts, each with its path and the digest of its content;
  • 12 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

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/10.1186/s13059-026-04069-z.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 7 MeSH terms, 9 funders, 59 references.

Cite

This paper

Huang, S., Ran, Q., Tang, J., Wang, X., Xi, J., Ma, S., & Xi, R. (2026). SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data. Genome biology, 27(1), 182. https://doi.org/10.1186/s13059-026-04069-z

BibTeX

@article{huang2026spaniche,
author = {Huang, Siyuan and Ran, Qinghua and Tang, Junjie and Wang, Xiaochen and Xi, Junqing and Ma, Shiyang and Xi, Ruibin},
title = {{SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data}},
journal = {Genome biology},
year = {2026},
month = apr,
volume = {27},
number = {1},
pages = {182},
publisher = {BMC},
issn = {1474-7596},
doi = {10.1186/s13059-026-04069-z},
url = {https://doi.org/10.1186/s13059-026-04069-z},
pmid = {42015285},
pmcid = {PMC13231777}
}

RIS

TY - JOUR
AU - Huang, Siyuan
AU - Ran, Qinghua
AU - Tang, Junjie
AU - Wang, Xiaochen
AU - Xi, Junqing
AU - Ma, Shiyang
AU - Xi, Ruibin
TI - SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data
T2 - Genome biology
J2 - Genome Biol
PY - 2026
DA - 2026/04/21
VL - 27
IS - 1
SP - 182
SN - 1474-7596
PB - BMC
DO - 10.1186/s13059-026-04069-z
UR - https://doi.org/10.1186/s13059-026-04069-z
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13059-026-04069-z",
"type": "article-journal",
"title": "SpaNiche: spatial niche analysis to explore colocalization patterns and cellular interactions in spatial transcriptomics data",
"container-title": "Genome biology",
"author": [
{
"family": "Huang",
"given": "Siyuan"
},
{
"family": "Ran",
"given": "Qinghua"
},
{
"family": "Tang",
"given": "Junjie"
},
{
"family": "Wang",
"given": "Xiaochen"
},
{
"family": "Xi",
"given": "Junqing"
},
{
"family": "Ma",
"given": "Shiyang"
},
{
"family": "Xi",
"given": "Ruibin"
}
],
"container-title-short": "Genome Biol",
"volume": "27",
"issue": "1",
"page": "182",
"DOI": "10.1186/s13059-026-04069-z",
"PMID": "42015285",
"PMCID": "PMC13231777",
"ISSN": "1474-7596",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s13059-026-04069-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
21
]
]
}
}

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

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