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scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq.

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] § Implementation › Adaptive parameter selection › Feature extraction from fitted trajectories ↔ scTrends-Test.R, lines 60–129 · score 0.59 · scPS, autocorrelation, CV, ratio, variance, slopes
  2. [2] § Results › Automatic, dataset-specific calibration of scTrends parameters ↔ scTrends-Test.R, lines 60–129 · score 0.55 · CV threshold, scPS, autocorrelation, slope, prominence, cells
  3. [3] § Results › Pseudotime analysis and binning ↔ scTrends-Test.R, lines 1–58 · score 0.52 · CD4 TCM, UMAP, PCA, PBMC, positioned, cell

Paper

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The authors' code

R · 237 lines · 7 KB · CC-BY-4.0 · 3 matches

  1. library(SeuratData)
  2. library(Seurat)
  3. library(scTrends)
  4. AvailableData()
  5. data("pbmcMultiome")
  6. data("pbmc3k", package = "SeuratData")
  7. pbmc3k=UpdateSeuratObject(pbmc3k)
  8. table(pbmc3k$seurat_annotations)
  9. SeuratData::InstallData("pbmcMultiome")
  10. PBMC=pbmc.rna
  11. table(PBMC$seurat_annotations)
  12. DimPlot(PBMC)
  13. PBMC <- FindVariableFeatures(PBMC,selection.method = "vst", nfeatures = 3000)
  14. PBMC <- ScaleData(PBMC,verbose = T)
  15. PBMC <- RunPCA(PBMC,npcs = 50, verbose = T)
  16. PBMC=RunUMAP(PBMC,dim = 1:50)
  17. Idents(PBMC)="seurat_annotations"
  18. sceT=subset(PBMC, idents=c("CD4 Naive","CD4 TCM"))
  19. sceT <- ScaleData(sceT,verbose = T)
  20. sceT <- RunPCA(sceT,npcs = 50, verbose = T)
  21. sceT=RunUMAP(sceT,dim = 1:50)
  22. DimPlot(sceT)
  23. print(sceT)
  24. sceT=PBMC
  25. #Figure 2A
  26. CellDimPlot(
  27. sceT,
  28. group.by = "seurat_annotations",
  29. reduction = "UMAP",
  30. palcolor = c("#FB9A99","#B2DF8A"),
  31. pt.size = 2,
  32. label = F,
  33. label.size= 5,
  34. theme_use = "theme_blank",
  35. legend.position = "none"
  36. )
  37. library(CytoTRACE2)
  40. cytotrace2 <- cytotrace2(sceT,
  41. is_seurat = TRUE,
  42. slot_type = "counts",
  43. species = "human",
  44. seed =5201314)
  46. df=[email hidden]
  48. df$cellID=row.names(df)
  49. UMAP=cytotrace2@reductions[["umap"]]@cell.embeddings
  50. UMAP=as.data.frame(UMAP)
  51. UMAP$cellID=row.names(UMAP)
  52. head(df)
  53. # 替换df$cellID中的"."为"-"
  54. df$cellID <- gsub("\\.", "-", df$cellID)
  57. df$UMAP_1 <- UMAP[df$cellID, "umap_1"]
  58. df$UMAP_2 <- UMAP[df$cellID, "umap_2"]
  60. head(df)
  61. #Figure 2B
  62. plotthis::FeatureDimPlot(df, dims = 11:12,features = c("CytoTRACE2_Relative"), palcolor = (RColorBrewer::brewer.pal(11, "Spectral")),
  64. pt_size = 2,theme = "theme_blank",hex = TRUE,hex_bins = 30,theme_args = list(
  65. legend.text = element_text(size = 20), # 设置图注文本字体大小
  66. legend.title = element_text(size = 14) # 设置图注标题字体大小
  67. ))
  69. result=scBin(
  70. cytotrace2,
  71. pseudotime_col="CytoTRACE2_Relative",
  72. assay = "RNA",
  73. slot = "data",
  74. n_bins = 20,
  75. overlap_ratio = 0,
  76. pseudotime_order ="decreasing",
  77. x_range = c(0, 1),
  78. filter_genes = TRUE,
  79. max_zero_bins_pct = 0,
  80. min_expression = 0,
  81. min_cells_per_bin = 20,
  82. verbose = TRUE
  83. )
  84. bin_means <- result$bin_means
  85. x <- result$x
  86. head(bin_means)
  87. str(bin_means)
  88. mat20 <- bin_means[1:20,]
  89. #Figure 2c
  90. pheatmap(
  91. mat20,
  92. scale = "row",
  93. show_rownames = TRUE,
  94. show_colnames = TRUE,
  95. cluster_rows = FALSE,
  96. cluster_cols = FALSE,
  97. border_color = "white",
  98. fontsize = 12,
  99. fontsize_row = 20,
  100. fontsize_col = 20,
  101. color = colorRampPalette(
  102. rev(brewer.pal(n = 11, name = "Spectral"))
  103. )(100)
  104. )
  105. ps <- scPS(result, test_genes =9702, sample_method = "none")
  107. Tcell=scTrends(result,
  108. min_cells = 4,
  109. peak_position_margin = 0.15,
  110. max_sign_changes_mono = 1,
  111. gam_k = 8,
  112. stable_cv_threshold = 0.168,
  113. stable_range_threshold = 0.100,
  114. stable_autocorr_threshold = 0.750,
  115. min_peak_prominence = 0.200,
  116. min_slope_ratio = 0.100,
  117. total_change_up = 0.991,
  118. total_change_down = -0.793,
  119. monotone_consistency = 0.750,
  120. min_sign_changes_complex = 3,
  121. complex_variance_threshold = 0.350,
  122. compute_pvalue = T,
  123. n_perm = 1000,
  124. adjust_method = "BH",
  125. alpha = 0.05,
  126. n_cores = 16,
  127. use_parallel = TRUE,
  128. return_details = FALSE,
  129. verbose = TRUE)
  132. data=Tcell$results
  133. write.xlsx(data,"E:/足细胞应激/函数/data.xlsx")
  134. #Figure 3A-F
  135. plot_trends(
  136. trend_result = data,
  137. bin_means = bin_means,
  138. x = x,
  139. trend_type = "Complex",
  140. max_genes = 30,
  141. sig_metric = "p_value",
  142. sig_cutoff = 1,
  143. show_individual = TRUE,
  144. show_mean = TRUE,
  145. show_se = TRUE,
  146. individual_color = "gray80",
  147. individual_alpha = 0.3,
  148. individual_size = 0.5,
  149. mean_color ="#999999",
  150. mean_size = 1.5,
  151. se_fill = "#F5D2A8",
  152. se_alpha = 0.3,
  153. se_scale = 1.96
  154. )+
  155. labs(
  156. title = "Down-Up (Top 30)",
  157. x = "Pseudotime (0 = Early, 1 = Late)",
  158. y = "Expression Level"
  159. ) +
  160. theme_minimal() +
  161. theme(
  162. plot.title = element_text(face = "bold", size = 24, hjust = 0.5),
  163. axis.title = element_blank(),
  164. axis.text = element_text(size = 20),
  166. panel.grid.major = element_line(color = "gray90", linewidth = 0.3),
  167. panel.grid.minor = element_blank(),
  168. panel.border = element_rect(color = "black", fill = NA, linewidth = 1),
  169. panel.background = element_rect(fill = "white", color = NA))+coord_cartesian(
  170. xlim = c(0, 1),
  171. ylim = c(0, 0.5),
  172. expand = FALSE
  173. )
  174. colors <- c(
  175. "#FB9A99", "#377EB8", "#4DAF4A", "#F781BF", "#BC9DCC", "#A65628", "#54B0E4", "#F5D2A8",
  176. "#222F75","#B2DF8A", "#E3BE00", "#E7298A", "#E41A1C", "#D2EBC8", "#00CDD1",
  177. "#5E4FA2", "#8CA77B", "#1B9E77","#8DD3C7", "#7DBFC7", "#B3DE69", "#999999", "#FCED82",
  178. "#F5CFE4", "#F5D2A8", "#B383B9", "#EE934E", "#BBDD78"
  179. )
  180. #Figure 3G
  181. my_genes=c("LEF1","TCF7","CD27","CD28")
  182. plot_genes(bin_means, x, genes = my_genes, color = colors, size = 1.2, alpha = 0.5) +
  183. labs(
  184. x = "Pseudotime (0 = Early, 1 = Late)",
  185. y = "Expression Level"
  186. ) +
  187. theme_minimal() +
  188. theme(
  189. plot.title = element_text(face = "bold", size = 24, hjust = 0.5),
  190. axis.title = element_text(size = 24),
  191. axis.text = element_text(size = 24),
  192. legend.text = element_text(size = 24),
  193. legend.title = element_text(size = 24),
  194. panel.grid.major = element_line(color = "gray90", linewidth = 0.3),
  195. panel.grid.minor = element_blank(),
  196. legend.position = "right",
  197. panel.border = element_rect(color = "black", fill = NA, linewidth = 1),
  198. panel.background = element_rect(fill = "white", color = NA)
  199. ) +
  200. coord_cartesian(
  201. xlim = c(0, 1),
  202. ylim = c(0, 5),
  203. expand = FALSE
  204. ) +
  206. guides(color = guide_legend(override.aes = list(size = 5)))
  208. genes_remove <- c("SCAMP2", "FAM13A", "RAD51-AS1", "MRPL38", "IDH3B")
  210. top_genes <- top_genes %>%
  211. filter(!gene %in% genes_remove)
  212. top_genes <- data %>%
  213. group_by(trend) %>%
  214. arrange(p_value) %>%
  215. slice_head(n = 5) %>%
  216. ungroup()
  217. genes_use <- top_genes$gene
  218. bin_sub <- bin_means[genes_use, ]
  220. #Figure 3H
  221. pheatmap(
  222. bin_sub,
  223. scale = "row",
  224. show_rownames = TRUE,
  225. show_colnames = TRUE,
  226. cluster_rows = FALSE,
  227. cluster_cols = FALSE,
  228. border_color = "white",
  229. fontsize = 12,
  230. fontsize_row = 24,
  231. fontsize_col = 20,
  232. color = colorRampPalette(
  233. rev(brewer.pal(n = 11, name = "Spectral"))
  234. )(100)
  235. )
  237. saveRDS(sceT,"E:/足细胞应激/函数/投稿/PBMC.rds")

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

Overview

Authors: Jianbo Qing1, Jiaying Hu1, Xiao Wang2, Junnan Wu1
  1. Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine,Hangzhou, 310000 China
  2. Core Laboratory, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine,Hangzhou, 310000 China
Institutions: Zhejiang University (China)
Journal: BMC genomics, volume 27, issue 1, article 649
Dates: received 30 January 2026; accepted 20 May 2026; published online 30 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12864-026-12987-2 · PMID 42218405 · PMCID PMC13435386 · OpenAlex W7162852431
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), cellular / molecular (subfield)
Keywords: scTrends, Single-cell, Pseudotime, Gene trends, Automatic classification
MeSH: Gene Expression Profiling*, RNA-Seq*, Single-Cell Analysis*, Software*, Transcriptome*, Algorithms, Animals, Humans, Mice, Single-Cell Gene Expression Analysis (* major topic)
Journal subjects: Software
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (82370717); Key Program of the Natural Science Foundation of Zhejiang Province (LZ23H050001)
Citations: not cited yet (Europe PMC); 41 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

figshare 32325834

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 4 files, 1 script
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.6084/m9.figshare.32325834

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

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Code and data availability statement

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Read it in the paper: doi.org/10.1186/s12864-026-12987-2.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 10 MeSH terms, 2 funders, 41 references.

Cite

This paper

Qing, J., Hu, J., Wang, X., & Wu, J. (2026). scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq. BMC genomics, 27(1), 649. https://doi.org/10.1186/s12864-026-12987-2

BibTeX

@article{qing2026sctrends,
author = {Qing, Jianbo and Hu, Jiaying and Wang, Xiao and Wu, Junnan},
title = {{scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq}},
journal = {BMC genomics},
year = {2026},
month = may,
volume = {27},
number = {1},
pages = {649},
publisher = {BMC},
issn = {1471-2164},
doi = {10.1186/s12864-026-12987-2},
url = {https://doi.org/10.1186/s12864-026-12987-2},
pmid = {42218405},
pmcid = {PMC13435386}
}

RIS

TY - JOUR
AU - Qing, Jianbo
AU - Hu, Jiaying
AU - Wang, Xiao
AU - Wu, Junnan
TI - scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq
T2 - BMC genomics
J2 - BMC Genomics
PY - 2026
DA - 2026/05/30
VL - 27
IS - 1
SP - 649
SN - 1471-2164
PB - BMC
DO - 10.1186/s12864-026-12987-2
UR - https://doi.org/10.1186/s12864-026-12987-2
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12864-026-12987-2",
"type": "article-journal",
"title": "scTrends: automated classification and strength quantification of gene expression trends along pseudotime in single-cell RNA-seq",
"container-title": "BMC genomics",
"author": [
{
"family": "Qing",
"given": "Jianbo"
},
{
"family": "Hu",
"given": "Jiaying"
},
{
"family": "Wang",
"given": "Xiao"
},
{
"family": "Wu",
"given": "Junnan"
}
],
"container-title-short": "BMC Genomics",
"volume": "27",
"issue": "1",
"page": "649",
"DOI": "10.1186/s12864-026-12987-2",
"PMID": "42218405",
"PMCID": "PMC13435386",
"ISSN": "1471-2164",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12864-026-12987-2",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
30
]
]
}
}

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