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Latent Factor Modeling Reveals Unexpected Spatial Heterogeneity in Human Alzheimer's Disease Brain Transcriptomes.

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 › Single-sample GSEA ↔ supplementary/4.ST_WM_GSVA.qmd, lines 232–257 · score 0.61 · WM signature, ssGSEA, WM score, GSVA, RF, ROSMAP
  2. [2] § Methods › Random forest classifier ↔ supplementary/2.RF_score.qmd, lines 66–85 · score 0.53 · OOB accuracy, random forest, Model, MAP
  3. [3] § Methods › Discovery of the latent factor ↔ supplementary/1.DASC_multipleRun.qmd, lines 188–272 · score 0.51 · adjusted Rand, Pairwise, sensitivity, ranks, DASC, cluster

Paper

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The authors' code

Quarto · 378 lines · 12 KB · MIT · 1 match

  3. Calculate the correlation between RF classification probability for cluster 2 and WM scores using enriched WM siganture genes from supplementary table S4B, with those having t stats > 10
  5. ```{r setup}
  6. rm(list=ls())
  7. library(readxl)
  8. library(GSVA)
  9. library(ggplot2)
  10. library(dplyr)
  12. outDir <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/out/4.ST_WM_GSVA"
  13. dir.create(outDir, recursive = TRUE, showWarnings = FALSE)
  15. suppFile <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/data/supplementary_table.xlsx"
  16. countFile <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/data/AD/ROSMAP_AD-N/ROSMAP_AMPAD_AD-N_counts.txt"
  17. rfScoreFile <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/out/2.RF_score/ROSMAP_AMPAD_AD-N_meta_with_RF_scores.txt"
  18. rfValidationFile <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/out/2.RF_score/validation_summary.csv"
  19. rfPerclassFile <- "/home/zhijiany/workdir/2026/0402_AD_PaperReviews/out/2.RF_score/perclass_metrics.csv"
  21. theme_set(theme_bw())
  23. # cluster colors: 1=green, 2=blue, 3=red
  24. clus_colors <- c("1" = "#00BA38", "2" = "#619CFF", "3" = "#F8766D")
  25. ```
  27. ## Helper functions
  29. ```{r helpers}
  30. run_ssgsea_safe <- function(expr_mat, gene_sets) {
  31. if ("ssgseaParam" %in% getNamespaceExports("GSVA")) {
  32. param <- GSVA::ssgseaParam(exprData = expr_mat, geneSets = gene_sets,
  33. alpha = 0.25,
  34. normalize = TRUE)
  35. ssgsea_res <- GSVA::gsva(param, verbose = FALSE)
  36. } else {
  37. ssgsea_res <- GSVA::gsva(expr = expr_mat, gset.idx.list = gene_sets,
  38. method = "ssgsea",
  39. ssgsea.norm = TRUE,
  40. verbose = FALSE)
  41. }
  43. as.numeric(ssgsea_res[1, ])
  44. }
  46. compute_correlations <- function(df, cohort_name) {
  47. tmp <- df |>
  48. filter(!is.na(RF_score_clus2), !is.na(WM_ssGSEA_score))
  50. if (nrow(tmp) < 3 ||
  51. length(unique(tmp$RF_score_clus2)) < 2 ||
  52. length(unique(tmp$WM_ssGSEA_score)) < 2) {
  53. return(data.frame(
  54. cohort = cohort_name,
  55. n = nrow(tmp),
  56. spearman_rho = NA_real_,
  57. spearman_p = NA_real_,
  58. pearson_r = NA_real_,
  59. pearson_p = NA_real_,
  60. stringsAsFactors = FALSE
  61. ))
  62. }
  64. spearman_test <- cor.test(tmp$RF_score_clus2, tmp$WM_ssGSEA_score,
  65. method = "spearman", exact = FALSE)
  66. pearson_test <- cor.test(tmp$RF_score_clus2, tmp$WM_ssGSEA_score,
  67. method = "pearson")
  69. data.frame(
  70. cohort = cohort_name,
  71. n = nrow(tmp),
  72. spearman_rho = unname(spearman_test$estimate),
  73. spearman_p = spearman_test$p.value,
  74. pearson_r = unname(pearson_test$estimate),
  75. pearson_p = pearson_test$p.value,
  76. stringsAsFactors = FALSE
  77. )
  78. }
  80. fmt_p <- function(x) {
  81. ifelse(x < 2.2e-16, "<2.2e-16", formatC(x, format = "e", digits = 2))
  82. }
  84. compute_boxplot_stats <- function(df) {
  85. plot_df <- df |>
  86. filter(!is.na(RF_predicted_clus), !is.na(WM_ssGSEA_score)) |>
  87. mutate(RF_predicted_clus = factor(RF_predicted_clus))
  89. kw_test <- kruskal.test(WM_ssGSEA_score ~ RF_predicted_clus, data = plot_df)
  91. group_levels <- levels(plot_df$RF_predicted_clus)
  92. pair_df <- utils::combn(group_levels, 2, simplify = FALSE) |>
  93. lapply(function(pair) {
  94. pair_data <- plot_df |>
  95. filter(RF_predicted_clus %in% pair)
  96. pair_test <- wilcox.test(WM_ssGSEA_score ~ RF_predicted_clus, data = pair_data,
  97. exact = FALSE)
  98. data.frame(
  99. group1 = pair[[1]],
  100. group2 = pair[[2]],
  101. p_value = pair_test$p.value,
  102. stringsAsFactors = FALSE
  103. )
  104. }) |>
  105. bind_rows() |>
  106. mutate(
  107. xmin = match(group1, group_levels),
  108. xmax = match(group2, group_levels)
  109. )
  111. y_max <- max(plot_df$WM_ssGSEA_score, na.rm = TRUE)
  112. y_min <- min(plot_df$WM_ssGSEA_score, na.rm = TRUE)
  113. y_range <- y_max - y_min
  114. if (!is.finite(y_range) || y_range == 0) {
  115. y_range <- max(abs(y_max), 1)
  116. }
  117. step_height <- 0.08 * y_range
  118. tick_height <- 0.015 * y_range
  119. label_offset <- 0.02 * y_range
  121. pair_df <- pair_df |>
  122. arrange(xmin, xmax) |>
  123. mutate(
  124. y = y_max + step_height * seq_len(n()),
  125. tick_y = y - tick_height,
  126. label_y = y + label_offset,
  127. label = paste0("Wilcoxon p=", fmt_p(p_value))
  128. )
  130. list(
  131. plot_df = plot_df,
  132. kw_label = paste0(
  133. "Kruskal-Wallis chi-squared=", sprintf("%.2f", unname(kw_test$statistic)),
  134. ", p=", fmt_p(kw_test$p.value)
  135. ),
  136. y_min = y_min,
  137. kw_y = y_max + step_height * (nrow(pair_df) + 1.2),
  138. y_upper = y_max + step_height * (nrow(pair_df) + 2.2),
  139. pair_df = pair_df
  140. )
  141. }
  142. ```
  144. ## Load RF-scored metadata and counts
  146. ```{r load-data}
  147. metaDf <- read.table(rfScoreFile, header = TRUE, sep = "\t",
  148. stringsAsFactors = FALSE, check.names = FALSE)
  149. metaDf$sample_id <- sub("^X", "", metaDf$newId)
  151. countDf <- read.table(countFile, header = TRUE, sep = "\t",
  152. stringsAsFactors = FALSE, check.names = FALSE)
  153. rownames(countDf) <- countDf[, 1]
  154. countDf <- as.matrix(countDf[, -1])
  155. storage.mode(countDf) <- "numeric"
  157. stopifnot(all(metaDf$sample_id %in% colnames(countDf)))
  158. metaDf <- metaDf[match(colnames(countDf), metaDf$sample_id), ]
  159. stopifnot(all(metaDf$sample_id == colnames(countDf)))
  161. cat("Samples in metadata:", nrow(metaDf), "\n")
  162. cat("Samples in counts:", ncol(countDf), "\n")
  163. print(table(metaDf$study))
  164. ```
  166. ## Load WM signature from Supplementary Table S4B
  168. ```{r signature}
  169. wmSigRaw <- read_excel(suppFile, sheet = "Table S4B")
  170. wmSigRaw <- as.data.frame(wmSigRaw, stringsAsFactors = FALSE)
  172. wmSigDf <- wmSigRaw |>
  173. select(ensembl, gene, t_stat_WM, p_value_WM, fdr_WM) |>
  174. filter(!is.na(ensembl), !is.na(t_stat_WM), t_stat_WM > 10) |>
  175. arrange(desc(t_stat_WM))
  177. wmGenesAll <- unique(wmSigDf$ensembl)
  178. wmGenesPresent <- intersect(wmGenesAll, rownames(countDf))
  179. wmGenesMissing <- setdiff(wmGenesAll, rownames(countDf))
  181. cat("WM signature genes (t_stat_WM > 10):", length(wmGenesAll), "\n")
  182. cat("Present in counts:", length(wmGenesPresent), "\n")
  183. cat("Missing from counts:", length(wmGenesMissing), "\n")
  185. stopifnot(length(wmGenesAll) == 302)
  186. stopifnot(length(wmGenesPresent) == 299)
  187. stopifnot(length(wmGenesMissing) == 3)
  189. wmSigPresentDf <- wmSigDf |> filter(ensembl %in% wmGenesPresent)
  190. wmSigMissingDf <- wmSigDf |> filter(ensembl %in% wmGenesMissing)
  192. write.table(wmSigPresentDf,
  193. file = file.path(outDir, "WM_signature_genes_tstat_gt10_present.txt"),
  194. sep = "\t", row.names = FALSE, quote = FALSE)
  195. write.table(wmSigMissingDf,
  196. file = file.path(outDir, "WM_signature_genes_tstat_gt10_missing.txt"),
  197. sep = "\t", row.names = FALSE, quote = FALSE)
  199. summaryLines <- c(
  200. paste("WM signature genes with t_stat_WM > 10:", length(wmGenesAll)),
  201. paste("Present in counts:", length(wmGenesPresent)),
  202. paste("Missing from counts:", length(wmGenesMissing)),
  203. paste("Missing genes:", paste(wmSigMissingDf$gene, collapse = ", "))
  204. )
  205. writeLines(summaryLines, con = file.path(outDir, "WM_signature_summary.txt"))
  206. wmSigMissingDf
  207. ```
  209. ## Prepare expression matrix for ssGSEA
  211. The ROSMAP matrix is already continuous normalized expression, so we use it
  212. directly for ssGSEA rather than re-running count-based normalization. Because
  213. ssGSEA is based on within-sample gene ranks, we score the combined matrix once
  214. across all samples and keep the default ssGSEA normalization enabled
  215. (`normalize = TRUE` or `ssgsea.norm = TRUE`) so WM enrichment scores are on a
  216. more comparable cross-sample scale for visualization and downstream summaries.
  218. ```{r expression-matrix}
  219. exprMat <- countDf
  221. stopifnot(identical(colnames(exprMat), metaDf$sample_id))
  222. stopifnot(identical(rownames(exprMat), rownames(countDf)))
  224. normSummaryDf <- metaDf |>
  225. count(study, name = "n_samples")
  226. write.table(normSummaryDf, file = file.path(outDir, "cohort_sample_counts.txt"),
  227. sep = "\t", row.names = FALSE, quote = FALSE)
  229. summary(as.numeric(exprMat[, 1:5]))
  230. ```
  232. ## Compute WM ssGSEA score per sample
  234. ```{r ssgsea-score}
  235. wmScores <- run_ssgsea_safe(
  236. expr_mat = exprMat,
  237. gene_sets = list(WM_signature = wmGenesPresent)
  238. )
  240. gsvaDf <- data.frame(
  241. sample_id = colnames(exprMat),
  242. WM_ssGSEA_score = wmScores,
  243. stringsAsFactors = FALSE
  244. )
  246. resDf <- metaDf |>
  247. left_join(gsvaDf, by = "sample_id")
  249. stopifnot(sum(is.na(resDf$WM_ssGSEA_score)) == 0)
  250. stopifnot(sum(is.na(resDf$RF_score_clus2)) == 0)
  252. write.table(resDf,
  253. file = file.path(outDir, "ROSMAP_AMPAD_AD-N_meta_with_RF_and_ssGSEA_scores.txt"),
  254. sep = "\t", row.names = FALSE, quote = FALSE)
  256. summary(resDf$WM_ssGSEA_score)
  257. ```
  259. ## Correlation between ssGSEA WM score and RF cluster 2 score
  261. ```{r correlations}
  262. corSummaryDf <- bind_rows(
  263. compute_correlations(resDf, "Pooled"),
  264. compute_correlations(filter(resDf, study == "MAP"), "MAP"),
  265. compute_correlations(filter(resDf, study == "ROS"), "ROS")
  266. )
  268. corSummaryDf$label <- paste0(
  269. corSummaryDf$cohort,
  270. ": rho=", sprintf("%.3f", corSummaryDf$spearman_rho),
  271. ", p=", fmt_p(corSummaryDf$spearman_p),
  272. ", n=", corSummaryDf$n
  273. )
  275. write.table(corSummaryDf,
  276. file = file.path(outDir, "WM_ssGSEA_vs_RF_score_correlations.txt"),
  277. sep = "\t", row.names = FALSE, quote = FALSE)
  279. corSummaryDf
  280. ```
  282. ## Reviewer-facing figures
  284. ```{r figures}
  285. pooled_label <- corSummaryDf$label[corSummaryDf$cohort == "Pooled"]
  287. p_scatter <- ggplot(resDf, aes(x = RF_score_clus2, y = WM_ssGSEA_score, color = study)) +
  288. geom_point(size = 2.2, alpha = 0.8) +
  289. geom_smooth(method = "lm", se = FALSE, linewidth = 0.8) +
  290. geom_smooth(
  291. data = resDf,
  292. aes(x = RF_score_clus2, y = WM_ssGSEA_score),
  293. inherit.aes = FALSE,
  294. method = "lm", se = FALSE, color = "black",
  295. linetype = "dashed", linewidth = 0.9
  296. ) +
  297. annotate("text", x = Inf, y = -Inf, label = pooled_label,
  298. hjust = 1.05, vjust = -0.8, size = 3.7) +
  299. labs(
  300. title = "WM ssGSEA score tracks RF cluster 2 score",
  301. subtitle = "Colored lines: cohort-specific fits; dashed black line: pooled fit",
  302. x = "RF_score_clus2",
  303. y = "WM ssGSEA score",
  304. color = "Cohort"
  305. )
  307. ggsave(file.path(outDir, "WM_ssGSEA_vs_RF_score_scatter.pdf"),
  308. p_scatter, width = 5.8, height = 5.8)
  310. p_dist <- ggplot(resDf, aes(x = study, y = WM_ssGSEA_score, fill = study)) +
  311. geom_violin(trim = FALSE, alpha = 0.5, color = NA) +
  312. geom_boxplot(width = 0.18, outlier.shape = NA, alpha = 0.85) +
  313. labs(
  314. title = "Distribution of WM ssGSEA scores by cohort",
  315. x = "Cohort",
  316. y = "WM ssGSEA score",
  317. fill = "Cohort"
  318. ) +
  319. guides(fill = "none")
  321. ggsave(file.path(outDir, "WM_ssGSEA_score_by_cohort.pdf"),
  322. p_dist, width = 5.5, height = 5)
  324. clusterStatList <- compute_boxplot_stats(resDf)
  326. p_cluster <- ggplot(clusterStatList$plot_df,
  327. aes(x = RF_predicted_clus, y = WM_ssGSEA_score,
  328. fill = RF_predicted_clus)) +
  329. geom_boxplot(alpha = 0.8, outlier.shape = NA) +
  330. geom_jitter(width = 0.12, alpha = 0.35, size = 1.2) +
  331. geom_segment(data = clusterStatList$pair_df,
  332. aes(x = xmin, xend = xmax, y = y, yend = y),
  333. inherit.aes = FALSE, linewidth = 0.5) +
  334. geom_segment(data = clusterStatList$pair_df,
  335. aes(x = xmin, xend = xmin, y = tick_y, yend = y),
  336. inherit.aes = FALSE, linewidth = 0.5) +
  337. geom_segment(data = clusterStatList$pair_df,
  338. aes(x = xmax, xend = xmax, y = tick_y, yend = y),
  339. inherit.aes = FALSE, linewidth = 0.5) +
  340. geom_text(data = clusterStatList$pair_df,
  341. aes(x = (xmin + xmax) / 2, y = label_y, label = label),
  342. inherit.aes = FALSE, size = 3.1) +
  343. annotate("text", x = 2, y = clusterStatList$kw_y, label = clusterStatList$kw_label,
  344. size = 3.3) +
  345. coord_cartesian(ylim = c(clusterStatList$y_min, clusterStatList$y_upper)) +
  346. scale_fill_manual(values = clus_colors) +
  347. labs(
  348. title = "WM ssGSEA score by RF-predicted cluster",
  349. x = "RF-predicted cluster",
  350. y = "WM ssGSEA score",
  351. fill = "RF cluster"
  352. )
  354. ggsave(file.path(outDir, "WM_ssGSEA_score_by_RF_predicted_cluster.pdf"),
  355. p_cluster, width = 6.2, height = 5)
  357. p_scatter
  358. ```
  360. ## Existing RF validation metrics
  362. ```{r rf-validation}
  363. rfValidationDf <- read.csv(rfValidationFile, stringsAsFactors = FALSE)
  364. rfPerclassDf <- read.csv(rfPerclassFile, stringsAsFactors = FALSE)
  366. write.table(rfValidationDf,
  367. file = file.path(outDir, "RF_validation_summary_reused.txt"),
  368. sep = "\t", row.names = FALSE, quote = FALSE)
  369. write.table(rfPerclassDf,
  370. file = file.path(outDir, "RF_perclass_metrics_reused.txt"),
  371. sep = "\t", row.names = FALSE, quote = FALSE)
  373. rfValidationDf
  374. ```
  376. ```{r}
  377. rfPerclassDf
  378. ```

4.ST_WM_GSVA.qmd at commit 9af7a5d, under MIT · at the source

Overview

Authors: Rami Al-Ouran1,2,3, Chaozhong Liu2,3, Linhua Wang2,3, Zhijian Yu2,3, Ying-Wooi Wan2,3, Chaohao Gu2,3, Xiqi Li4, Gerarda Cappuccio2,3, Mirjana Maletic-Savatic2,3,4,5, Aleksandar Milosavljevic6, Joshua M. Shulman2,5,6,7,8, Hu Chen2,3, Zhandong Liu2,3
ORCID iDs: Rami Al-Ouran, Chaozhong Liu, Linhua Wang, Zhijian Yu, Gerarda Cappuccio, Aleksandar Milosavljevic, Hu Chen
  1. Department of Data Science and Artificial Intelligence, Al Hussein Technical University, Amman, Jordan
  2. Jan and Dan Duncan Neurologic Research Institute, Texas Children’s Hospital, Houston, TX 77030, USA
  3. Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
  4. Center for Drug Discovery, Baylor College of Medicine, Houston, TX 77030, USA
  5. Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
  6. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
  7. Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
  8. Center for Alzheimer’s and Neurodegenerative Diseases, Baylor College of Medicine, Houston, TX 77030, USA
Institutions: Baylor College of Medicine (United States); Texas Children's Hospital (United States); Al Hussein Technical University (Jordan)
Journal: Computational and structural biotechnology journal, volume 35, issue 1, article 0108
Dates: received 13 November 2025; accepted 21 April 2026; published online 14 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.34133/csbj.0108 · PMID 42146901 · PMCID PMC13172580 · OpenAlex W7155057420
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, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Ting Tsung and Wei Fong Chao Family Foundation; Huffington Foundation
Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

Alzheimer’s disease is characterized by complex molecular and cellular heterogeneity, which complicates efforts to identify consistent biomarkers and therapeutic targets. To better characterize the heterogeneity, we applied latent factor modeling to RNA sequencing data from approximately 2,500 human Alzheimer’s disease brain samples, uncovering underlying patterns in gene expression. These transcriptional groups demonstrated unique gene expression profiles related to synaptic and neuronal pathways, vasculature development, and protein folding and antigen processing. Notably, this latent factor reflects variation in spatial sampling. Adjusting for the latent factor improved the identification of differentially expressed genes in disease samples. This finding suggests that spatial heterogeneity is a pervasive driver of transcriptomic variation and has important implications for future studies of Alzheimer’s disease and related neurological disorders.

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

Repository

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LiuzLab/AD_LatentFactor

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 9af7a5d1792672956fe3592eb5f264c2960eae16, 6 April 2026
Languages: Quarto (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, license file, 4 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (4 files), tidyverse (3 files), clusterProfiler (1 file), limma (1 file), pheatmap (1 file), pROC (1 file), randomForest (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
6 files
At the source: github.com/LiuzLab/AD_LatentFactor

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

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Data

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Data Availability

Codes used in this study were available on github: https://github.com/LiuzLab/AD_LatentFactor.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 funders, 35 references.

Cite

This paper

Al-Ouran, R., Liu, C., Wang, L., Yu, Z., Wan, Y.-W., Gu, C., Li, X., Cappuccio, G., Maletic-Savatic, M., Milosavljevic, A., Shulman, J. M., Chen, H., & Liu, Z. (2026). Latent Factor Modeling Reveals Unexpected Spatial Heterogeneity in Human Alzheimer's Disease Brain Transcriptomes. Computational and structural biotechnology journal, 35(1), 0108. https://doi.org/10.34133/csbj.0108

BibTeX

@article{alouran2026latent,
author = {Al-Ouran, Rami and Liu, Chaozhong and Wang, Linhua and Yu, Zhijian and Wan, Ying-Wooi and Gu, Chaohao and Li, Xiqi and Cappuccio, Gerarda and Maletic-Savatic, Mirjana and Milosavljevic, Aleksandar and Shulman, Joshua M. and Chen, Hu and Liu, Zhandong},
title = {{Latent Factor Modeling Reveals Unexpected Spatial Heterogeneity in Human Alzheimer's Disease Brain Transcriptomes}},
journal = {Computational and structural biotechnology journal},
year = {2026},
month = may,
volume = {35},
number = {1},
pages = {0108},
publisher = {AAAS Science Partner Journal Program},
issn = {2001-0370},
doi = {10.34133/csbj.0108},
url = {https://doi.org/10.34133/csbj.0108},
pmid = {42146901},
pmcid = {PMC13172580}
}

RIS

TY - JOUR
AU - Al-Ouran, Rami
AU - Liu, Chaozhong
AU - Wang, Linhua
AU - Yu, Zhijian
AU - Wan, Ying-Wooi
AU - Gu, Chaohao
AU - Li, Xiqi
AU - Cappuccio, Gerarda
AU - Maletic-Savatic, Mirjana
AU - Milosavljevic, Aleksandar
AU - Shulman, Joshua M.
AU - Chen, Hu
AU - Liu, Zhandong
TI - Latent Factor Modeling Reveals Unexpected Spatial Heterogeneity in Human Alzheimer's Disease Brain Transcriptomes
T2 - Computational and structural biotechnology journal
J2 - Comput Struct Biotechnol J
PY - 2026
DA - 2026/05/14
VL - 35
IS - 1
SP - 0108
SN - 2001-0370
PB - AAAS Science Partner Journal Program
DO - 10.34133/csbj.0108
UR - https://doi.org/10.34133/csbj.0108
LA - en
ER -

CSL-JSON

{
"id": "10.34133/csbj.0108",
"type": "article-journal",
"title": "Latent Factor Modeling Reveals Unexpected Spatial Heterogeneity in Human Alzheimer's Disease Brain Transcriptomes",
"container-title": "Computational and structural biotechnology journal",
"author": [
{
"family": "Al-Ouran",
"given": "Rami"
},
{
"family": "Liu",
"given": "Chaozhong"
},
{
"family": "Wang",
"given": "Linhua"
},
{
"family": "Yu",
"given": "Zhijian"
},
{
"family": "Wan",
"given": "Ying-Wooi"
},
{
"family": "Gu",
"given": "Chaohao"
},
{
"family": "Li",
"given": "Xiqi"
},
{
"family": "Cappuccio",
"given": "Gerarda"
},
{
"family": "Maletic-Savatic",
"given": "Mirjana"
},
{
"family": "Milosavljevic",
"given": "Aleksandar"
},
{
"family": "Shulman",
"given": "Joshua M."
},
{
"family": "Chen",
"given": "Hu"
},
{
"family": "Liu",
"given": "Zhandong"
}
],
"container-title-short": "Comput Struct Biotechnol J",
"volume": "35",
"issue": "1",
"page": "0108",
"DOI": "10.34133/csbj.0108",
"PMID": "42146901",
"PMCID": "PMC13172580",
"ISSN": "2001-0370",
"publisher": "AAAS Science Partner Journal Program",
"URL": "https://doi.org/10.34133/csbj.0108",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
14
]
]
}
}

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