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Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood in Huntington's disease.

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The authors' code

R · 186 lines · 9.5 KB · no license

  1. ##' @import Biobase
  2. ##' @importFrom plyr rbind.fill
  3. ##' @import methods
  4. ##' @include AllGenerics.R
  5. NULL
  8. Mandatory_Featurevars <- character()
  9. Mandatory_Cellvars <- character()
  11. ##' @import SingleCellExperiment
  12. ##' @import S4Vectors
  13. ##' @importMethodsFrom S4Vectors mcols
  14. ##' @importMethodsFrom SummarizedExperiment colData assays assay
  15. setClass('SingleCellAssay', contains='SingleCellExperiment',
  16. slots=list(cmap='character', fmap='character'),
  17. prototype=list(cmap=Mandatory_Cellvars,
  18. fmap=Mandatory_Featurevars))
  20. Fluidigm_Cellvars <- c(Mandatory_Cellvars, ncells='ncells')
  21. setClass('FluidigmAssay', contains='SingleCellAssay', prototype=list(cmap=Fluidigm_Cellvars))
  25. ## Classes
  26. ##' Linear Model-like Class
  27. ##'
  28. ##' Wrapper around modeling function to make them behave enough alike that Wald tests and Likelihood ratio are easy to do.
  29. ##' To implement a new type of zero-inflated model, extend this class.
  30. ##' Depending on how different the method is, you will definitely need to override the \code{fit} method, and possibly the \code{model.matrix}, \code{model.matrix<-}, \code{update}, \code{coef}, \code{vcov}, and \code{logLik} methods.
  31. ##'
  32. ##' @section Slots:
  33. ##' \describe{
  34. ##' \item{design}{a data.frame from which variables are taken for the right hand side of the regression}
  35. ##' \item{fitC}{The continuous fit}
  36. ##' \item{fitD}{The discrete fit}
  37. ##' \item{response}{The left hand side of the regression}
  38. ##' \item{fitted}{A \code{logical} with components "C" and "D", TRUE if the respective component has converged}
  39. ##' \item{formula}{A \code{formula} for the regression}
  40. ##' \item{fitArgsC}{}
  41. ##' \item{fitArgsD}{Both \code{list}s giving arguments that will be passed to the fitter (such as convergence criteria or case weights)}
  42. ##' }
  43. ##' @seealso coef
  44. ##' @seealso lrTest
  45. ##' @seealso waldTest
  46. ##' @seealso vcov
  47. ##' @seealso logLik
  48. setClass('LMlike',
  49. slots=c(design='ANY', modelMatrix='matrix', fitC='ANY', fitD='ANY', response='ANY', fitted='logical', formula='formula', fitArgsD='list', fitArgsC='list', priorVar='numeric', priorDOF='numeric',
  50. ## this speeds construction of coef and vcov, which is a pinch point in zlm
  51. defaultCoef='numeric',
  52. defaultVcov='matrix'),
  53. prototype=list(fitted =c(C=FALSE, D=FALSE), formula=formula(0~0),modelMatrix=matrix(nrow=0, ncol=0), priorVar=0, priorDOF=0), validity=function(object){
  54. stopifnot( all(c("C", "D") %in% names(object@fitted)))
  55. if(length(object@response)>0){
  56. if(any(is.na(object@response))) stop('NAs not permitted in response')
  57. if(length(object@response)!=nrow(object@design)) stop('Response length differs from design length')
  58. ##if(nrow(object@design) != nrow(object@modelMatrix)) stop('Design length differs from model.matrix length')
  59. }
  60. })
  62. ##' Wrapper for regular glm/lm
  63. ##'
  64. ##' @slot weightFun function to map expression values to probabilities of expression. Currently unused.
  65. setClass('GLMlike', contains='LMlike', slots=c(weightFun='function'), prototype=list(weightFun=function(x){
  66. ifelse(x>0, 1, 0)
  67. }))
  69. ##' Initialize a prior to be used a prior for BayeGLMlike/BayesGLMlike2
  70. ##'
  71. ##' @param names character vector of coefficients. The `(Intercept)` will be ignored.
  72. ##' @return 3d array, with leading dimension giving the prior 'loc'ation, 'scale' and degrees of freedom (df),
  73. ##' second dimension giving the component ('C'ontinuous or 'D'iscrete)
  74. ##' and trailing dimension giving the coefficient to which the prior applies.
  75. ##' The location is initialized to be 0, the scale to 2, and degrees of freedom of 1, following the default of bayesglm.
  76. ##' @export
  77. ##' @examples
  78. ##' dp <- defaultPrior('Stim.ConditionUnstim')
  79. ##' \dontrun{
  80. ##' data(vbetaFA)
  81. ##' zlmVbeta <- zlm(~ Stim.Condition, vbetaFA, method='bayesglm', coefPrior=dp)
  82. ##' }
  83. defaultPrior <- function(names){
  84. names <- setdiff(names, '(Intercept)')
  85. p <- length(names)
  86. ar <- array(rep(c(0, 2.5, 1), times=2*p), dim=c(3, 2,p), dimnames=list(metric=c('loc', 'scale', 'df'), comp=c('C', 'D'), names))
  87. #if(p>0) ar['scale',,names=='(Intercept)'] <- 10
  88. ar
  89. }
  92. ##' Wrapper for bayesian GLM
  93. ##'
  94. ##' @slot prior \code{numeric} optional 3d array used to specify prior for coefficients
  95. ##' @slot useContinuousBayes \code{logical} should \code{bayesglm} be used to fit the continuous component as well?
  96. setClass('BayesGLMlike', contains='GLMlike', slots=c(coefPrior='array', useContinuousBayes='logical'),
  97. prototype=list(coefPrior=defaultPrior(character(0)), useContinuousBayes=FALSE),
  98. validity=function(object){
  99. ## if(length(object@coefPrior>0))
  100. ## if(dim(object@coefPrior)[3] != sum(colnames(model.matrix(object))!='(Intercept)')) stop('prior must have same number of components as model.matrix')
  101. TRUE
  102. })
  103. setClass('BayesGLMlikeWeight', contains='BayesGLMlike')
  106. ##' Wrapper for lmer/glmer
  107. ##'
  108. ##' A horrendous hack is employed in order to do arbitrary likelihood ratio tests: the model matrix is built, the names possibly mangled, then fed in as a symbolic formula to glmer/lmer.
  109. ##' This is necessary because there is no (easy) way to specify an arbitrary fixed-effect model matrix in glmer.
  110. ##' @slot pseudoMM part of this horrendous hack.
  111. ##' @slot strictConvergence \code{logical} (default: \code{TRUE}) return results even when the optimizer or *lmer complains about convergence
  112. ##' @slot optimMsg \code{character} record warnings from lme. \code{NA_character_} means no warnings.
  113. setClass('LMERlike', contains='LMlike', slots=c(pseudoMM='data.frame',
  114. optimMsg='character',
  115. strictConvergence='logical'),
  116. validity=function(object){
  117. if(length(object@response)>0 & nrow(object@pseudoMM)>0){
  118. stopifnot(nrow(object@pseudoMM)==length(object@response))
  119. }
  120. if(object@priorDOF!=0) stop('Empirical bayes shrinkage not implemented for lmer/glmer.')
  121. },
  122. prototype=list(strictConvergence=TRUE, optimMsg=c(C=NA_character_, D=NA_character_))
  123. )
  125. setClass('bLMERlike', contains='LMERlike')
  127. setClass('ConstrainedGLMlike', contains='LMlike')
  128. setClass('RidgeBGLMlike',contains="BayesGLMlike",slots=c(lambda='numeric'),prototype = list(lambda=0.1) )
  130. ## Ways to specify hypothesis
  131. setClass('Hypothesis', contains='character', slots=list(contrastMatrix='matrix'))
  132. setClass('CoefficientHypothesis', contains='Hypothesis', slots=list(index='numeric'))
  134. ##' An S4 class to hold the output of a call to zlm
  135. ##'
  136. ##' This holds output from a call to zlm. Many methods are defined to operate on it. See below.
  137. ##' @slot coefC matrix of continuous coefficients
  138. ##' @slot coefD matrix of discrete coefficients
  139. ##' @slot vcovC array of variance/covariance matrices for coefficients
  140. ##' @slot vcovD array of variance/covariance matrices for coefficients
  141. ##' @slot LMlike the LmWrapper object used
  142. ##' @slot sca the \code{SingleCellAssay} object used
  143. ##' @slot deviance matrix of deviances
  144. ##' @slot loglik matrix of loglikelihoods
  145. ##' @slot df.null matrix of null (intercept only) degrees of freedom
  146. ##' @slot df.resid matrix of residual DOF
  147. ##' @slot dispersion matrix of dispersions (after shrinkage)
  148. ##' @slot dispersionNoShrink matrix of dispersion (before shrinkage)
  149. ##' @slot priorDOF shrinkage weight in terms of number of psuedo-obs
  150. ##' @slot priorVar shrinkage target
  151. ##' @slot converged output that may optionally be set by the underlying modeling function
  152. ##' @slot hookOut a list of length ngenes containing output from a hook function, if \code{zlm} was called with one
  153. ##' @slot exprs_values `character` or `integer` with the `assay` used.
  154. ##' @seealso zlm summary,ZlmFit-method
  155. ##' @aliases ZlmFit
  156. ##' @examples
  157. ##' data(vbetaFA)
  158. ##' zlmVbeta <- zlm(~ Stim.Condition+Population, subset(vbetaFA, ncells==1)[1:10,])
  159. ##' #Coefficients and standard errors
  160. ##' coef(zlmVbeta, 'D')
  161. ##' coef(zlmVbeta, 'C')
  162. ##' se.coef(zlmVbeta, 'C')
  163. ##' #Test for a Population effect by dropping the whole term (a 5 degree of freedom test)
  164. ##' lrTest(zlmVbeta, 'Population')
  165. ##' #Test only if the VbetaResponsive cells differ from the baseline group
  166. ##' lrTest(zlmVbeta, CoefficientHypothesis('PopulationVbetaResponsive'))
  167. ##' # Test if there is a difference between CD154+/Unresponsive and CD154-/Unresponsive.
  168. ##' # Note that because we parse the expression
  169. ##' # the columns must be enclosed in backquotes
  170. ##' # to protect the \quote{+} and \quote{-} characters.
  171. ##' lrTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
  172. ##' `PopulationCD154-VbetaUnresponsive`'))
  173. ##' waldTest(zlmVbeta, Hypothesis('`PopulationCD154+VbetaUnresponsive` -
  174. ##' `PopulationCD154-VbetaUnresponsive`'))
  175. setClass('ZlmFit', slots=list(coefC='matrix', coefD='matrix', vcovC='array', vcovD='array', LMlike='LMlike', sca='SingleCellAssay', deviance='matrix', loglik='matrix', df.null='matrix', df.resid='matrix', dispersion='matrix', dispersionNoshrink='matrix', priorDOF='numeric', priorVar='numeric', converged='matrix', hookOut='ANY', exprs_values = 'ANY'))
  177. ##' An S4 class for Gene Set Enrichment output
  178. ##'
  179. ##' This holds output from a call to gseaAfterBoot.
  180. ##' It primarily provides a summary method.
  181. ##' @slot tests array: gene sets X {discrete,continuous} X {stat, variance, degrees of freedom, avg correlation} X {test, null}
  182. ##' @slot bootR number of bootstrap replicates
  183. ##' @seealso gseaAfterBoot
  184. ##' @seealso calcZ
  185. ##' @seealso summary,GSEATests-method
  186. setClass('GSEATests', slots=list(tests='array', bootR='numeric'))

AllClasses.R at commit d46c1d1, no license · at the source

Overview

Authors: Paula Martín-Climent1,2,3, Samanta Ortuño-Miquel4, Juan F Gallego-Serna1,5, Silvia Martí-Martínez6, Luis M Valor1,5
ORCID iDs: Paula Martín-Climent, Samanta Ortuño-Miquel
  1. Research Laboratory, Dr. Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
  2. Present Address: Centre for Nutrition Research, Department of Nutrition, Food Science and Physiology, University of Navarra, 31008 Pamplona, Spain
  3. Centro de Investigación Biomédica en Red de Fisiopatología de La Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
  4. Unit of Epidemiology and Bioinformatics, Dr. Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
  5. Institute of Research, Development, and Innovation in Healthcare Biotechnology in Elche (IDiBE), Miguel Hernández University, 03202 Elche, Spain
  6. Department of Neurology, Dr. Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), 03010 Alicante, Spain
Institutions: Instituto de Salud Carlos III (Spain); Spanish Biomedical Research Centre in Physiopathology of Obesity and Nutrition (Spain); Hospital General Universitario de Alicante Doctor Balmis (Spain); Instituto de investigación sanitaria y biomédica de Alicante (Spain); Universidad de Navarra (Spain); Universitat de Miguel Hernández d'Elx (Spain); Instituto de Investigación, Desarrollo e Innovación en Biotecnología Sanitaria de Elche (IDiBE) (Spain)
Journal: Scientific reports, volume 16, issue 1, article 23980
Dates: received 2 December 2025; accepted 19 May 2026; published online 26 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-54412-4 · PMID 42192121 · PMCID PMC13434588 · OpenAlex W7162411650
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Polyglutamine, Blood, PBMC, scRNA-seq, Biomarker, Human, R6/1, Interferon, Biomarkers, Computational biology and bioinformatics, Genetics, Molecular biology, Neuroscience
MeSH: Biomarkers*, Huntington Disease*, Single-Cell Analysis*, Transcriptome*, Animals, Disease Models, Animal, Female, Gene Expression Profiling, Humans, Leukocytes, Mononuclear, Male, Mice, Mice, Transgenic, Phenotype, Single-Cell Gene Expression Analysis (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Instituto de Salud Carlos III (PI19/00125, PI23/01858); Ministerio de Ciencia e Innovación (CNS2022-136169); Instituto de Investigación Sanitaria y Biomédica de Alicante (2021-0406, 2022-0338,2024/C/6, 2025/C.1/5, 2025/C.1/16)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Transcriptional dysregulation is among the most prominent molecular alterations in Huntington’s disease (HD). It is not confined to the brain but also extends to peripheral tissues and cells, enabling minimally invasive screening strategies to identify transcriptional surrogates of the health status in HD mutation carriers. Nonetheless, transcriptomics approaches have failed to identify consistent candidates from peripheral blood, probably due to the low impact of the HD mutation in the transcriptional profiles of circulating cells, which can be masked by the high cellular complexity of this biofluid. In this study, we applied for the first time single-cell RNA-seq to peripheral blood mononuclear cells (PBMCs) to determine which cells accumulate the most prominent gene expression changes, and therefore, represent potential sources of reliable biomarkers. We observed common transcriptional alterations across different blood cell subtypes, which were partially validated in published bulk transcriptomics datasets. To relate these gene expression patterns with disease progression in the absence of a large cohort of patients, we examined selected candidates in a phenotypically characterized cohort of transgenic R6/1 mice. Among the tested genes, only the variations in the interferon related gene Irf7 in blood were mildly correlated with motor coordination performance in mutant mice. Notably, striatal Irf7 expression did not show such phenotypical correlation in the same individuals. Overall, transcriptional-based changes in peripheral blood can be linked to HD but they are mild and not apparently confined to particular cellular subpopulations. In conclusion, dissection of individual blood cells in combination with subsequent validation in mouse models emphasizes the challenges in obtaining clinically relevant biomarkers in HD.

Supplementary Information: The online version contains supplementary material available at 10.1038/s41598-026-54412-4.

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

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Its files are read in the Code ↔ Paper reader above.

RGLab/MAST

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d46c1d16951b0689b6d18e5f90f8d7f1f0b06910, 17 October 2025
Languages: R (56), JavaScript (4)
Size: 292 files, 60 scripts
Software Heritage: not archived
Found in: the text, “Bioinformatics analysis and statistical analysis”
Holds: README, environment (DESCRIPTION), tests, continuous integration, documentation, 3 notebooks
Not found: license file, CITATION.cff
Tools: reshape2 (9 files), data.table (4 files), ggplot2 (4 files), lme4 (4 files), tidyverse (2 files), car (1 file), limma (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
61 files
At the source: github.com/RGLab/MAST/

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 60 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
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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

The scRNA-seq data can be downloaded from ArrayExpress using the accession number E-MTAB-15129.

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

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, 5 authors, 13 keywords, 15 MeSH terms, 3 funders, 54 references.

Cite

This paper

Martín-Climent, P., Ortuño-Miquel, S., Gallego-Serna, J. F., Martí-Martínez, S., & Valor, L. M. (2026). Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood in Huntington's disease. Scientific reports, 16(1), 23980. https://doi.org/10.1038/s41598-026-54412-4

BibTeX

@article{martincliment2026single,
author = {Martín-Climent, Paula and Ortuño-Miquel, Samanta and Gallego-Serna, Juan F and Martí-Martínez, Silvia and Valor, Luis M},
title = {{Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood in Huntington's disease}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {23980},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-54412-4},
url = {https://doi.org/10.1038/s41598-026-54412-4},
pmid = {42192121},
pmcid = {PMC13434588}
}

RIS

TY - JOUR
AU - Martín-Climent, Paula
AU - Ortuño-Miquel, Samanta
AU - Gallego-Serna, Juan F
AU - Martí-Martínez, Silvia
AU - Valor, Luis M
TI - Single-cell transcriptomics and mouse model phenotyping for biomarker screen of peripheral blood in Huntington's disease
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/26
VL - 16
IS - 1
SP - 23980
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-54412-4
UR - https://doi.org/10.1038/s41598-026-54412-4
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13434588",
"ISSN": "2045-2322",
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