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Acarbose modulates microglial Pkm2 acetylation to reshape immunometabolism and preserve retinal neurons after ischemia-reperfusion.

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  1. [1] § Materials and methods › ScRNA-seq analysis ↔ R/generics.R, lines 109–170 · score 0.68 · quality control, Fold Change, FindMarkers, Seurat, seq, gene
  2. [2] § Materials and methods › ScRNA-seq analysis ↔ R/differential_expression.R, lines 455–538 · score 0.65 · Fold Change, FindMarkers, scRNA, utilized, Seurat, ratio

Paper

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The authors' code

R · 842 lines · 28 KB · other · 1 match

  1. #' @include reexports.R
  2. #'
  3. NULL
  5. #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  6. # Generics
  7. #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  9. #' Calculate module scores for feature expression programs in single cells
  10. #'
  11. #' Calculate the average expression levels of each program (cluster) on single
  12. #' cell level, subtracted by the aggregated expression of control feature sets.
  13. #' All analyzed features are binned based on averaged expression, and the
  14. #' control features are randomly selected from each bin.
  15. #'
  16. #' @param object An object
  17. #' @param ... Arguments passed to other methods
  18. #'
  19. #' @return Returns a Seurat object with module scores added to object meta data;
  20. #' each module is stored as \code{name#} for each module program present in
  21. #' \code{features}
  22. #'
  23. #' @references Tirosh et al, Science (2016)
  24. #'
  25. #' @export
  26. #' @concept utilities
  27. #'
  28. AddModuleScore <- function(object, ...) {
  29. UseMethod(generic = 'AddModuleScore', object = object)
  30. }
  32. #' Add info to anchor matrix
  33. #'
  34. #' @param anchors An \code{\link{AnchorSet}} object
  35. #' @param vars Variables to pull for each object via FetchData
  36. #' @param slot Slot to pull feature data for
  37. #' @param assay Specify the Assay per object if annotating with expression data
  38. #' @param ... Arguments passed to other methods
  39. #
  40. #' @return Returns the anchor dataframe with additional columns for annotation
  41. #' metadata
  42. #'
  43. #' @export
  44. #'
  45. AnnotateAnchors <- function(anchors, vars, slot, ...) {
  46. UseMethod(generic = 'AnnotateAnchors', object = anchors)
  47. }
  49. #' Convert objects to CellDataSet objects
  50. #'
  51. #' @param x An object to convert to class \code{CellDataSet}
  52. #' @param ... Arguments passed to other methods
  53. #'
  54. #' @rdname as.CellDataSet
  55. #' @export as.CellDataSet
  56. #'
  57. as.CellDataSet <- function(x, ...) {
  58. UseMethod(generic = 'as.CellDataSet', object = x)
  59. }
  61. #' Convert objects to SingleCellExperiment objects
  62. #'
  63. #' @param x An object to convert to class \code{SingleCellExperiment}
  64. #' @param ... Arguments passed to other methods
  65. #'
  66. #' @rdname as.SingleCellExperiment
  67. #' @export as.SingleCellExperiment
  68. #'
  69. as.SingleCellExperiment <- function(x, ...) {
  70. UseMethod(generic = 'as.SingleCellExperiment', object = x)
  71. }
  73. #' Cluster Determination
  74. #'
  75. #' Identify clusters of cells by a shared nearest neighbor (SNN) modularity
  76. #' optimization based clustering algorithm. First calculate k-nearest neighbors
  77. #' and construct the SNN graph. Then optimize the modularity function to
  78. #' determine clusters. For a full description of the algorithms, see Waltman and
  79. #' van Eck (2013) \emph{The European Physical Journal B}. Thanks to Nigel
  80. #' Delaney (evolvedmicrobe@github) for the rewrite of the Java modularity
  81. #' optimizer code in Rcpp!
  82. #'
  83. #' To run Leiden algorithm, you must first install the leidenalg python
  84. #' package (e.g. via pip install leidenalg), see Traag et al (2018).
  85. #'
  86. #' @param object An object
  87. #' @param ... Arguments passed to other methods
  88. #'
  89. #' @return Returns a Seurat object where the idents have been updated with new cluster info;
  90. #' latest clustering results will be stored in object metadata under 'seurat_clusters'.
  91. #' Note that 'seurat_clusters' will be overwritten everytime FindClusters is run
  92. #'
  93. #' @export
  94. #'
  95. #' @rdname FindClusters
  96. #' @export FindClusters
  97. #'
  98. FindClusters <- function(object, ...) {
  99. UseMethod(generic = 'FindClusters', object = object)
  100. }
  102. #' Gene expression markers of identity classes
  103. #'
  104. #' Finds markers (differentially expressed genes) for each of the identity classes in a dataset. Note that differential expression between groups (e.g. treatment conditions) should account for biological variation across samples, e.g. by using pseudobulk expression profiles.
  105. #'
  106. #' @param object An object
  107. #' @param ... Arguments passed to other methods and to specific DE methods
  109. #' @return data.frame with a ranked list of putative markers as rows, and associated
  110. #' statistics as columns (p-values, ROC score, etc., depending on the test used (\code{test.use})). The following columns are always present:
  111. #' \itemize{
  112. #' \item \code{avg_logFC}: log fold-chage of the average expression between the two groups. Positive values indicate that the gene is more highly expressed in the first group
  113. #' \item \code{pct.1}: The percentage of cells where the gene is detected in the first group
  114. #' \item \code{pct.2}: The percentage of cells where the gene is detected in the second group
  115. #' \item \code{p_val_adj}: Adjusted p-value, based on bonferroni correction using all genes in the dataset
  116. #' }
  117. #'
  118. #' @details p-value adjustment is performed using bonferroni correction based on
  119. #' the total number of genes in the dataset. Other correction methods are not
  120. #' recommended, as Seurat pre-filters genes using the arguments above, reducing
  121. #' the number of tests performed. Lastly, as Aaron Lun has pointed out, p-values
  122. #' should be interpreted cautiously, as the genes used for clustering are the
  123. #' same genes tested for differential expression.
  124. #'
  125. #' @references McDavid A, Finak G, Chattopadyay PK, et al. Data exploration,
  126. #' quality control and testing in single-cell qPCR-based gene expression experiments.
  127. #' Bioinformatics. 2013;29(4):461-467. doi:10.1093/bioinformatics/bts714
  128. #' @references Trapnell C, et al. The dynamics and regulators of cell fate
  129. #' decisions are revealed by pseudotemporal ordering of single cells. Nature
  130. #' Biotechnology volume 32, pages 381-386 (2014)
  131. #' @references Andrew McDavid, Greg Finak and Masanao Yajima (2017). MAST: Model-based
  132. #' Analysis of Single Cell Transcriptomics. R package version 1.2.1.
  133. #' https://github.com/RGLab/MAST/
  134. #' @references Love MI, Huber W and Anders S (2014). "Moderated estimation of
  135. #' fold change and dispersion for RNA-seq data with DESeq2." Genome Biology.
  136. #' https://bioconductor.org/packages/release/bioc/html/DESeq2.html
  137. #'
  138. #' @note See the DE vignette for more details and examples
  139. #'
  140. #' @export
  141. #'
  142. #' @examples
  143. #' \dontrun{
  144. #' data("pbmc_small")
  145. #' # Find markers for cluster 2
  146. #' markers <- FindMarkers(object = pbmc_small, ident.1 = 2)
  147. #' head(x = markers)
  148. #'
  149. #' # Within cluster 2, find markers for cells in the 'g1' group compared with
  150. #' # other cells, using the 'groups' metadata variable.
  151. #' markers <- FindMarkers(pbmc_small, ident.1 = "g1", group.by = 'groups', subset.ident = "2")
  152. #' head(x = markers)
  153. #'
  154. #' # Pass 'clustertree' or an object of class phylo to ident.1 and
  155. #' # a node to ident.2 as a replacement for FindMarkersNode
  156. #' if (requireNamespace("ape", quietly = TRUE)) {
  157. #' pbmc_small <- BuildClusterTree(object = pbmc_small)
  158. #' markers <- FindMarkers(object = pbmc_small, ident.1 = 'clustertree', ident.2 = 5)
  159. #' head(x = markers)
  160. #' }
  161. #' }
  162. #'
  163. #' @rdname FindMarkers
  164. #' @export FindMarkers
  165. #'
  166. #' @aliases FindMarkersNode
  167. #'
  168. FindMarkers <- function(object, ...) {
  169. UseMethod(generic = 'FindMarkers', object = object)
  170. }
  172. #' (Shared) Nearest-neighbor graph construction
  173. #'
  174. #' Computes the \code{k.param} nearest neighbors for a given dataset. Can also
  175. #' optionally (via \code{compute.SNN}), construct a shared nearest neighbor
  176. #' graph by calculating the neighborhood overlap (Jaccard index) between every
  177. #' cell and its \code{k.param} nearest neighbors.
  178. #'
  179. #' @param object An object
  180. #' @param ... Arguments passed to other methods
  181. #'
  182. #' @return This function can either return a \code{\link[SeuratObject]{Neighbor}} object
  183. #' with the KNN information or a list of \code{\link[SeuratObject]{Graph}} objects with
  184. #' the KNN and SNN depending on the settings of \code{return.neighbor} and
  185. #' \code{compute.SNN}. When running on a \code{\link[SeuratObject]{Seurat}} object, this
  186. #' returns the \code{\link[SeuratObject]{Seurat}} object with the Graphs or Neighbor objects
  187. #' stored in their respective slots. Names of the Graph or Neighbor object can
  188. #' be found with \code{\link[SeuratObject]{Graphs}} or \code{\link[SeuratObject]{Neighbors}}.
  189. #'
  190. #' @examples
  191. #' data("pbmc_small")
  192. #' pbmc_small
  193. #' # Compute an SNN on the gene expression level
  194. #' pbmc_small <- FindNeighbors(pbmc_small, features = VariableFeatures(object = pbmc_small))
  195. #'
  196. #' # More commonly, we build the SNN on a dimensionally reduced form of the data
  197. #' # such as the first 10 principle components.
  198. #'
  199. #' pbmc_small <- FindNeighbors(pbmc_small, reduction = "pca", dims = 1:10)
  200. #'
  201. #' @rdname FindNeighbors
  202. #' @export FindNeighbors
  203. #'
  204. FindNeighbors <- function(object, ...) {
  205. UseMethod(generic = 'FindNeighbors', object = object)
  206. }
  208. #' Find variable features
  209. #'
  210. #' Identifies features that are outliers on a 'mean variability plot'.
  211. #'
  212. #' For the mean.var.plot method:
  213. #' Exact parameter settings may vary empirically from dataset to dataset, and
  214. #' based on visual inspection of the plot. Setting the y.cutoff parameter to 2
  215. #' identifies features that are more than two standard deviations away from the
  216. #' average dispersion within a bin. The default X-axis function is the mean
  217. #' expression level, and for Y-axis it is the log(Variance/mean). All mean/variance
  218. #' calculations are not performed in log-space, but the results are reported in
  219. #' log-space - see relevant functions for exact details.
  220. #'
  221. #' @param object An object
  222. #' @param ... Arguments passed to other methods
  223. #'
  224. #' @rdname FindVariableFeatures
  225. #' @export FindVariableFeatures
  226. #'
  227. #' @aliases FindVariableGenes
  228. #'
  229. FindVariableFeatures <- function(object, ...) {
  230. UseMethod(generic = 'FindVariableFeatures', object = object)
  231. }
  233. #' Find spatially variable features
  234. #'
  235. #' Identify features whose variability in expression can be explained to some
  236. #' degree by spatial location.
  237. #'
  238. #' @param object An object
  239. #' @param ... Arguments passed to other methods
  240. #'
  241. #' @rdname FindSpatiallyVariableFeatures
  242. #' @export FindSpatiallyVariableFeatures
  243. #'
  244. FindSpatiallyVariableFeatures <- function(object, ...) {
  245. UseMethod(generic = 'FindSpatiallyVariableFeatures', object = object)
  246. }
  248. #' Fold Change
  249. #'
  250. #' Calculate log fold change and percentage of cells expressing each feature
  251. #' for different identity classes.
  252. #'
  253. #' If the slot is \code{scale.data} or a reduction is specified, average difference
  254. #' is returned instead of log fold change and the column is named "avg_diff".
  255. #' Otherwise, log2 fold change is returned with column named "avg_log2_FC".
  256. #'
  257. #' @examples
  258. #' \dontrun{
  259. #' data("pbmc_small")
  260. #' FoldChange(pbmc_small, ident.1 = 1)
  261. #' }
  262. #'
  263. #' @param object A Seurat object
  264. #' @param ... Arguments passed to other methods
  265. #' @rdname FoldChange
  266. #' @export FoldChange
  267. #' @return Returns a data.frame
  268. #' @seealso \code{FindMarkers}
  269. FoldChange <- function(object, ...) {
  270. UseMethod(generic = 'FoldChange', object = object)
  271. }
  273. #' Get an Assay object from a given Seurat object.
  274. #'
  275. #' @param object An object
  276. #' @param ... Arguments passed to other methods
  277. #'
  278. #' @return Returns an Assay object
  279. #'
  280. #' @rdname GetAssay
  281. #' @export GetAssay
  282. #'
  283. GetAssay <- function(object, ...) {
  284. UseMethod(generic = 'GetAssay', object = object)
  285. }
  287. #' Integrate low dimensional embeddings
  288. #'
  289. #' Perform dataset integration using a pre-computed Anchorset of specified low
  290. #' dimensional representations.
  291. #'
  292. #' The main steps of this procedure are identical to \code{\link{IntegrateData}}
  293. #' with one key distinction. When computing the weights matrix, the distance
  294. #' calculations are performed in the full space of integrated embeddings when
  295. #' integrating more than two datasets, as opposed to a reduced PCA space which
  296. #' is the default behavior in \code{\link{IntegrateData}}.
  297. #'
  298. #' @param anchorset An AnchorSet object
  299. #' @param new.reduction.name Name for new integrated dimensional reduction.
  300. #' @param reductions Name of reductions to be integrated. For a
  301. #' TransferAnchorSet, this should be the name of a reduction present in the
  302. #' anchorset object (for example, "pcaproject"). For an IntegrationAnchorSet,
  303. #' this should be a \code{\link[SeuratObject]{DimReduc}} object containing all cells present
  304. #' in the anchorset object.
  305. #' @param dims.to.integrate Number of dimensions to return integrated values for
  306. #' @param weight.reduction Dimension reduction to use when calculating anchor
  307. #' weights. This can be one of:
  308. #' \itemize{
  309. #' \item{A string, specifying the name of a dimension reduction present in
  310. #' all objects to be integrated}
  311. #' \item{A vector of strings, specifying the name of a dimension reduction to
  312. #' use for each object to be integrated}
  313. #' \item{A vector of \code{\link[SeuratObject]{DimReduc}} objects, specifying the object to
  314. #' use for each object in the integration}
  315. #' \item{NULL, in which case the full corrected space is used for computing
  316. #' anchor weights.}
  317. #' }
  318. #' @param ... Reserved for internal use
  319. #'
  320. #' @return When called on a TransferAnchorSet (from FindTransferAnchors), this
  321. #' will return the query object with the integrated embeddings stored in a new
  322. #' reduction. When called on an IntegrationAnchorSet (from IntegrateData), this
  323. #' will return a merged object with the integrated reduction stored.
  324. #'
  325. #' @rdname IntegrateEmbeddings
  326. #' @export IntegrateEmbeddings
  327. #'
  328. IntegrateEmbeddings <- function(anchorset, ...) {
  329. UseMethod(generic = "IntegrateEmbeddings", object = anchorset)
  330. }
  332. #' Leverage Score Calculation
  333. #'
  334. #' This function computes the leverage scores for a given object
  335. #' It uses the concept of sketching and random projections. The function provides an approximation
  336. #' to the leverage scores using a scalable method suitable for large matrices.
  337. #'
  338. #' @param object A matrix-like object
  339. #' @param ... Arguments passed to other methods
  340. #'
  341. #' @references Clarkson, K. L. & Woodruff, D. P.
  342. #' Low-rank approximation and regression in input sparsity time.
  343. #' JACM 63, 1–45 (2017). \doi{10.1145/3019134};
  344. #'
  345. #' @export
  346. #'
  347. LeverageScore <- function(object, ...) {
  348. UseMethod(generic = 'LeverageScore', object = object)
  349. }
  351. #' Normalize Raw Data
  352. #'
  353. #' @param data Matrix with the raw count data
  354. #' @param scale.factor Scale the data; default is \code{1e4}
  355. #' @param margin Margin to normalize over
  356. #' @param verbose Print progress
  357. #'
  358. #' @return A matrix with the normalized and log-transformed data
  359. #'
  360. #' @template param-dotsm
  361. #'
  362. #' @export
  363. #' @concept preprocessing
  364. #'
  365. #' @examples
  366. #' mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
  367. #' mat
  368. #' mat_norm <- LogNormalize(data = mat)
  369. #' mat_norm
  370. #'
  371. LogNormalize <- function(
  372. data,
  373. scale.factor = 1e4,
  374. margin = 2L,
  375. verbose = TRUE,
  376. ...
  377. ) {
  378. UseMethod(generic = 'LogNormalize', object = data)
  379. }
  382. #' Metric for evaluating mapping success
  383. #'
  384. #' This metric was designed to help identify query cells that aren't well
  385. #' represented in the reference dataset. The intuition for the score is that we
  386. #' are going to project the query cells into a reference-defined space and then
  387. #' project them back onto the query. By comparing the neighborhoods before and
  388. #' after projection, we identify cells who's local neighborhoods are the most
  389. #' affected by this transformation. This could be because there is a population
  390. #' of query cells that aren't present in the reference or the state of the cells
  391. #' in the query is significantly different from the equivalent cell type in the
  392. #' reference.
  393. #'
  394. #' @param anchors Set of anchors
  395. #' @param ... Arguments passed to other methods
  396. #'
  397. #' @rdname MappingScore
  398. #' @export MappingScore
  399. #'
  400. MappingScore <- function(anchors, ...) {
  401. UseMethod(generic = "MappingScore", object = anchors)
  402. }
  404. #' Normalize Data
  405. #'
  406. #' Normalize the count data present in a given assay.
  407. #'
  408. #' @param object An object
  409. #' @param ... Arguments passed to other methods
  410. #'
  411. #' @return Returns object after normalization
  412. #'
  413. #' @rdname NormalizeData
  414. #' @export NormalizeData
  415. #'
  416. NormalizeData <- function(object, ...) {
  417. UseMethod(generic = 'NormalizeData', object = object)
  418. }
  420. #' Project query data to the reference dimensional reduction
  421. #'
  422. #'
  423. #' @param query An object for query cells
  424. #' @param reference An object for reference cells
  425. #' @param query.assay Assay name for query object
  426. #' @param reference.assay Assay name for reference object
  427. #' @param reduction Name of dimensional reduction from reference object
  428. #' @param dims Dimensions used for reference dimensional reduction
  429. #' @param scale Determine if scale query data based on reference data variance
  430. #' @param verbose Print progress
  431. #' @param feature.mean Mean of features in reference
  432. #' @param feature.sd Standard variance of features in reference
  433. #'
  434. #' @return A matrix with projected cell embeddings
  435. #'
  436. #' @rdname ProjectCellEmbeddings
  437. #' @export ProjectCellEmbeddings
  438. #'
  439. #' @keywords internal
  440. #'
  441. ProjectCellEmbeddings <- function(
  442. query,
  443. ...
  444. ) {
  445. UseMethod(generic = 'ProjectCellEmbeddings', object = query)
  446. }
  448. #' Project query into UMAP coordinates of a reference
  449. #'
  450. #' This function will take a query dataset and project it into the coordinates
  451. #' of a provided reference UMAP. This is essentially a wrapper around two steps:
  452. #' \itemize{
  453. #' \item{FindNeighbors - Find the nearest reference cell neighbors and their
  454. #' distances for each query cell.}
  455. #' \item{RunUMAP - Perform umap projection by providing the neighbor set
  456. #' calculated above and the umap model previously computed in the reference.}
  457. #' }
  458. #'
  459. #' @param query Query dataset
  460. #'
  461. #' @rdname ProjectUMAP
  462. #' @export ProjectUMAP
  463. #'
  464. ProjectUMAP <- function(query, ...) {
  465. UseMethod(generic = "ProjectUMAP", object = query)
  466. }
  468. #' Pseudobulk Expression
  469. #'
  470. #' Normalize the count data present in a given assay.
  471. #'
  472. #' @param object An assay
  473. #' @param ... Arguments passed to other methods
  474. #'
  475. #' @return Returns object after normalization
  476. #'
  477. #' @rdname PseudobulkExpression
  478. #' @export PseudobulkExpression
  479. #' @concept utilities
  480. #'
  481. PseudobulkExpression <- function(object, ...) {
  482. UseMethod(generic = "PseudobulkExpression", object = object)
  483. }
  485. #' Perform Canonical Correlation Analysis
  486. #'
  487. #' Runs a canonical correlation analysis using a diagonal implementation of CCA.
  488. #' For details about stored CCA calculation parameters, see
  489. #' \code{PrintCCAParams}.
  490. #' @param object1 First Seurat object
  491. #' @param object2 Second Seurat object.
  492. # @param ... Arguments passed to other methods
  493. #'
  494. #' @return Returns a combined Seurat object with the CCA results stored.
  495. #'
  496. #' @seealso \code{\link[SeuratObject]{merge.Seurat}}
  497. #'
  498. #' @examples
  499. #' \dontrun{
  500. #' data("pbmc_small")
  501. #' pbmc_small
  502. #' # As CCA requires two datasets, we will split our test object into two just for this example
  503. #' pbmc1 <- subset(pbmc_small, cells = colnames(pbmc_small)[1:40])
  504. #' pbmc2 <- subset(pbmc_small, cells = colnames(x = pbmc_small)[41:80])
  505. #' pbmc1[["group"]] <- "group1"
  506. #' pbmc2[["group"]] <- "group2"
  507. #' pbmc_cca <- RunCCA(object1 = pbmc1, object2 = pbmc2)
  508. #' # Print results
  509. #' print(x = pbmc_cca[["cca"]])
  510. #' }
  511. #'
  512. #' @rdname RunCCA
  513. #' @export RunCCA
  514. #'
  515. RunCCA <- function(object1, object2, ...) {
  516. UseMethod(generic = 'RunCCA', object = object1)
  517. }
  520. #' Run Graph Laplacian Eigendecomposition
  521. #'
  522. #' Run a graph laplacian dimensionality reduction. It is used as a low
  523. #' dimensional representation for a cell-cell graph. The input graph
  524. #' should be symmetric
  525. #'
  526. #' @param object A Seurat object
  527. #' @param ... Arguments passed to
  528. #' \code{\link[RSpectra:eigs_sym]{RSpectra::eigs_sym}}
  529. #'
  530. #' @return Returns Seurat object with the Graph laplacian eigenvector
  531. #' calculation stored in the reductions slot
  532. #'
  533. #' @rdname RunGraphLaplacian
  534. #' @export RunGraphLaplacian
  535. #'
  537. RunGraphLaplacian <- function(object, ...) {
  538. UseMethod(generic = 'RunGraphLaplacian', object = object)
  539. }
  542. #' Run Independent Component Analysis on gene expression
  543. #'
  544. #' Run fastica algorithm from the ica package for ICA dimensionality reduction.
  545. #' For details about stored ICA calculation parameters, see
  546. #' \code{PrintICAParams}.
  547. #'
  548. #' @param object Seurat object
  549. #'
  550. #' @rdname RunICA
  551. #' @export RunICA
  552. #'
  553. RunICA <- function(object, ...) {
  554. UseMethod(generic = "RunICA", object = object)
  555. }
  557. #' Run Linear Discriminant Analysis
  558. #'
  559. #'
  560. #' @param object An object
  561. #' @param ... Arguments passed to other methods
  562. #'
  563. #' @rdname RunLDA
  564. #' @export RunLDA
  565. #'
  566. #' @aliases RunLDA
  567. #'
  568. RunLDA <- function(object, ...) {
  569. UseMethod(generic = 'RunLDA', object = object)
  570. }
  572. #' Run Principal Component Analysis
  573. #'
  574. #' Run a PCA dimensionality reduction. For details about stored PCA calculation
  575. #' parameters, see \code{PrintPCAParams}.
  576. #'
  577. #' @param object An object
  578. #' @param ... Arguments passed to other methods and IRLBA
  579. #'
  580. #' @return Returns Seurat object with the PCA calculation stored in the reductions slot
  581. #'
  582. #' @export
  583. #'
  584. #' @rdname RunPCA
  585. #' @export RunPCA
  586. #'
  587. RunPCA <- function(object, ...) {
  588. UseMethod(generic = 'RunPCA', object = object)
  589. }
  591. #' Run Supervised Latent Semantic Indexing
  592. #'
  593. #' Run a supervised LSI (SLSI) dimensionality reduction supervised by a
  594. #' cell-cell kernel. SLSI is used to capture a linear transformation of peaks
  595. #' that maximizes its dependency to the given cell-cell kernel.
  596. #'
  597. #' @param object An object
  598. #' @param ... Arguments passed to IRLBA irlba
  599. #'
  600. #' @return Returns Seurat object with the SLSI calculation stored in the
  601. #' reductions slot
  602. #'
  603. #' @export
  604. #'
  605. #' @rdname RunSLSI
  606. #' @export RunSLSI
  607. #'
  608. RunSLSI <- function(object, ...) {
  609. UseMethod(generic = 'RunSLSI', object = object)
  610. }
  612. #' Run Supervised Principal Component Analysis
  613. #'
  614. #' Run a supervised PCA (SPCA) dimensionality reduction supervised by a cell-cell kernel.
  615. #' SPCA is used to capture a linear transformation which maximizes its dependency to
  616. #' the given cell-cell kernel. We use SNN graph as the kernel to supervise the linear
  617. #' matrix factorization.
  618. #'
  619. #' @param object An object
  620. #' @param ... Arguments passed to other methods and IRLBA
  621. #'
  622. #' @return Returns Seurat object with the SPCA calculation stored in the reductions slot
  623. #' @references Barshan E, Ghodsi A, Azimifar Z, Jahromi MZ.
  624. #' Supervised principal component analysis: Visualization, classification and
  625. #' regression on subspaces and submanifolds.
  626. #' Pattern Recognition. 2011 Jul 1;44(7):1357-71. \url{doi:10.1016/j.patcog.2010.12.015};
  627. #' @export
  628. #'
  629. #' @rdname RunSPCA
  630. #' @export RunSPCA
  631. #'
  632. RunSPCA <- function(object, ...) {
  633. UseMethod(generic = 'RunSPCA', object = object)
  634. }
  636. #' Run t-distributed Stochastic Neighbor Embedding
  637. #'
  638. #' Run t-SNE dimensionality reduction on selected features. Has the option of
  639. #' running in a reduced dimensional space (i.e. spectral tSNE, recommended),
  640. #' or running based on a set of genes. For details about stored TSNE calculation
  641. #' parameters, see \code{PrintTSNEParams}.
  642. #'
  643. #' @param object Seurat object
  644. #' @param ... Arguments passed to other methods and to t-SNE call (most commonly used is perplexity)
  645. #'
  646. #' @rdname RunTSNE
  647. #' @export RunTSNE
  648. #'
  649. RunTSNE <- function(object, ...) {
  650. UseMethod(generic = 'RunTSNE', object = object)
  651. }
  653. #' Run UMAP
  654. #'
  655. #' Runs the Uniform Manifold Approximation and Projection (UMAP) dimensional
  656. #' reduction technique. To run using \code{umap.method="umap-learn"}, you must
  657. #' first install the umap-learn python package (e.g. via
  658. #' \code{pip install umap-learn}). Details on this package can be
  659. #' found here: \url{https://github.com/lmcinnes/umap}. For a more in depth
  660. #' discussion of the mathematics underlying UMAP, see the ArXiv paper here:
  661. #' \url{https://arxiv.org/abs/1802.03426}.
  662. #'
  663. #' @param object An object
  664. #' @param ... Arguments passed to other methods and UMAP
  665. #'
  666. #' @return Returns a Seurat object containing a UMAP representation
  667. #'
  668. #' @references McInnes, L, Healy, J, UMAP: Uniform Manifold Approximation and
  669. #' Projection for Dimension Reduction, ArXiv e-prints 1802.03426, 2018
  670. #'
  671. #' @export
  672. #'
  673. #' @examples
  674. #' \dontrun{
  675. #' data("pbmc_small")
  676. #' pbmc_small
  677. #' # Run UMAP map on first 5 PCs
  678. #' pbmc_small <- RunUMAP(object = pbmc_small, dims = 1:5)
  679. #' # Plot results
  680. #' DimPlot(object = pbmc_small, reduction = 'umap')
  681. #' }
  682. #'
  683. #' @rdname RunUMAP
  684. #' @export RunUMAP
  685. #'
  686. RunUMAP <- function(object, ...) {
  687. UseMethod(generic = 'RunUMAP', object = object)
  688. }
  690. #' Scale and center the data.
  691. #'
  692. #' Scales and centers features in the dataset. If variables are provided in vars.to.regress,
  693. #' they are individually regressed against each feature, and the resulting residuals are
  694. #' then scaled and centered.
  695. #'
  696. #' ScaleData now incorporates the functionality of the function formerly known
  697. #' as RegressOut (which regressed out given the effects of provided variables
  698. #' and then scaled the residuals). To make use of the regression functionality,
  699. #' simply pass the variables you want to remove to the vars.to.regress parameter.
  700. #'
  701. #' Setting center to TRUE will center the expression for each feature by subtracting
  702. #' the average expression for that feature. Setting scale to TRUE will scale the
  703. #' expression level for each feature by dividing the centered feature expression
  704. #' levels by their standard deviations if center is TRUE and by their root mean
  705. #' square otherwise.
  706. #'
  707. #' @param object An object
  708. #' @param ... Arguments passed to other methods
  709. #'
  710. #' @rdname ScaleData
  711. #' @export ScaleData
  712. #'
  713. ScaleData <- function(object, ...) {
  714. UseMethod(generic = 'ScaleData', object = object)
  715. }
  717. #' Get image scale factors
  718. #'
  719. #' @param object An object to get scale factors from
  720. #' @param ... Arguments passed to other methods
  721. #'
  722. #' @return An object of class \code{scalefactors}
  723. #'
  724. #' @rdname ScaleFactors
  725. #' @export ScaleFactors
  726. #'
  727. ScaleFactors <- function(object, ...) {
  728. UseMethod(generic = 'ScaleFactors', object = object)
  729. }
  731. #' Compute Jackstraw scores significance.
  732. #'
  733. #' Significant PCs should show a p-value distribution that is
  734. #' strongly skewed to the left compared to the null distribution.
  735. #' The p-value for each PC is based on a proportion test comparing the number
  736. #' of features with a p-value below a particular threshold (score.thresh), compared with the
  737. #' proportion of features expected under a uniform distribution of p-values.
  738. #'
  739. #' @param object An object
  740. #' @param ... Arguments passed to other methods
  741. #'
  742. #' @return Returns a Seurat object
  743. #'
  744. #' @author Omri Wurtzel
  745. #' @seealso \code{\link{JackStrawPlot}}
  746. #'
  747. #' @rdname ScoreJackStraw
  748. #' @export ScoreJackStraw
  749. #'
  750. ScoreJackStraw <- function(object, ...) {
  751. UseMethod(generic = 'ScoreJackStraw', object = object)
  752. }
  754. #' Perform sctransform-based normalization
  755. #' @param object An object
  756. #' @param ... Arguments passed to other methods (not used)
  757. #'
  758. #' @rdname SCTransform
  759. #' @export SCTransform
  760. #'
  761. SCTransform <- function(object, ...) {
  762. UseMethod(generic = 'SCTransform', object = object)
  763. }
  765. #' Get SCT results from an Assay
  766. #'
  767. #' Pull the \code{\link{SCTResults}} information from an \code{\link{SCTAssay}}
  768. #' object.
  769. #'
  770. #' @param object An object
  771. #' @param ... Arguments passed to other methods (not used)
  772. #'
  773. #' @rdname SCTResults
  774. #' @export SCTResults
  775. #'
  776. SCTResults <- function(object, ...) {
  777. UseMethod(generic = 'SCTResults', object = object)
  778. }
  780. #' Get the Pearson residuals from an sctransform-normalized dataset.
  781. #'
  782. #' @param object An object
  783. #' @param ... Arguments passed to other methods (not used)
  784. #'
  785. #' @rdname FetchResiduals
  786. #' @export FetchResiduals
  787. #'
  788. FetchResiduals <- function(object, ...) {
  789. UseMethod(generic = 'FetchResiduals', object = object)
  790. }
  792. #' @param value new data to set
  793. #'
  794. #' @rdname SCTResults
  795. #' @export SCTResults<-
  796. #'
  797. "SCTResults<-" <- function(object, ..., value) {
  798. UseMethod(generic = 'SCTResults<-', object = object)
  799. }
  801. #' Variance Stabilizing Transformation
  802. #'
  803. #' Apply variance stabilizing transformation for selection of variable features
  804. #'
  805. #' @inheritParams stats::loess
  806. #' @param data A matrix-like object
  807. #' @param margin Unused
  808. #' @param nselect Number of of features to select
  809. #' @param clip Upper bound for values post-standardization; defaults to the
  810. #' square root of the number of cells
  811. #' @param verbose ...
  812. #'
  813. #' @template param-dotsm
  814. #'
  815. #' @return A data frame with the following columns:
  816. #' \itemize{
  817. #' \item \dQuote{\code{mean}}: ...
  818. #' \item \dQuote{\code{variance}}: ...
  819. #' \item \dQuote{\code{variance.expected}}: ...
  820. #' \item \dQuote{\code{variance.standardized}}: ...
  821. #' \item \dQuote{\code{variable}}: \code{TRUE} if the feature selected as
  822. #' variable, otherwise \code{FALSE}
  823. #' \item \dQuote{\code{rank}}: If the feature is selected as variable, then how
  824. #' it compares to other variable features with lower ranks as more variable;
  825. #' otherwise, \code{NA}
  826. #' }
  827. #'
  828. #' @rdname VST
  829. #' @export VST
  830. #'
  831. #' @keywords internal
  832. #'
  833. VST <- function(
  834. data,
  835. margin = 1L,
  836. nselect = 2000L,
  837. span = 0.3,
  838. clip = NULL,
  839. ...
  840. ) {
  841. UseMethod(generic = 'VST', object = data)
  842. }

generics.R at commit 586015a, under other · at the source

Overview

Authors: Yuwen Wen1, Ya-nan Dou2, Xiaohong Chen1, Xiuxing Liu1, Zhenlan Yang1, Yingting Zhu1, Zhidong Li1, Caibin Deng1, Ye Deng1, Wenru Su3, Yehong Zhuo1
  1. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Sun Yat-sen University, Guangzhou, 510060 China
  2. Department of Neurosurgery, Xijing Hospital, Air Force Military Medical University, Xi’an, 710032 China
  3. Department of Ophthalmology, Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011 China
Institutions: Sun Yat-sen University (China); State Key Laboratory of Ophthalmology; Zhongshan Ophthalmic Center, Sun Yat-sen University; Xijing Hospital (China); Air Force Medical University (China); Shanghai Jiao Tong University (China); Shanghai Ninth People's Hospital (China)
Journal: Journal of neuroinflammation, volume 23, issue 1, article 206
Dates: received 3 November 2025; accepted 22 April 2026; published online 2 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12974-026-03838-8 · PMID 42069589 · PMCID PMC13281619 · OpenAlex W7159953270
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics
Keywords: Retinal ischemia and reperfusion, Acarbose, Immunometabolism, Microglia, Pkm2, Acetylation
MeSH: Microglia*, Pyruvate Kinase*, Reperfusion Injury*, Retinal Ganglion Cells*, Retinal Neurons*, Thyroid Hormones*, Acetylation, Animals, Male, Mice, Mice, Inbred C57BL (* major topic)
Topic: Neuroinflammation and Neurodegeneration Mechanisms (Neurology, Neuroscience), according to OpenAlex
Funding: Guangdong Basic Research Center of Excellence for Major Blinding Eye Diseases Prevention and Treatment (2024-PIZC-022); National Natural Science Foundation of China (82401720, 32571061, 82171057); Youth Science Fund Project (82525019); Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515013058)
Citations: cited by 1 paper (Europe PMC); 89 references in the paper
Research resources: The mouse BV2 cells line RRID:CVCL_0182

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 2 matches between paragraphs and lines of code.

satijalab/seurat

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 586015abde10618ecb32d3fe632267a83317a08d, 21 September 2026
Languages: R (114), C++ (8), C/C++ (4), C (1)
Size: 455 files, 127 scripts
Software Heritage: archived
Found in: the text, “ScRNA-seq analysis”
Holds: README, license file, environment (DESCRIPTION), tests, continuous integration, documentation, 70 notebooks
Not found: CITATION.cff
Tools: Seurat (75 files), ggplot2 (48 files), patchwork (27 files), tidyverse (19 files), cowplot (10 files), reshape2 (3 files), SingleCellExperiment (3 files), Plotly (2 files), data.table (1 file), DESeq2 (1 file), Harmony (1 file), igraph (1 file), limma (1 file), Monocle 3 (1 file), reticulate (1 file), UMAP (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
130 files
At the source: github.com/satijalab/seurat

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 127 scripts, each with its path and the digest of its content;
  • 2 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

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Data availability statement

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  • it says that the data are available on request

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Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 6 keywords, 11 MeSH terms, 4 funders, 89 references, 1 RRID.

Cite

This paper

Wen, Y., Dou, Y.-n., Chen, X., Liu, X., Yang, Z., Zhu, Y., Li, Z., Deng, C., Deng, Y., Su, W., & Zhuo, Y. (2026). Acarbose modulates microglial Pkm2 acetylation to reshape immunometabolism and preserve retinal neurons after ischemia-reperfusion. Journal of neuroinflammation, 23(1), 206. https://doi.org/10.1186/s12974-026-03838-8

BibTeX

@article{wen2026acarbose,
author = {Wen, Yuwen and Dou, Ya-nan and Chen, Xiaohong and Liu, Xiuxing and Yang, Zhenlan and Zhu, Yingting and Li, Zhidong and Deng, Caibin and Deng, Ye and Su, Wenru and Zhuo, Yehong},
title = {{Acarbose modulates microglial Pkm2 acetylation to reshape immunometabolism and preserve retinal neurons after ischemia-reperfusion}},
journal = {Journal of neuroinflammation},
year = {2026},
month = may,
volume = {23},
number = {1},
pages = {206},
publisher = {BMC},
issn = {1742-2094},
doi = {10.1186/s12974-026-03838-8},
url = {https://doi.org/10.1186/s12974-026-03838-8},
pmid = {42069589},
pmcid = {PMC13281619}
}

RIS

TY - JOUR
AU - Wen, Yuwen
AU - Dou, Ya-nan
AU - Chen, Xiaohong
AU - Liu, Xiuxing
AU - Yang, Zhenlan
AU - Zhu, Yingting
AU - Li, Zhidong
AU - Deng, Caibin
AU - Deng, Ye
AU - Su, Wenru
AU - Zhuo, Yehong
TI - Acarbose modulates microglial Pkm2 acetylation to reshape immunometabolism and preserve retinal neurons after ischemia-reperfusion
T2 - Journal of neuroinflammation
J2 - J Neuroinflammation
PY - 2026
DA - 2026/05/02
VL - 23
IS - 1
SP - 206
SN - 1742-2094
PB - BMC
DO - 10.1186/s12974-026-03838-8
UR - https://doi.org/10.1186/s12974-026-03838-8
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12974-026-03838-8",
"type": "article-journal",
"title": "Acarbose modulates microglial Pkm2 acetylation to reshape immunometabolism and preserve retinal neurons after ischemia-reperfusion",
"container-title": "Journal of neuroinflammation",
"author": [
{
"family": "Wen",
"given": "Yuwen"
},
{
"family": "Dou",
"given": "Ya-nan"
},
{
"family": "Chen",
"given": "Xiaohong"
},
{
"family": "Liu",
"given": "Xiuxing"
},
{
"family": "Yang",
"given": "Zhenlan"
},
{
"family": "Zhu",
"given": "Yingting"
},
{
"family": "Li",
"given": "Zhidong"
},
{
"family": "Deng",
"given": "Caibin"
},
{
"family": "Deng",
"given": "Ye"
},
{
"family": "Su",
"given": "Wenru"
},
{
"family": "Zhuo",
"given": "Yehong"
}
],
"container-title-short": "J Neuroinflammation",
"volume": "23",
"issue": "1",
"page": "206",
"DOI": "10.1186/s12974-026-03838-8",
"PMID": "42069589",
"PMCID": "PMC13281619",
"ISSN": "1742-2094",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12974-026-03838-8",
"language": "en",
"issued": {
"date-parts": [
[
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5,
2
]
]
}
}

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