Question

runPCA in a SingleCellExperiment results ina colMeans error

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Lluís Revilla Sancho ▴ 760

@lluis-revilla-sancho

Last seen 7 days ago

European Union

I'm testing analyzing an example dataset from the ParseBioscience, they have a tutorial with Seurat but I tried to use Bioconductor instead.

I'm having troubles running a PCA because there is some problem with the data from a dgTMatrix (from the Matrix package).
The data files read initially are a PBMC dataset provided by them:

library("Matrix")
library("SingleCellExperiment")
library("scuttle")
library("BiocSingular")
mat <- readMM("data/PBMCS/DGE2.mtx")
metadata <- read.csv("data/PBMCS/cell_metadata2.csv")
genes <- read.csv("data/PBMCS/all_genes2.csv")
matt <- t(mat) # To convert to genes x samples
sce <- SingleCellExperiment(assays = SimpleList(counts = matt), rowData = genes, colData = metadata)
scee <- sce |> 
  logNormCounts() |> 
  runPCA(rank = 50)
## dimnames(.) <- NULL translated to
## dimnames(.) <- list(NULL,NULL)
## dimnames(.) <- NULL translated to
## dimnames(.) <- list(NULL,NULL)
## dimnames(.) <- NULL translated to
## dimnames(.) <- list(NULL,NULL)
## Error in base::colMeans(x, na.rm = na.rm, dims = dims, ...) : 
##   'x' must be an array of at least two dimensions

I'm using BiocSingular 1.16.0 and I don't see anything weird in the data, the process or the traceback:

traceback()
16: stop("'x' must be an array of at least two dimensions")
15: base::colMeans(x, na.rm = na.rm, dims = dims, ...)
14: colMeans(x)
13: colMeans(x)
12: .compute_center_and_scale(x, center, scale)
11: standardize_matrix(x, center = center, scale = scale, deferred = deferred, 
        BPPARAM = BPPARAM)
10: (function (x, k = min(dim(x)), nu = k, nv = k, center = FALSE, 
        scale = FALSE, deferred = FALSE, fold = Inf, BPPARAM = SerialParam()) 
    {
        checked <- check_numbers(x, k = k, nu = nu, nv = nv)
        k <- checked$k
        nv <- checked$nv
        nu <- checked$nu
        old <- getAutoBPPARAM()
        setAutoBPPARAM(BPPARAM)
        on.exit(setAutoBPPARAM(old))
        if (!.bpisup2(BPPARAM)) {
            bpstart(BPPARAM)
            on.exit(bpstop(BPPARAM), add = TRUE)
        }
        x <- standardize_matrix(x, center = center, scale = scale, 
            deferred = deferred, BPPARAM = BPPARAM)
        if (use_crossprod(x, fold)) {
            res <- svd_via_crossprod(x, k = k, nu = nu, nv = nv, 
                FUN = safe_svd)
        }
     ...
9: do.call(FUN, c(list(x = x, k = k, nu = nu, nv = nv, center = center, 
       scale = scale, BPPARAM = BPPARAM, ...), ARGS(BSPARAM)))
8: (new("standardGeneric", .Data = function (x, k, nu = k, nv = k, 
       center = FALSE, scale = FALSE, BPPARAM = SerialParam(), ..., 
       BSPARAM = ExactParam()) 
   standardGeneric("runSVD"), generic = structure("runSVD", package = "BiocSingular"), 
       package = "BiocSingular", group = list(), valueClass = character(0), 
       signature = "BSPARAM", default = NULL, skeleton = (function (x, 
           k, nu = k, nv = k, center = FALSE, scale = FALSE, BPPARAM = SerialParam(), 
           ..., BSPARAM = ExactParam()) 
       stop(gettextf("invalid call in method dispatch to '%s' (no default method)", 
           "runSVD"), domain = NA))(x, k, nu, nv, center, scale, 
           BPPARAM, ..., BSPARAM = BSPARAM)))(x = new("SingleCellExperiment", 
       int_elementMetadata = new("DFrame", rownames = NULL, nrows = 62703L, 
           elementType = "ANY", elementMetadata = NULL, metadata = list(), 
           listData = list(rowPairs = new("DFrame", rownames = NULL, 
               nrows = 62703L, elementType = "ANY", elementMetadata = NULL, 
               metadata = list(), listData = structure(list(), names = character(0))))), 
       int_colData = new("DFrame", rownames = NULL, nrows = 5017L, 
           elementType = "ANY", elementMetadata = NULL, metadata = list(), 
           listData = list(reducedDims = new("DFrame", rownames = NULL, 
               nrows = 5017L, elementType = "ANY", elementMetadata = NULL, 
    ...
7: (new("standardGeneric", .Data = function (x, k, nu = k, nv = k, 
       center = FALSE, scale = FALSE, BPPARAM = SerialParam(), ..., 
       BSPARAM = ExactParam()) 
   standardGeneric("runSVD"), generic = structure("runSVD", package = "BiocSingular"), 
       package = "BiocSingular", group = list(), valueClass = character(0), 
       signature = "BSPARAM", default = NULL, skeleton = (function (x, 
           k, nu = k, nv = k, center = FALSE, scale = FALSE, BPPARAM = SerialParam(), 
           ..., BSPARAM = ExactParam()) 
       stop(gettextf("invalid call in method dispatch to '%s' (no default method)", 
           "runSVD"), domain = NA))(x, k, nu, nv, center, scale, 
           BPPARAM, ..., BSPARAM = BSPARAM)))(x = new("SingleCellExperiment", 
       int_elementMetadata = new("DFrame", rownames = NULL, nrows = 62703L, 
           elementType = "ANY", elementMetadata = NULL, metadata = list(), 
           listData = list(rowPairs = new("DFrame", rownames = NULL, 
               nrows = 62703L, elementType = "ANY", elementMetadata = NULL, 
               metadata = list(), listData = structure(list(), names = character(0))))), 
       int_colData = new("DFrame", rownames = NULL, nrows = 5017L, 
           elementType = "ANY", elementMetadata = NULL, metadata = list(), 
           listData = list(reducedDims = new("DFrame", rownames = NULL, 
               nrows = 5017L, elementType = "ANY", elementMetadata = NULL, 
    ...
6: do.call(FUN, c(list(x = x, k = k, nu = nu, nv = nv, center = center, 
       scale = scale, BPPARAM = BPPARAM, ...), ARGS(BSPARAM)))
5: runSVD(x, k = rank, nu = ifelse(get.pcs, rank, 0), nv = ifelse(get.rotation, 
       rank, 0), center = center, scale = scale, ...)
4: runSVD(x, k = rank, nu = ifelse(get.pcs, rank, 0), nv = ifelse(get.rotation, 
       rank, 0), center = center, scale = scale, ...)
3: .local(x, ...)
2: runPCA(logNormCounts(sce), rank = 50)
1: runPCA(logNormCounts(sce), rank = 50)
BiocManager::version()
## [1] '3.17'
is(matt)
##  [1] "dgTMatrix"     "TsparseMatrix" "dsparseMatrix" "generalMatrix" "dMatrix"       "sparseMatrix"  "compMatrix"   
##  [8] "Matrix"        "xMatrix"       "mMatrix"       "replValueSp" 
 matt[1:5, 1:5]
## 5 x 5 sparse Matrix of class "dgTMatrix"
##               
## [1,] . . . . .
## [2,] . . . . .
## [3,] . 1 . . 1
## [4,] 2 1 . 1 1
## [5,] . . . . .
head(genes)
##           gene_id gene_name genome
## 1 ENSG00000000003    TSPAN6   hg38
## 2 ENSG00000000005      TNMD   hg38
## 3 ENSG00000000419      DPM1   hg38
## 4 ENSG00000000457     SCYL3   hg38
## 5 ENSG00000000460  C1orf112   hg38
## 6 ENSG00000000938       FGR   hg38
head(metadata)
##   bc_wells   sample species gene_count tscp_count mread_count bc1_well bc2_well bc3_well bc1_wind bc2_wind
## 1 01_01_14 Sample_1    hg38       3077       7477       29532       A1       A1       B2        1        1
## 2 01_01_31 Sample_1    hg38       2619       5776       21785       A1       A1       C7        1        1
## 3 01_01_66 Sample_1    hg38       2648       5906       22928       A1       A1       F6        1        1
## 4 01_01_70 Sample_1    hg38       2339       4698       18252       A1       A1      F10        1        1
## 5 01_02_03 Sample_1    hg38       2009       4001       15789       A1       A2       A3        1        2
## 6 01_02_62 Sample_1    hg38       2481       5093       19933       A1       A2       F2        1        2
##   bc3_wind gene_count2
## 1       14        3077
## 2       31        2619
## 3       66        2648
## 4       70        2339
## 5        3        2009
## 6       62        2481

scran SingleCellExperiment • 1.3k views

ADD COMMENT • link 17 months ago Lluís Revilla Sancho ▴ 760

score 0 · Answer 1 · 2023-06-21

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ATpoint ★ 4.5k

@atpoint-13662

Last seen 1 day ago

Germany

Not sure why, but when you specify rank, then the runPCA from BiocSingular needs a matrix, else it can accept a SCE. Anyway, just use scater, this works with SCE well.

library(SingleCellExperiment)
library(scuttle)
library(BiocSingular)
library(scater)

sce <- mockSCE()
sce <- logNormCounts(sce)

# ok
BiocSingular::runPCA(sce)

# error
BiocSingular::runPCA(sce, rank=50)

# ok and should be identical to above
scater::runPCA(sce, ncomponents=50)

I would also use dgCMatrix, in my hands that has a smaller memory footprint and faster processing times for many functions. dgC rather than dgT is also used for example by DropletUtils: https://github.com/MarioniLab/DropletUtils/blob/devel/R/read10xCounts.R#L275

ADD COMMENT • link 17 months ago ATpoint ★ 4.5k

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Thanks for the tip to convert it to dgCMatrix I had a 25% memory decrease. It is the first time using this big datasets and I am not familiar with all the practical memory reduction tricks. Precisely what I want to know with this approach is if the droplet based methods work with the combinatorics approach used here.

ADD REPLY • link 17 months ago Lluís Revilla Sancho ▴ 760