Hello everyone,

I have Single-cell RNA seq data consisting of 5 samples, untreated (day 0) and treated (day 1, 3, 5, and 10). I want to see the see DGE of the selected cluster between treated and untreated groups. I am using the following codes but it gives me an error, I am new to R and can not figure out where is the problem, I will really appreciate help in this regard.
(Note: Computer RAM 20GB)

######Libraries loaded##########
library(Seurat)
library(DESeq2)
library(ggplot2)
library(pheatmap)
library(patchwork)
library(dittoSeq)
library(parallel)
seurat <- readRDS("H:/Manuscripts/DS-Model_ScRNA_Seq_Analysis/DS_model_DR20.rds")
seurat$days <- plyr::mapvalues(seurat$orig.ident, from = c("S1_NS","S2_DS1","S3_DS3","S4_DS5","S5_DS10"), to = c(0, 1, 3, 5, 10))
  # Load dataset
counts <- seurat@assays$RNA@counts
metadata <- seurat@meta.data

  # Run DESeq2
dds <- DESeqDataSetFromMatrix(countData = round(counts),
                                colData = metadata,
                                design = ~days)
dds <- scran::computeSumFactors(dds)
#> dds <- DESeq(dds, test="LRT", reduced = ~1, useT=TRUE, minmu=1e-6, minReplicatesForReplace=Inf)
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates
Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'obj' in selecting a method for function 'unname': error in evaluating the argument 'x' in selecting a method for function 'rowSums': cannot allocate vector of size 5.5 Gb



sessionInfo( )

R version 4.1.2 (2021-11-01) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages: [1] stats4 stats graphics grDevices utils datasets methods base

other attached packages: [1] glmGamPoi_1.6.0 BiocManager_1.30.16 limma_3.50.0
[4] SeuratObject_4.0.4 DESeq2_1.34.0 SummarizedExperiment_1.24.0 [7] Biobase_2.54.0 MatrixGenerics_1.6.0 matrixStats_0.61.0
[10] GenomicRanges_1.46.1 GenomeInfoDb_1.30.0 IRanges_2.28.0
[13] S4Vectors_0.32.3 BiocGenerics_0.40.0 Matrix_1.3-4

loaded via a namespace (and not attached): [1] httr_1.4.2 edgeR_3.36.0 BiocSingular_1.10.0
[4] bit64_4.0.5 splines_4.1.2 scuttle_1.4.0
[7] DelayedMatrixStats_1.16.0 assertthat_0.2.1 statmod_1.4.36
[10] dqrng_0.3.0 blob_1.2.2 GenomeInfoDbData_1.2.7
[13] pillar_1.6.5 RSQLite_2.2.10 lattice_0.20-45
[16] glue_1.6.1 beachmat_2.10.0 RColorBrewer_1.1-2
[19] XVector_0.34.0 colorspace_2.0-2 plyr_1.8.6
[22] XML_3.99-0.9 pkgconfig_2.0.3 genefilter_1.76.0
[25] zlibbioc_1.40.0 purrr_0.3.4 xtable_1.8-4
[28] scales_1.1.1 ScaledMatrix_1.2.0 BiocParallel_1.28.3
[31] tibble_3.1.6 annotate_1.72.0 KEGGREST_1.34.0
[34] generics_0.1.1 ggplot2_3.3.5 ellipsis_0.3.2
[37] cachem_1.0.6 survival_3.2-13 magrittr_2.0.1
[40] crayon_1.4.2 memoise_2.0.1 fansi_1.0.2
[43] bluster_1.4.0 tools_4.1.2 lifecycle_1.0.1
[46] munsell_0.5.0 locfit_1.5-9.4 cluster_2.1.2
[49] DelayedArray_0.20.0 irlba_2.3.5 AnnotationDbi_1.56.2
[52] Biostrings_2.62.0 compiler_4.1.2 rsvd_1.0.5
[55] rlang_0.4.12 grid_4.1.2 RCurl_1.98-1.5
[58] BiocNeighbors_1.12.0 rstudioapi_0.13 SingleCellExperiment_1.16.0 [61] igraph_1.2.11 bitops_1.0-7 gtable_0.3.0
[64] DBI_1.1.2 R6_2.5.1 dplyr_1.0.7
[67] fastmap_1.1.0 bit_4.0.4 utf8_1.2.2
[70] metapod_1.2.0 parallel_4.1.2 Rcpp_1.0.8
[73] scran_1.22.1 vctrs_0.3.8 geneplotter_1.72.0
[76] png_0.1-7 tidyselect_1.1.1 sparseMatrixStats_1.6.0

What is the dimension of dds: dim(dds)?

You may want to do some minimal filtering before running DESeq2. Likely many genes have too low total count here.

Also, for single cell analysis we recommend fitType="glmGamPoi", see the current vignette.