You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I am working with a very larger sc/sn RNA-Seq dataset. Starting from an h5ad file have used BPcells package to load data in-memory as follows:
`raw <- open_matrix_anndata_hdf5(path="/novo/projects/departments/compbio/sysbio/Projects/mouse_liver_models/single_cell_and_nuclei/concatenated.dir/concatenated.h5ad") #imports as data type float
raw <- convert_matrix_type(raw, type = "uint32_t") #must convert count matrix from type float (non-integer) to integer values
write_matrix_dir(mat = raw, dir = "/novo/projects/shared_projects/liver_biology_colab/people/aqnf/mouse_sc_sn_AQNF_June24/BPcells/mouse_counts")
I am working with seurat v5, so I am trying to split layers based on the perepartion method (single cell and single nuc seq). After that I am creating a sketch assay for my seurat object in-memory in order to run downstream analysis more efficiently (the dataset is to large for the available memory):
Up to that point everything runs fine but then when I try to get started with the dimensionality reduction I am running into issues that I don't understand. It seems like something goes wrong when trying to RunPCA, as the Ellbow plot looks very weird and other steps of the pipeline relying on the pca, fail to run. I tried to trace the issue but have failed, so help is very welcome:
Load compressed matrix from directory /novo/projects/shared_projects/liver_biology_colab/people/aqnf/mouse_sc_sn_AQNF_June24/BPcells/mouse_counts
sobj
An object of class Seurat
33696 features across 1191094 samples within 1 assay
Active assay: RNA (33696 features, 2000 variable features)
4 layers present: counts.SC, counts.SN, data.SC, data.SN
sobj.sketch
An object of class Seurat
67392 features across 1191094 samples within 2 assays
Active assay: sketch (33696 features, 2000 variable features)
5 layers present: counts.SC, counts.SN, data.SC, data.SN, scale.data
1 other assay present: RNA
1 dimensional reduction calculated: pca
sobj.sketch@assays$sketch
Assay (v5) data with 33696 features for 1e+05 cells
Top 10 variable features:
Mmp12, Igfbp5, Igkc, Nxph1, Kcnip4, Ighm, Grm8, Nrg1, Jchain, Siglech
Layers:
counts.SC, counts.SN, data.SC, data.SN, scale.data
Ellbow Plot after RunPCA on sketch assay in sobj.sketch
Hi,
I am working with a very larger sc/sn RNA-Seq dataset. Starting from an h5ad file have used BPcells package to load data in-memory as follows:
`raw <- open_matrix_anndata_hdf5(path="/novo/projects/departments/compbio/sysbio/Projects/mouse_liver_models/single_cell_and_nuclei/concatenated.dir/concatenated.h5ad") #imports as data type float
raw <- convert_matrix_type(raw, type = "uint32_t") #must convert count matrix from type float (non-integer) to integer values
write_matrix_dir(mat = raw, dir = "/novo/projects/shared_projects/liver_biology_colab/people/aqnf/mouse_sc_sn_AQNF_June24/BPcells/mouse_counts")
raw.mat <- open_matrix_dir(dir = "/novo/projects/shared_projects/liver_biology_colab/people/aqnf/mouse_sc_sn_AQNF_June24/BPcells/mouse_counts")
sobj <- CreateSeuratObject(counts = raw.mat)
meta <- merge(x= metadata_BSCK, y= metadata_CPDM, by.x = "LibraryID", by.y = "library_id", all.y=T)
sobj<- AddMetaData(sobj, metadata = meta)`
I am working with seurat v5, so I am trying to split layers based on the perepartion method (single cell and single nuc seq). After that I am creating a sketch assay for my seurat object in-memory in order to run downstream analysis more efficiently (the dataset is to large for the available memory):
`sobj <- subset(sobj, subset = nCount_RNA < 50000 & nFeature_RNA > 250 & nFeature_RNA < 8000 & pct_ribo < 20)
sobj[["RNA"]] <- split(sobj[["RNA"]], f = sobj$group)
sobj <- NormalizeData(sobj)
sobj <- FindVariableFeatures(sobj)
sobj.sketch <- SketchData(
object = sobj,
ncells = 50000,
method = "LeverageScore",
sketched.assay = "sketch")
DefaultAssay(sobj.sketch) <- "sketch"`
Up to that point everything runs fine but then when I try to get started with the dimensionality reduction I am running into issues that I don't understand. It seems like something goes wrong when trying to RunPCA, as the Ellbow plot looks very weird and other steps of the pipeline relying on the pca, fail to run. I tried to trace the issue but have failed, so help is very welcome:
`sobj.sketch <- FindVariableFeatures(sobj.sketch)
sobj.sketch <- ScaleData(sobj.sketch)
sobj.sketch <- RunPCA(sobj.sketch)
sobj.sketch <- FindNeighbors(sobj.sketch, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
Error: std::bad_alloc`
The text was updated successfully, but these errors were encountered: