ObjectConversion

Introduction

There are many tools have been developed to process scRNA-seq data, such as Scanpy, Seurat, scran and Monocle. These tools have their own objects, such as AnnData of Scanpy, SeuratObject of Seurat, SingleCellExperiment of scran and CellDataSet/cell_data_set of Monocle2/Monocle3. There are also some file format designed for large omics datasets, such as loom. To perform a comprehensive scRNA-seq data analysis, we usually need to combine multiple tools, which means we need to perform object conversion frequently.

To facilitate user analysis of scRNA-seq data, GEfetch2R:

• benchmarked the format conversion tools and provides some guides for tool selection under different scenarios(AnnData -> SeuratObject, SeuratObject to AnnData, AnnData to SingleCellExperiment, SingleCellExperiment to AnnData)
• provides multiple functions to perform format conversion between widely used scRNA-seq objects (SeuratObject, AnnData, SingleCellExperiment, CellDataSet/cell_data_set and loom)

---

Test data

# library
library(GEfetch2R)
library(Seurat) # pbmc_small
# library(scRNAseq) # seger

SeuratObject:

# object
pbmc_small
# An object of class Seurat
# 230 features across 80 samples within 1 assay
# Active assay: RNA (230 features, 20 variable features)
#  2 dimensional reductions calculated: pca, tsne

# metadata
head(pbmc_small@meta.data)
#                   orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
# ATGCCAGAACGACT SeuratProject         70           47               0             A     g2
# CATGGCCTGTGCAT SeuratProject         85           52               0             A     g1
# GAACCTGATGAACC SeuratProject         87           50               1             B     g2
# TGACTGGATTCTCA SeuratProject        127           56               0             A     g2
# AGTCAGACTGCACA SeuratProject        173           53               0             A     g2
# TCTGATACACGTGT SeuratProject         70           48               0             A     g1
#                RNA_snn_res.1
# ATGCCAGAACGACT             0
# CATGGCCTGTGCAT             0
# GAACCTGATGAACC             0
# TGACTGGATTCTCA             0
# AGTCAGACTGCACA             0
# TCTGATACACGTGT             0

SingleCellExperiment:

# seger <- scRNAseq::SegerstolpePancreasData()
# load from local
seger <- readRDS("/home/songyabing/gefetch2r/doc/conversion/seger.rds")
seger
# class: SingleCellExperiment
# dim: 26179 3514
# metadata(0):
# assays(1): counts
# rownames(26179): SGIP1 AZIN2 ... BIVM-ERCC5 eGFP
# rowData names(2): refseq symbol
# colnames(3514): HP1502401_N13 HP1502401_D14 ... HP1526901T2D_O11 HP1526901T2D_A8
# colData names(9): individual sex ... submitted single cell quality cell type
# reducedDimNames(0):
# altExpNames(1): ERCC

AnnData (generate_pbmc3k_anndata.ipynb):

import scanpy as sc
pbmc3k = sc.read('/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad')
pbmc3k
# AnnData object with n_obs × n_vars = 2638 × 1838
#     obs: 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'leiden'
#     var: 'gene_ids', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'mean', 'std'
#     uns: 'hvg', 'leiden', 'log1p', 'neighbors', 'pca', 'rank_genes_groups', 'umap'
#     obsm: 'X_pca', 'X_umap'
#     varm: 'PCs'
#     layers: 'logcounts', 'rawcounts'
#     obsp: 'connectivities', 'distances'

---

Convert SeuratObject to other objects

Here, we will convert SeuratObject to SingleCellExperiment, CellDataSet/cell_data_set, AnnData, loom.

SeuratObject to SingleCellExperiment

The conversion is performed with functions implemented in Seurat:

sce.obj <- ExportSeurat(seu.obj = pbmc_small, assay = "RNA", to = "SCE")
sce.obj
# class: SingleCellExperiment
# dim: 230 80
# metadata(0):
# assays(2): counts logcounts
# rownames(230): MS4A1 CD79B ... SPON2 S100B
# rowData names(5): vst.mean vst.variance vst.variance.expected vst.variance.standardized
#   vst.variable
# colnames(80): ATGCCAGAACGACT CATGGCCTGTGCAT ... GGAACACTTCAGAC CTTGATTGATCTTC
# colData names(8): orig.ident nCount_RNA ... RNA_snn_res.1 ident
# reducedDimNames(2): PCA TSNE
# altExpNames(0):

---

SeuratObject to CellDataSet/cell_data_set

To CellDataSet (The conversion is performed with functions implemented in Seurat):

# BiocManager::install("monocle") # reuqire monocle
cds.obj <- ExportSeurat(seu.obj = pbmc_small, assay = "RNA", reduction = "tsne", to = "CellDataSet")
cds.obj
# CellDataSet (storageMode: environment)
# assayData: 230 features, 80 samples
#   element names: exprs
# protocolData: none
# phenoData
#   sampleNames: ATGCCAGAACGACT CATGGCCTGTGCAT ... CTTGATTGATCTTC (80 total)
#   varLabels: orig.ident nCount_RNA ... Size_Factor (8 total)
#   varMetadata: labelDescription
# featureData
#   featureNames: MS4A1 CD79B ... S100B (230 total)
#   fvarLabels: vst.mean vst.variance ... gene_short_name (6 total)
#   fvarMetadata: labelDescription
# experimentData: use 'experimentData(object)'
# Annotation:

To cell_data_set (The conversion is performed with functions implemented in SeuratWrappers):

# remotes::install_github('cole-trapnell-lab/monocle3') # reuqire monocle3
cds3.obj <- ExportSeurat(seu.obj = pbmc_small, assay = "RNA", to = "cell_data_set")
cds3.obj
# class: cell_data_set
# dim: 230 80
# metadata(0):
# assays(2): counts logcounts
# rownames(230): MS4A1 CD79B ... SPON2 S100B
# rowData names(0):
# colnames(80): ATGCCAGAACGACT CATGGCCTGTGCAT ... GGAACACTTCAGAC CTTGATTGATCTTC
# colData names(9): orig.ident nCount_RNA ... ident Size_Factor
# reducedDimNames(2): PCA TSNE
# mainExpName: RNA
# altExpNames(0):

---

SeuratObject to AnnData

There are multiple tools available for format conversion from SeuratObject to AnnData:

• scDIOR is the best method in terms of information kept and usability
• sceasy has best performance in running time and disk usage.

# sceasy, generate pbmc_small_sceasy.h5ad
Seu2AD(
  seu.obj = pbmc_small, method = "sceasy", out.folder = "/home/songyabing/gefetch2r/doc/conversion",
  assay = "RNA", slot = "counts", conda.path = "/home/songyabing/anaconda3"
)
# AnnData object with n_obs × n_vars = 80 × 230
#     obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'RNA_snn_res.0.8', 'letter.idents', 'groups', 'RNA_snn_res.1'
#     var: 'vst.mean', 'vst.variance', 'vst.variance.expected', 'vst.variance.standardized', 'vst.variable'
#     obsm: 'X_pca', 'X_tsne'

# SeuratDisk, generate pbmc_small_SeuratDisk.h5Seurat, pbmc_small_SeuratDisk.h5ad
Seu2AD(
  seu.obj = pbmc_small, method = "SeuratDisk", out.folder = "/home/songyabing/gefetch2r/doc/conversion",
  assay = "RNA", save.scale = TRUE
)
# Creating h5Seurat file for version 3.1.5.9900
# Adding counts for RNA
# Adding data for RNA
# Adding scale.data for RNA
# Adding variable features for RNA
# Adding feature-level metadata for RNA
# Adding cell embeddings for pca
# Adding loadings for pca
# Adding projected loadings for pca
# Adding standard deviations for pca
# Adding JackStraw information for pca
# Adding cell embeddings for tsne
# No loadings for tsne
# No projected loadings for tsne
# No standard deviations for tsne
# No JackStraw data for tsne
# Validating h5Seurat file
# Adding scale.data from RNA as X
# Transfering meta.features to var
# Adding data from RNA as raw
# Transfering meta.features to raw/var
# Transfering meta.data to obs
# Adding dimensional reduction information for pca
# Adding feature loadings for pca
# Adding dimensional reduction information for tsne
# Adding RNA_snn as neighbors
# [1] "/home/songyabing/gefetch2r/doc/conversion/pbmc_small_SeuratDisk.h5ad"

# # scDIOR, generate pbmc_small_scDIOR.h5
Seu2AD(
  seu.obj = pbmc_small, method = "scDIOR",
  out.folder = "/home/songyabing/gefetch2r/doc/conversion", assay = "RNA", save.scale = TRUE
)
# NULL
# Warning message:
# In matrix_to_h5(mat = slot(object = slot_assay, name = "scale.data"),  :
#   sdata is dense matrix

---

SeuratObject to loom

The conversion is performed with functions implemented in SeuratDisk:

ExportSeurat(
  seu.obj = pbmc_small, assay = "RNA", to = "loom",
  loom.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc_small.loom"
)
# Convert SeuratObject to loom!
# Saving data from RNA as /matrix
#   |========================================================================================| 100%
# Adding slot counts for assay RNA
# Adding layer counts
#   |========================================================================================| 100%
# Adding col attribute CellID
# Adding col attribute orig.ident
# Adding col attribute nCount_RNA
# Adding col attribute nFeature_RNA
# Adding col attribute RNA_snn_res.0.8
# Adding col attribute letter.idents
# Adding col attribute groups
# Adding col attribute RNA_snn_res.1
# Adding row attribute Gene

---

Convert other objects to SeuratObject

SingleCellExperiment to SeuratObject

The conversion is performed with functions implemented in Seurat:

seu.obj.sce <- ImportSeurat(
  obj = sce.obj, from = "SCE", count.assay = "counts",
  data.assay = "logcounts", assay = "RNA"
)
# Convert SingleCellExperiment to SeuratObject!
# The assay you provided: RNA is not in the cell_data_set object: . Change assay to NULL # (Seurat 4.4.0)
# Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from PC__ to PC_
# Warning: All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to PC_
# Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from tSNE__ to tSNE_
# Warning: All keys should be one or more alphanumeric characters followed by an underscore '_', setting key to tSNE_
seu.obj.sce
# An object of class Seurat
# 230 features across 80 samples within 1 assay
# Active assay: RNA (230 features, 0 variable features)
#  2 dimensional reductions calculated: pca, tsne

---

CellDataSet/cell_data_set to SeuratObject

CellDataSet to SeuratObject (The conversion is performed with functions implemented in Seurat):

seu.obj.cds <- ImportSeurat(
  obj = cds.obj, from = "CellDataSet",
  count.assay = "counts", assay = "RNA"
)
# Convert CellDataSet (Monocle) to SeuratObject!
# Pulling expression data
# Building Seurat object
# Adding feature-level metadata
# No dispersion information in CellDataSet object
# No variable features present
# Adding tSNE dimensional reduction
seu.obj.cds
# An object of class Seurat
# 230 features across 80 samples within 1 assay
# Active assay: RNA (230 features, 0 variable features)
#  1 dimensional reduction calculated: tsne

cell_data_set to SeuratObject (The conversion is performed with functions implemented in Seurat):

seu.obj.cds3 <- ImportSeurat(
  obj = cds3.obj, from = "cell_data_set",
  count.assay = "counts", data.assay = "logcounts", assay = "RNA"
)
# Convert cell_data_set (Monocle3) to SeuratObject!
# The assay you provided: RNA is not in the cell_data_set object: . Change assay to NULL # (Seurat 4.4.0)
seu.obj.cds3
# An object of class Seurat
# 230 features across 80 samples within 1 assay
# Active assay: RNA (230 features, 0 variable features)
#  2 dimensional reductions calculated: pca, tsne

---

AnnData to SeuratObject

There are multiple tools available for format conversion from AnnData to SeuratObject:

• scDIOR is the best method in terms of information kept (GEfetch2R integrates scDIOR and SeuratDisk to achieve the best performance in information kept)
• schard is the best method in terms of usability
• schard and sceasy have comparable performance when cell number below 200k, but sceasy has better performance in scalability
• sceasy has better performance in disk usage

# sceasy
ann.sceasy <- AD2Seu(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad", method = "sceasy",
  assay = "RNA", slot = "scale.data"
)
# Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
# X -> scale.data; raw.X -> data
ann.sceasy
# An object of class Seurat
# 13714 features across 2638 samples within 1 assay
# Active assay: RNA (13714 features, 0 variable features)
#  2 dimensional reductions calculated: pca, umap

# SeuratDisk
ann.seu <- AD2Seu(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "SeuratDisk", assay = "RNA", load.assays = c("RNA")
)
# Warning: Unknown file type: h5ad
# Creating h5Seurat file for version 3.1.5.9900
# Adding X as scale.data
# Adding raw/X as data
# Adding raw/X as counts
# Adding meta.features from raw/var
# Adding dispersions from scaled feature-level metadata
# Adding dispersions_norm from scaled feature-level metadata
# Merging gene_ids from scaled feature-level metadata
# Adding highly_variable from scaled feature-level metadata
# Adding mean from scaled feature-level metadata
# Merging mean_counts from scaled feature-level metadata
# Adding means from scaled feature-level metadata
# Merging mt from scaled feature-level metadata
# Merging n_cells from scaled feature-level metadata
# Merging n_cells_by_counts from scaled feature-level metadata
# Merging pct_dropout_by_counts from scaled feature-level metadata
# Adding std from scaled feature-level metadata
# Merging total_counts from scaled feature-level metadata
# Adding X_pca as cell embeddings for pca
# Adding X_umap as cell embeddings for umap
# Adding PCs as feature loadings fpr pca
# Adding miscellaneous information for pca
# Adding standard deviations for pca
# Adding miscellaneous information for umap
# Adding hvg to miscellaneous data
# Adding leiden to miscellaneous data
# Adding log1p to miscellaneous data
# Adding rank_genes_groups to miscellaneous data
# Adding layer logcounts as data in assay logcounts
# Adding layer rawcounts as data in assay rawcounts
# Validating h5Seurat file
# Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
# Initializing RNA with data
# Adding counts for RNA
# Adding scale.data for RNA
# Adding feature-level metadata for RNA
# Adding reduction pca
# Adding cell embeddings for pca
# Adding feature loadings for pca
# Adding miscellaneous information for pca
# Adding reduction umap
# Adding cell embeddings for umap
# Adding miscellaneous information for umap
# Adding command information
# Adding cell-level metadata
ann.seu
# An object of class Seurat
# 13714 features across 2638 samples within 1 assay
# Active assay: RNA (13714 features, 0 variable features)
#  2 dimensional reductions calculated: pca, umap

# scDIOR
ann.scdior <- AD2Seu(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "scDIOR", assay = "RNA"
)
# Warning: No columnames present in cell embeddings, setting to 'PCA_1:50'
# Warning: No columnames present in cell embeddings, setting to 'UMAP_1:2'
# Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
ann.scdior
# An object of class Seurat
# 17390 features across 2638 samples within 3 assays
# Active assay: RNA (13714 features, 0 variable features)
#  2 other assays present: logcounts, rawcounts
#  2 dimensional reductions calculated: pca, umap

# schard
ann.schard <- AD2Seu(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "schard", assay = "RNA", use.raw = T
)
# Warning: Keys should be one or more alphanumeric characters followed by an underscore, setting key from rna to rna_
# Warning: Invalid name supplied, making object name syntactically valid. New object name is X_indexn_genesn_genes_by_countstotal_countstotal_counts_mtpct_counts_mtleiden; see ?make.names for more details on syntax validity
ann.schard
# An object of class Seurat
# 13714 features across 2638 samples within 1 assay
# Active assay: RNA (13714 features, 0 variable features)
#  2 dimensional reductions calculated: Xpca_, Xumap_

# SeuratDisk+scDIOR
ann.seuscdior <- AD2Seu(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "SeuratDisk+scDIOR", assay = "RNA", load.assays = c("RNA")
)
# Warning: Unknown file type: h5ad
# Creating h5Seurat file for version 3.1.5.9900
# Adding X as scale.data
# Adding raw/X as data
# Adding raw/X as counts
# Adding meta.features from raw/var
# Adding dispersions from scaled feature-level metadata
# Adding dispersions_norm from scaled feature-level metadata
# Merging gene_ids from scaled feature-level metadata
# Adding highly_variable from scaled feature-level metadata
# Adding mean from scaled feature-level metadata
# Merging mean_counts from scaled feature-level metadata
# Adding means from scaled feature-level metadata
# Merging mt from scaled feature-level metadata
# Merging n_cells from scaled feature-level metadata
# Merging n_cells_by_counts from scaled feature-level metadata
# Merging pct_dropout_by_counts from scaled feature-level metadata
# Adding std from scaled feature-level metadata
# Merging total_counts from scaled feature-level metadata
# Adding X_pca as cell embeddings for pca
# Adding X_umap as cell embeddings for umap
# Adding PCs as feature loadings fpr pca
# Adding miscellaneous information for pca
# Adding standard deviations for pca
# Adding miscellaneous information for umap
# Adding hvg to miscellaneous data
# Adding leiden to miscellaneous data
# Adding log1p to miscellaneous data
# Adding rank_genes_groups to miscellaneous data
# Adding layer logcounts as data in assay logcounts
# Adding layer rawcounts as data in assay rawcounts
# Validating h5Seurat file
# Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
# Initializing RNA with data
# Adding counts for RNA
# Adding scale.data for RNA
# Adding feature-level metadata for RNA
# Adding reduction pca
# Adding cell embeddings for pca
# Adding feature loadings for pca
# Adding miscellaneous information for pca
# Adding reduction umap
# Adding cell embeddings for umap
# Adding miscellaneous information for umap
# Adding command information
# Adding cell-level metadata
# Warning: No columnames present in cell embeddings, setting to 'PCA_1:50'
# Warning: No columnames present in cell embeddings, setting to 'UMAP_1:2'
# Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
ann.seuscdior
# An object of class Seurat
# 17390 features across 2638 samples within 3 assays
# Active assay: RNA (13714 features, 0 variable features)
#  2 other assays present: logcounts, rawcounts
#  2 dimensional reductions calculated: pca, umap

---

loom to SeuratObject

The conversion is performed with functions implemented in SeuratDisk and Seurat:

# loom will lose reduction
seu.obj.loom <- ImportSeurat(loom.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc_small.loom", from = "loom")
# Convert loom to SeuratObject!
# Reading in /matrix
# Storing /matrix as counts
# Saving /matrix to assay 'RNA'
# Loading graph RNA_snn
seu.obj.loom
# An object of class Seurat
# 230 features across 80 samples within 1 assay
# Active assay: RNA (230 features, 0 variable features)

---

Conversion between SingleCellExperiment and AnnData

SingleCellExperiment to AnnData

There are multiple tools available for format conversion from SingleCellExperiment to AnnData:

• zellkonverter is the best method in terms of information kept and running time
• scDIOR is the best method in terms of usability and disk usage (scDIOR require diopy to read h5 file)

# zellkonverter, output seger_zellkonverter.h5ad
SCE2AD(
  sce.obj = seger, method = "zellkonverter",
  out.folder = "/home/songyabing/gefetch2r/doc/conversion", slot = "counts",
  conda.path = "/home/songyabing/anaconda3"
)
# NULL
# # read seger_zellkonverter.h5ad in python
# import scanpy as sc
# seger_zellkonverter = sc.read('/home/songyabing/gefetch2r/doc/conversion/seger_zellkonverter.h5ad')
# seger_zellkonverter
# AnnData object with n_obs × n_vars = 3514 × 26179
#     obs: 'individual', 'sex', 'age', 'body mass index', 'clinical information', 'disease', 'single cell well quality', 'submitted single cell quality', 'cell type'
#     var: 'refseq', 'symbol'
#     uns: 'X_name'

# sceasy, output seger_sceasy.h5ad
SCE2AD(
  sce.obj = seger, method = "sceasy", out.folder = "/home/songyabing/gefetch2r/doc/conversion",
  slot = "counts", conda.path = "/home/songyabing/anaconda3"
)
# AnnData object with n_obs × n_vars = 3514 × 26179
#     obs: 'individual', 'sex', 'age', 'body.mass.index', 'clinical.information', 'disease', 'single.cell.well.quality', 'submitted.single.cell.quality', 'cell.type'
#     var: 'refseq', 'symbol'

# scDIOR, output seger.scdior_scDIOR.h5
seger.scdior <- seger
library(SingleCellExperiment)
# scDIOR does not support varm in rowData
rowData(seger.scdior)$varm <- NULL
SCE2AD(sce.obj = seger.scdior, method = "scDIOR", out.folder = "/home/songyabing/gefetch2r/doc/conversion")
# [1] "The first 'assayNames' defaults to 'X'"
# [1] "RNA"
# # read seger.scdior_scDIOR.h5 in python
# import diopy
# seger_scdior = diopy.input.read_h5(file = "/home/songyabing/gefetch2r/doc/conversion/seger.scdior_scDIOR.h5")
# seger_scdior
# AnnData object with n_obs × n_vars = 3514 × 26179
#     obs: 'individual', 'sex', 'age', 'body mass index', 'clinical information', 'disease', 'single cell well quality', 'submitted single cell quality', 'cell type'
#     var: 'refseq', 'symbol'

---

AnnData to SingleCellExperiment

There are multiple tools available for format conversion from AnnData to SingleCellExperiment:

• zellkonverter is the best method in terms of information kept
• schard is the best method in terms of usability and running time
• schard and scDIOR have comparable performance in disk usage

# zellkonverter
sce.zell <- AD2SCE(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "zellkonverter", slot = "scale.data",
  use.raw = TRUE, conda.path = "/home/songyabing/anaconda3"
)
sce.zell
# class: SingleCellExperiment
# dim: 1838 2638
# metadata(7): hvg leiden ... rank_genes_groups umap
# assays(3): scale.data logcounts rawcounts
# rownames(1838): TNFRSF4 CPSF3L ... S100B PRMT2
# rowData names(14): gene_ids n_cells ... std varm
# colnames(2638): AAACATACAACCAC-1 AAACATTGAGCTAC-1 ... TTTGCATGAGAGGC-1
#   TTTGCATGCCTCAC-1
# colData names(6): n_genes n_genes_by_counts ... pct_counts_mt leiden
# reducedDimNames(2): X_pca X_umap
# altExpNames(1): raw

# scDIOR
sce.scdior <- AD2SCE(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "scDIOR", assay = "RNA",
  use.raw = TRUE, conda.path = "/home/songyabing/anaconda3"
)
sce.scdior
# class: SingleCellExperiment
# dim: 13714 2638
# metadata(0):
# assays(1): X
# rownames(13714): AL627309.1 AP006222.2 ... PNRC2-1 SRSF10-1
# rowData names(7): gene_ids n_cells ... pct_dropout_by_counts
#   total_counts
# colnames(2638): AAACATACAACCAC-1 AAACATTGAGCTAC-1 ... TTTGCATGAGAGGC-1
#   TTTGCATGCCTCAC-1
# colData names(6): n_genes n_genes_by_counts ... pct_counts_mt leiden
# reducedDimNames(2): pca umap
# altExpNames(0):

# schard
sce.schard <- AD2SCE(
  anndata.file = "/home/songyabing/gefetch2r/doc/conversion/pbmc3k.h5ad",
  method = "schard", use.raw = TRUE
)
sce.schard
# class: SingleCellExperiment
# dim: 13714 2638
# metadata(0):
# assays(1): X
# rownames(13714): AL627309.1 AP006222.2 ... PNRC2-1 SRSF10-1
# rowData names(8): _index gene_ids ... pct_dropout_by_counts
#   total_counts
# colnames(2638): AAACATACAACCAC-1 AAACATTGAGCTAC-1 ... TTTGCATGAGAGGC-1
#   TTTGCATGCCTCAC-1
# colData names(7): _index n_genes ... pct_counts_mt leiden
# reducedDimNames(2): X_pca X_umap
# altExpNames(0):

---

Conversion between SingleCellExperiment and loom

The conversion is performed with functions implemented in LoomExperiment.

SingleCellExperiment to loom

# remove seger.loom first
SCELoom(
  from = "SingleCellExperiment", to = "loom", sce = seger,
  loom.file = "/home/songyabing/gefetch2r/doc/conversion/seger.loom"
)
# Convert SingleCellExperiment to loom.

---

loom to SingleCellExperiment

seger.loom <- SCELoom(
  from = "loom", to = "SingleCellExperiment",
  loom.file = "/home/songyabing/gefetch2r/doc/conversion/seger.loom"
)
# Convert loom to SingleCellExperiment.
seger.loom
# class: SingleCellExperiment
# dim: 26179 3514
# metadata(0):
# assays(1): counts
# rownames(26179): SGIP1 AZIN2 ... BIVM-ERCC5.1 eGFP
# rowData names(2): refseq symbol
# colnames(3514): HP1502401_N13 HP1502401_D14 ... HP1526901T2D_O11 HP1526901T2D_A8
# colData names(9): age body.mass.index ... single.cell.well.quality
#   submitted.single.cell.quality
# reducedDimNames(0):
# altExpNames(0):

---