Merged objects

Each merged object contains combined information for all individual samples in the given ScPCA project. While each individual object, as described on the Single-cell gene expression file contents page, contains quantified gene expression results for a single library, each merged object contains all gene expression results, including gene expression counts and metadata, for all libraries and samples in the given ScPCA project. This information includes quantified gene expression data, cell and gene metrics, and associated metadata for all libraries. See the section on merged object processing for more information on how these objects were prepared.

Merged objects are provided in two formats:

• As an RDS file containing a SingleCellExperiment object for use in R.

• As an H5AD file containing an AnnData object for use in Python.

Below we present some details about the specific contents of the objects we provide.

Components of a SingleCellExperiment merged object

To begin, you will need to load the SingleCellExperiment package and read the RDS file:

library(SingleCellExperiment)
merged_sce <- readRDS("SCPCP000000_merged.rds")

SingleCellExperiment expression counts

Merged SingleCellExperiment objects contain two main assays, counts and logcounts, each containing RNA-seq expression data for all libraries in the given ScPCA project combined into a single matrix. The counts assay contains the primary raw counts represented as integers, and the logcounts assay contains normalized counts as described in the data post-processing section.

The counts assay includes reads aligned to both spliced and unspliced cDNA (see the section on Post Alevin-fry processing). Each assay is stored as a sparse matrix, where each column represents a cell or droplet, and each row represents a gene. The counts and logcounts assays can be accessed with the following R code:

counts(merged_sce) # combined counts matrix
logcounts(merged_sce) # combined logcounts matrix

Column names are cell barcode sequences prefixed with the originating library ID, e.g. SCPCL000000-{barcode}, and row names are Ensembl gene IDs. These names can be accessed with the following R code:

colnames(merged_sce) # matrix column names
rownames(merged_sce) # matrix row names

There is also a spliced assay which contains the counts matrix with only reads from spliced cDNA.

SingleCellExperiment cell metrics

Cell metrics calculated from the RNA-seq expression data are stored as a DataFrame in the colData slot, where row names are the cell barcode prefixed with the originating library ID, e.g. SCPCL000000-{barcode}. This DataFrame also contains additional sample metadata information stored in the colData slot for all projects that do not contain multiplexed libraries. Read more about the included sample metadata in the Sample metadata section,

colData(merged_sce) # cell metrics

The following per-cell data columns are included for each cell. Columns representing quality control statistics were calculated using the scuttle::addPerCellQCMetrics() function.

Column name	Contents
barcodes	The cell barcode
library_id	Library ID in the form SCPCL000000
cell_id	Unique ID for each cell in the format SCPCL000000-{barcode}. This value matches the colData row names
sum	UMI count for RNA-seq data
detected	Number of genes detected (gene count > 0 )
subsets_mito_sum	UMI count of mitochondrial genes
subsets_mito_detected	Number of mitochondrial genes detected
subsets_mito_percent	Percent of all UMI counts assigned to mitochondrial genes
total	Total UMI count for RNA-seq data and any alternative experiments (i.e., ADT data from CITE-seq)
prob_compromised	Probability that a cell is compromised (i.e., dead or damaged), as calculated by miQC
miQC_pass	Indicates whether the cell passed the default miQC filtering. TRUE is assigned to cells with a low probability of being compromised (prob_compromised < 0.75) or sufficiently low mitochondrial content
scpca_filter	Labels cells as either Keep or Remove based on filtering criteria (prob_compromised < 0.75 and number of unique genes detected > 200)
adt_scpca_filter	If CITE-seq was performed, labels cells as either Keep or Remove based on ADT filtering criteria (discard = TRUE as determined by DropletUtils::CleanTagCounts())
scDblFinder_class	The scDblFinder predicted classification, either “singlet” or “doublet”
scDblFinder_score	The scDblFinder doublet score, representing the probability that the cell is a doublet
submitter_celltype_annotation	If available, cell type annotations obtained from the group that submitted the original data. Cells that the submitter did not annotate are labeled as "Submitter-excluded"
openscpca_celltype_annotation	If available, cell type annotations obtained from the OpenScPCA project as determined by analysis performed in the OpenScPCA-analysis GitHub repository. Cells that were not annotated as part of OpenScPCA are labeled as "openscpca-excluded"
openscpca_celltype_ontology	If available, the Cell Ontology identifier associated with the openscpca_celltype_annotation. NA will be used if there is no appropriate cell ontology term

Unlike for individual SCE objects, cluster assignments are not included in the colData. Further, if cell type annotation was performed on at least one library included in the merged object, there will be additional colData columns with these annotation results, as described in the cell type annotation processing section.

Column name	Contents
singler_celltype_annotation	If cell typing with SingleR was performed, the annotated cell type. Cells labeled as NA are those which SingleR could not confidently annotate. If cell typing was performed for some but not all libraries in the merged object, libraries without annotations will be labeled "Cell type annotation not performed"
singler_celltype_ontology	If cell typing with SingleR was performed with ontology labels, the annotated cell type’s ontology ID. Cells labeled as NA are those which SingleR could not confidently annotate. If cell typing was performed for some but not all libraries in the merged object, libraries without annotations will be labeled "Cell type annotation not performed"
cellassign_celltype_annotation	If cell typing with CellAssign was performed, the annotated cell type. Cells labeled as "other" are those which CellAssign could not confidently annotate. If cell typing was performed for some but not all libraries in the merged object, libraries without annotations will be labeled "Cell type annotation not performed". If CellAssign was unable to complete successfully for a given library, cells will be labeled as "Not run"
cellassign_celltype_ontology	If cell typing with CellAssign was performed, the annotated cell type ontology ID. Cells labeled as NA are those which CellAssign could not confidently annotate. If cell typing was performed for some but not all libraries in the merged object, libraries without annotations will be labeled "Cell type annotation not performed"
cellassign_max_prediction	If cell typing with CellAssign was performed and completed successfully, the annotation’s prediction score (probability)
scimilarity_celltype_annotation	If cell typing with SCimilarity was performed, the annotated cell type. Cells labeled as "Unclassified cell" did not undergo cell type annotation with SCimilarity
scimilarity_celltype_ontology	If cell typing with SCimilarity was performed, the annotated cell type ontology ID.
scimilarity_min_distance	If cell typing with SCimilarity was performed and completed successfully, the minimum distance of the cell to all cells in the training set
consensus_celltype_annotation	The assigned consensus cell type annotation as determined by finding the latest common ancestor among SingleR, CellAssign, and SCimilarity cell type annotations. Cells labeled as "Unknown" do not have an appropriate consensus cell type label that could be assigned. If the consensus cell type was assigned for some but not all libraries in the merged object, libraries without consensus annotations will be labeled "No consensus cell type assigned"
consensus_celltype_ontology	The assigned consensus cell type ontology ID as determined by finding the latest common ancestor among SingleR, CellAssign, and SCimilarity cell type annotations. Cells labeled as NA do not have an appropriate consensus cell type label that could be assigned. If the consensus cell type was assigned for some but not all libraries in the merged object, libraries without consensus annotations will be labeled "No consensus cell type assigned"
is_infercnv_reference	Whether the cell would be considered part of the reference normal cells for inferCNV inference. Only present when consensus cell types were assigned and CNV inference was attempted. This will be NA for any samples obtained from normal or non-cancerous tissue
infercnv_total_cnv	If CNV inference was performed, the total number of CNV events in the cell calculated by inferCNV. Cells labeled NA were either filtered out during inferCNV calculations, as described in the CNV inference processing section, or may be the result of inferCNV result caching. This column is only present if inferCNV was successfully run

SingleCellExperiment gene information and metrics

Gene information and metrics calculated from the RNA-seq expression data are stored as a DataFrame in the rowData slot, with the Ensembl ID as the names of the rows.

rowData(merged_sce) # gene metrics

The following columns are included for all genes. The columns mean and detected will appear for each library ID included in the merged object, named as shown in the table below. However, there will only be a single gene_symbol and gene_ids column, as this information equally pertains to all libraries. Metrics were calculated for each library using the scuttle::addPerFeatureQCMetrics function.

Column name	Contents
gene_ids	Ensembl gene ID
gene_symbol	HUGO gene symbol, if defined
SCPCL000000-mean	Mean count across all cells/droplets for library SCPCL000000
SCPCL000000-detected	Percent of cells in which the gene was detected (gene count > 0 ) for library SCPCL000000

SingleCellExperiment experiment metadata

metadata(merged_sce) # experiment metadata

Item name	Contents
library_id	A vector of library IDs which are included in the merged object, each in the form SCPCL000000
sample_id	A vector of sample IDs which are included in the merged object, each in the form SCPCS000000
library_metadata	A list of the library metadata for each library. Each list is named with the appropriate library ID and contains the library metadata fields for the given library as they would appear in an individual library object. See the table below for a full description of its contents
sample_metadata	A data frame of additional sample metadata, as described in the Sample metadata section below. Only present for projects with multiplexed libraries
merged_highly_variable_genes	A vector of highly variable genes used for performing dimensionality reduction on the merged object, determined using scran::modelGeneVar, specifying each library as a separate block, and scran::getTopHVGs

To access the library_metadata field for a specific library, use the following code:

# Access individual library metadata for SCPCL000000
metadata(merged_sce)$library_metadata$SCPCL000000

Each such list will contain the following fields:

Item name	Contents
sample_id	Sample ID in the form SCPCS000000
library_id	Library ID in the form SCPCL000000
project_id	Project ID in the form SCPCP000000
salmon_version	Version of salmon used for initial mapping
reference_index	Transcriptome reference file used for mapping
total_reads	Total number of reads processed by salmon
mapped_reads	Number of reads successfully mapped
mapping_tool	Pipeline used for mapping and quantification (alevin-fry for all current data in ScPCA)
alevinfry_version	Version of alevin-fry used for mapping and quantification
af_permit_type	alevin-fry generate-permit-list method used for filtering cell barcodes
af_resolution	alevin-fry quant resolution mode used
usa_mode	Boolean indicating whether quantification was done using alevin-fry USA mode
af_num_cells	Number of cells reported by alevin-fry
tech_version	A string indicating the technology and version used for the single-cell library, such as 10Xv2, 10Xv3, or 10Xv3.1
assay_ontology_term_id	A string indicating the Experimental Factor Ontology term ID associated with the tech_version
seq_unit	cell for single-cell samples or nucleus for single-nucleus samples
transcript_type	Transcripts included in gene counts: spliced for single-cell samples and unspliced for single-nuclei
sample_type	A string indicating the type of sample, with one of the following values: "patient-derived xenograft", "cell line", or "patient tissue". If the library is multiplexed, this will be a named vector giving the sample type for each sample ID in the library. A value of "Not provided" indicates that this information is not available
filtering_method	The method used for cell filtering. One of emptyDrops, emptyDropsCellRanger, or UMI cutoff
umi_cutoff	The minimum UMI count per cell used as a threshold for removing empty droplets. Only present for objects where the filtering_method is UMI cutoff
prob_compromised_cutoff	The minimum cutoff for the probability of a cell being compromised, as calculated by miQC
scpca_filter_method	Method used by the Data Lab to filter low quality cells prior to normalization. Either miQC or Minimum_gene_cutoff
adt_scpca_filter_method	If CITE-seq was performed, the method used by the Data Lab to identify cells to be filtered prior to normalization, based on ADT counts. Either cleanTagCounts with isotype controls or cleanTagCounts without isotype controls. If filtering failed (i.e. DropletUtils::cleanTagCounts() could not reliably determine which cells to filter), the value will be No filter
min_gene_cutoff	The minimum cutoff for the number of unique genes detected per cell
normalization	The method used for normalization of raw RNA counts. Either deconvolution or log-normalization, as explained in the processed gene expression data section. Only present for processed objects
adt_normalization	If CITE-seq was performed, the method used for normalization of raw ADT counts. Either median-based or log-normalization, as explained in the processed ADT data section
highly_variable_genes	A list of highly variable genes used for dimensionality reduction, determined using scran::modelGeneVar and scran::getTopHVGs
celltype_methods	If cell type annotation was performed, a vector of the methods used for annotation. May include "submitter", "openscpca", "singler" and/or "cellassign"
openscpca_celltype_module_name	If cell type annotations from the OpenScPCA project are available, the original module name from the OpenScPCA-analysis GitHub repository
openscpca_celltype_nf_version	If cell type annotations from the OpenScPCA project are available, the version of the OpenScPCA-nf workflow used to generate annotations
openscpca_celltype_release_date	If cell type annotations from the OpenScPCA project are available, the release date for the input ScPCA data used when assigning annotations
singler_results	If cell typing with SingleR was performed, the full result object returned by SingleR annotation
singler_reference	If cell typing with SingleR was performed, the name of the reference dataset used for annotation
singler_reference_label	If cell typing with SingleR was performed, the name of the label in the reference dataset used for annotation
singler_reference_source	If cell typing with SingleR was performed, the source of the reference dataset (default is celldex)
singler_reference_version	If cell typing with SingleR was performed, the version of celldex used to create the reference dataset source, with periods replaced as dashes (-)
cellassign_predictions	If cell typing with CellAssign was performed and completed successfully, the full matrix of predictions across cells and cell types
cellassign_reference	If cell typing with CellAssign was performed and completed successfully, the reference name as established by the Data Lab used for cell type annotation
cellassign_reference_organs	If cell typing with CellAssign was performed and completed successfully, a comma-separated list of organs and/or tissue compartments from which marker genes were obtained to create the reference
cellassign_reference_source	If cell typing with CellAssign was performed and completed successfully, the source of the reference dataset (default is PanglaoDB)
cellassign_reference_version	If cell typing with CellAssign was performed and completed successfully, the version of the reference dataset source. For references obtained from PanglaoDB, the version scheme is a date in ISO8601 format
scimilarity_model	If cell typing with SCimilarity was performed, the name of the foundation model used to run SCimilarity. Specifically, the URI on S3 to the model file used is provided
consensus_celltype_methods	If consensus cell types are present, a vector with the names of the automated methods used to generate consensus cell type annotations
infercnv_reference_celltypes	Vector of consensus cell type labels which would be specified for the normal reference cells in inferCNV reference. Only present when consensus cell types were assigned and CNV inference was attempted. If CNV inference was attempted and the sample was obtained from normal or non-cancerous tissue this will not be present
infercnv_num_reference_cells	The total number of normal reference cells included in the inferCNV reference. Only present when consensus cell types were assigned and CNV inference was attempted. If CNV inference was attempted and the sample was obtained from normal or non-cancerous tissue this will be NA
infercnv_diagnosis_groups	The broad diagnosis group used to determine which cell types should be considered as part of the normal cell reference for inferCNV. If this value is Non-cancerous, inferCNV was not run
infercnv_status	String indicating the status of running inferCNV. A value of “success” indicates that inferCNV successfully ran, “failure” indicates that inferCNV was attempted but failed during inference for an unknown reason, “insufficient_reference_cells” indicates that inferCNV was not run due to insufficient normal reference cells, and “skipped_non_cancerous” indicates that inferCNV was not run because the sample was non-cancerous. Other string values may be present if different errors running inferCNV were encountered, the specific details for which are provided in the QC report. Only present when CNV inference was attempted
infercnv_options	A list containing the contents of the objects slot from the final inferCNV results object containing all parameters specified used when running inferCNV. Only present when CNV inference was performed
infercnv_table	The inferCNV metadata table with detailed CNV results output from inferCNV. This data frame corresponds to the map_metadata_from_infercnv.txt table calculated from the HMM results. Only present when CNV inference was performed

Unlike for individual SingleCellExperiment objects, cluster algorithm parameters are not included in these metadata lists because clusters themselves are not included in the merged object.

SingleCellExperiment sample metadata

Sample metadata describing each sample included in the merged object is stored in one of two locations, depending on the sample type:

If the project does_not_ contain multiplexed libraries, this information can be found as additional columns in the colData slot’s DataFrame, along with cell metrics.

colData(merged_sce) # sample metadata only for projects without multiplexing

If the project contains multiplexed libraries, this information is stored in the metadata slot in the sample_metadata field as a data.frame. Similar to merged objects with multiplexed libraries, all individual library objects will contain this sample metadata in the SingleCellExperiment object’s metadata slot.

metadata(merged_sce)$sample_metadata # sample metadata only for projects with multiplexed samples

Column name	Contents
sample_id	Sample ID in the form SCPCS000000
scpca_project_id	Project ID in the form SCPCP000000
tech_version	A string indicating the technology and version used for the sample’s single-cell library, such as 10Xv2, 10Xv3, or 10Xv3.1
assay_ontology_term_id	A string indicating the Experimental Factor Ontology term ID associated with the tech_version
suspension_type	cell for single-cell samples or nucleus for single-nucleus samples
additional_modalities	Any additional modalities associated with the library, represented as alternative experiment names such as "adt" or "cellhash". If there are no additional modalities, this value will be NA. If the library has multiple additional modalities, this will be a single string with modalities separated by a semicolon, e.g. "adt;cellhash"
participant_id	Unique ID corresponding to the donor from which the sample was obtained
submitter_id	Original sample identifier from submitter
submitter	Submitter name/ID
age	Age provided by submitter
age_timing	Whether age is the age at diagnosis (diagnosis), age at collection (collection), or unknown. This will be diagnosis for all samples collected at diagnosis, indicated by the disease_timing column
sex	Sex of patient that the sample was obtained from
diagnosis	Tumor type
subdiagnosis	Subcategory of diagnosis or mutation status (if applicable)
tissue_location	Where in the body the tumor sample was located
disease_timing	At what stage of disease the sample was obtained, either diagnosis or recurrence
organism	The organism the sample was obtained from (e.g., Homo_sapiens)
is_xenograft	Whether the sample is a patient-derived xenograft
is_cell_line	Whether the sample was derived from a cell line
development_stage_ontology_term_id	HsapDv ontology term indicating developmental stage. If unavailable, unknown is used
sex_ontology_term_id	PATO term referring to the sex of the sample. If unavailable, unknown is used
organism_ontology_id	NCBI taxonomy term for organism, e.g. NCBITaxon:9606
self_reported_ethnicity_ontology_term_id	For Homo sapiens, a Hancestro term. multiethnic indicates more than one ethnicity is reported. unknown indicates unavailable ethnicity and NA is used for all other organisms
disease_ontology_term_id	MONDO term indicating disease type. PATO:0000461 indicates normal or healthy tissue. If unavailable, NA is used
tissue_ontology_term_id	UBERON term indicating tissue of origin. If unavailable, NA is used

Some merged objects may have some additional sample metadata columns specific to the given ScPCA project’s disease type and experimental design. Examples of this include treatment or outcome.

SingleCellExperiment dimensionality reduction results

The reducedDim slot of the merged object will contain both principal component analysis (PCA) and UMAP results.

PCA results were calculated using batchelor::multiBatchPCA, specifying libraries as batches to ensure that each library in the merged object was equally weighted, and specifying a list of highly variable genes. The highly variable genes were selected in a library-aware manner with scran::modelGeneVar and scran::getTopHVGs. The vector of highly variable genes are stored in the SingleCellExperiment object in metadata(merged_sce)$merged_highly_variable_genes. The following command can be used to access the PCA results:

reducedDim(merged_sce, "PCA")

UMAP results were calculated using scater::runUMAP(), with the batch-aware PCA results as input rather than the full gene expression matrix. The following command can be used to access the UMAP results:

reducedDim(merged_sce, "UMAP")

Additional SingleCellExperiment components for CITE-seq libraries (with ADT tags)

ADT data from CITE-seq experiments, when present, is included within the merged SingleCellExperiment object as an “Alternative Experiment” named "adt", which can be accessed with the following command:

altExp(merged_sce, "adt") # adt experiment

Within this, the primary expression matrix is again found in the counts assay, and the normalized expression matrix is found in the logcounts assay. Each expression matrix contains the ADT expression data from the CITE-seq experiment for all libraries in the given ScPCA project combined into a single matrix. For each assay, each column corresponds to a cell or droplet (in the same order as the parent SingleCellExperiment) and each row corresponds to an antibody derived tag (ADT). Column names are again cell barcode sequences prefixed with the originating library ID, e.g. SCPCL000000-{barcode}, and row names are the antibody targets for each ADT. In some cases, only some libraries in the merged object will have associated CITE-seq data. Libraries which do not have CITE-seq expression data are included in the assay matrices with all NA count values.

Only cells which are denoted as “Keep” in the colData(merged_sce)$adt_scpca_filter column (as described above) have normalized expression values in the logcounts assay, and all other cells are assigned NA values. However, as described in the ADT data processing section, normalization may fail under certain circumstances. If a given ADT library failed normalization, it will also have NA values in the merged object.

The following additional per-cell data columns for the ADT data can be found in the main colData data frame (accessed with colData(merged_sce) as above).

Column name	Contents
altexps_adt_sum	UMI count for CITE-seq ADTs
altexps_adt_detected	Number of ADTs detected per cell (ADT count > 0 )
altexps_adt_percent	Percent of total UMI count from ADT reads

In addition, the following QC statistics from DropletUtils::cleanTagCounts(), which were calculated on individual libraries before objects were merged, can be found in the colData of the "adt" alternative experiment, accessed with colData(altExp(merged_sce, "adt")). As in the main experiment’s colData slot, the "adt" alternative experiment colData also contains the columns library_id and cell_id.

Column name	Contents
zero.ambient	Indicates whether the cell has zero ambient contamination
high.controls	Indicates whether the cell has unusually high total control counts. Only present if negative/isotype control ADTs were used
sum.controls	The sum of counts for all control features. Only present if negative/isotype control ADTs were used
high.ambient	Indicates whether the cell has unusually high contamination. Only present if negative/isotype control ADTs were not used
ambient.scale	The relative amount of ambient contamination. Only present if negative/isotype control ADTs were not used
discard	Indicates whether the cell should be discarded based on ADT QC statistics. The TRUE and FALSE values in this column correspond, respectively, to values "Discard" and "Keep" in the colData(merged_sce)$adt_scpca_filter column

Metrics for each of the ADTs assayed can be found as a DataFrame stored as rowData within the alternative experiment:

rowData(altExp(merged_sce, "adt")) # adt metrics

This data frame contains the following columns with statistics for each ADT. The columns SCPCL000000-mean and SCPCL000000-detected are present for each library in the merged object that has associated CITE-seq data.

Column name	Contents
adt_id	Name or ID of the ADT
target_type	Whether each ADT is a target (target), negative/isotype control (neg_control), or positive control (pos_control). If this information was not provided, all ADTs will have been considered targets and will be labeled as target
SCPCL000000-mean	Mean ADT count across all cells/droplets. Only present for libraries with CITE-seq data
SCPCL000000-detected	Percent of cells in which the ADT was detected (ADT count > 0 ). Only present for libraries with CITE-seq data

Finally, additional metadata for ADT processing can be found in the metadata slot of the alternative experiment.

Metadata associated with data processing is included in the metadata slot as a list. Similar to the parent experiment metadata, the metadata will contain three items: library_id, sample_id, and library_metadata. The library_metadata contains the same list of metadata as its corresponding parent experiment library’s metadata and includes one additional field ambient_profile, which holds a named list of the ambient concentrations of each ADT in the given library.

metadata(altExp(merged_sce, "adt")) # adt metadata

Additional SingleCellExperiment components for multiplexed libraries

Merged objects are not available for any projects that contain multiplexed libraries. This is because there is no guarantee that a unique HTO was used for each sample in a given project, so it would not necessarily be possible to determine which HTO corresponds to which sample in a merged object.

Components of an AnnData merged object

Before getting started, we highly encourage you to familiarize yourself with the general AnnData object structure and functions available as part of the AnnData package. For the most part, the AnnData objects that we provide are formatted to match the expected data format for CELLxGENE following schema version 3.0.0.

To begin, you will need to load the AnnData package and read the H5AD file:

import anndata
merged_adata_object = anndata.read_h5ad("SCPCP000000_merged_rna.h5ad")

AnnData expression counts

Merged AnnData objects contain two data matrices, each containing RNA-seq expression data for all libraries in the given ScPCA project combined into a single matrix. The data matrix raw.X of the merged AnnData object contains the RNA-seq expression data as primary integer counts, and the data matrix X contains the RNA-seq expression data as normalized counts. The data is stored as a sparse matrix, where each column represents a cell or droplet, and each row represents a gene. The raw.X and X matrices can be accessed with the following python code:

merged_adata_object.raw.X # raw count matrix
merged_adata_object.X # normalized count matrix

Column names are cell barcode sequences prefixed with the originating library ID, e.g. SCPCL000000-{barcode}, and row names are Ensembl gene IDs. These names can be accessed as with the following python code:

merged_adata_object.obs_names # matrix column names
merged_adata_object.var_names # matrix row names

AnnData cell metrics

Cell metrics calculated from the RNA-seq expression data, which were calculated separately for each library, are stored as a pandas.DataFrame in the .obs slot. The slot’s row names are cell barcode sequences prefixed with the originating library ID, e.g. SCPCL000000-{barcode}.

merged_adata_object.obs # cell metrics and metadata

All of the per-cell data columns included in the colData of the SingleCellExperiment merged objects are present in the .obs slot of the AnnData object. To see a full description of the included columns, see the sections on cell metrics and sample metadata in Components of a SingleCellExperiment merged object.

AnnData gene information and metrics

Gene information and metrics from the RNA-seq expression data, which were calculated separately for each library, are stored as a pandas.DataFrame in the .var slot, with the Ensembl ID as the names of the rows.

merged_adata_object.var # gene metrics

All of the per-gene data columns included in the rowData of the SingleCellExperiment objects are present in the .var slot of the AnnData object. Note that the SingleCellExperiment columns named SCPCL000000-mean and SCPCL000000-detected are instead named SCPCL000000.mean and SCPCL000000.detected, respectively, in the merged AnnData object. To see a full description of the included columns, see the section on gene metrics in Components of a SingleCellExperiment merged object.

AnnData experiment metadata

A partial set of the metadata associated with data processing is included in the .uns slot of the AnnData object as a list.

merged_adata_object.uns # experiment metadata

The following items are available in the .uns slot:

Item name	Contents
library_id	A list of library IDs which are included in the merged object, each in the form SCPCL000000
sample_id	A list of sample IDs which are included in the merged object, each in the form SCPCS000000
merged_highly_variable_genes	A list of highly variable genes used for performing dimensionality reduction on the merged object, determined using scran::modelGeneVar, specifying each library as a separate block, and scran::getTopHVGs

Additional experiment metadata is available in the metadata TSV file included in the ScPCA Portal download folder.

AnnData dimensionality reduction results

The merged AnnData object contains a slot .obsm with both principal component analysis (X_pca) and UMAP (X_umap) results.

For information on how PCA and UMAP results were calculated see the section on processed gene expression data.

The following command can be used to access the PCA and UMAP results:

merged_adata_object.obsm["X_pca"] # pca results
merged_adata_object.obsm["X_umap"] # umap results

Additional AnnData components for CITE-seq libraries (with ADT tags)

ADT data from CITE-seq experiments, when present, is available as a separate AnnData object in an H5AD file named with the _adt.h5ad suffix.

Merged AnnData objects contain two data matrices, each containing CITE-seq expression data for all libraries in a given ScPCA project combined into a single matrix. The data matrix raw.X of the merged AnnData object contains the CITE-seq expression data as primary integer counts, and the data matrix X contains the RNA-seq expression data as normalized counts. Note that only cells which are denoted as "Keep" in the merged_adata_object.uns["adt_scpca_filter"] column (as described above) have normalized expression values in the X matrix, and all other cells are assigned NA values. The data is stored as a sparse matrix, where each column represents a cell or droplet, and each row represents a single ADT. The raw.X and X matrices can be accessed with the following python code:

merged_citeseq_adata_object.raw.X # raw count matrix
merged_citeseq_adata_object.X # normalized count matrix

Column names are cell barcode sequences prefixed with the originating library ID, e.g. SCPCL000000-{barcode}, and row names are the ADT IDs.

merged_citeseq_adata_object.obs_names # matrix column names
merged_citeseq_adata_object.var_names # matrix row names

All of the per-cell data columns included in the colData of the "adt" alternative experiment in SingleCellExperiment merged objects are present in the .obs slot of the CITE-seq AnnData object. To see a full description of the included columns, see the section on additional SingleCellExperiment components for CITE-seq libraries.

In addition, all of the per-ADT data columns included in the rowData of the "adt" alternative experiment in SingleCellExperiment merged objects are present in the .var slot of the CITE-seq AnnData object. To see a full description of the included columns, see the section on additional SingleCellExperiment components for CITE-seq libraries.

Finally, the .uns slot of the CITE-seq AnnData object contains a limited set of experiment metadata information, including a list of library IDs (library_id) and sample IDs (sample_id) included in the merged object.