Package 'loupeR'

Extract the counts matrix from the Assay

Description

Extract the counts matrix from the Assay

Usage

counts_matrix_from_assay(assay)

Arguments

assay

A SeuratObject::Assay or SeuratObject::Assay5

Value

A sparse counts matrix

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Create a Bugreport from a count matrix, projections, and clusters

Description

This bugreport can then be included when reaching out to 10xGenomics Support or when filing a Github ticket. This information should be included along with any other output when creating a Loupe file.

Usage

create_bugreport(
  count_mat,
  clusters,
  projections,
  assay_name = NULL,
  seurat_obj_version = NULL,
  skip_metadata = FALSE
)

Arguments

count_mat	A sparse dgCMatrix as is generated via Matrix::rsparsematrix. Rows are features, Columns are barcodes.
clusters	list of factors that hold information for each barcode
projections	list of matrices, all with dimensions (barcodeCount x 2)
assay_name	optional string that holds the Seurat Object assay name.
seurat_obj_version	optional string that holds the Seurat Object version. It is useful for debugging compatibility issues.
skip_metadata	optional logical which skips printing metadata

---

Create a Bugreport from a Seurat Object

Description

This bugreport can then be included when reaching out to 10xGenomics Support or when filing a Github ticket. This information should be included along with any other output when creating a Loupe file.

Usage

create_bugreport_from_seurat(obj)

Arguments

obj	A Seurat Object

---

Create a Loupe file

Description

Create a Loupe file

Usage

create_loupe(
  count_mat,
  clusters = list(),
  projections = list(),
  output_dir = NULL,
  output_name = NULL,
  feature_ids = NULL,
  executable_path = NULL,
  force = FALSE,
  seurat_obj_version = NULL
)

Arguments

count_mat	A sparse dgCMatrix as is generated via Matrix::rsparsematrix. Rows are features, Columns are barcodes.
clusters	list of factors that hold information for each barcode
projections	list of matrices, all with dimensions (barcodeCount x 2)
output_dir	optional directory where the Loupe file will be written
output_name	optional name of the Loupe file with the extensions not included.
feature_ids	optional character vector that specifies the feature ids of the count matrix. Typically, these are the ensemble ids.
executable_path	optional path to the louper executable.
force	optional logical as to whether we should overwrite an already existing file
seurat_obj_version	optional string that holds the Seurat Object version. It is useful for debugging compatibility issues.

Value

TRUE on success, FALSE on error

---

Create a Loupe file from a Seurat Object

Description

create_loupe_from_seurat() passes the active counts matrix, reductions, and factors found in meta.data to create a Loupe file.

Usage

create_loupe_from_seurat(
  obj,
  output_dir = NULL,
  output_name = NULL,
  dedup_clusters = FALSE,
  feature_ids = NULL,
  executable_path = NULL,
  force = FALSE
)

Arguments

obj	A Seurat Object
output_dir	optional directory where the Loupe file will be written
output_name	optional name of the Loupe file with the extensions not included.
dedup_clusters	optional logical that will try to deduplicate all clusters that are numerically the same
feature_ids	optional character vector that specifies the feature ids of the count matrix. Typically, these are the ensemble ids.
executable_path	optional path to the louper executable.
force	optional logical as to whether we should overwrite an already existing file

Value

TRUE on success, FALSE on error

---

Read FeatureIds from 10x features.tsv.gz file

Description

Read FeatureIds from 10x features.tsv.gz file

Usage

read_feature_ids_from_tsv(tsv_path)

Arguments

tsv_path

character vector path to the features.tsv.gz file

Value

A character vector of the feature ids

---

Select the "best" assay for conversion

Description

Prioritizes the active assay, then RNA, and then the rest Usable assays must have a non empty count matrix

Usage

select_assay(obj)

Arguments

obj	A Seurat Object

Value

A list with the named Seurat Assay or NULL if not found

---

Select clusters from the assay

Description

Select clusters from the assay

Usage

select_clusters(obj, dedup = FALSE)

Arguments

obj	A Seurat Object
dedup	logical to dedupicate clusters. Default TRUE.

Value

A list of factors

---

Select projections from the assay

Description

Select projections from the assay

Usage

select_projections(obj)

Arguments

obj	A Seurat Object

Value

A list of matrices, all with dimensions (barcodeCount x 2)

---

Setup eula and download executable

Description

Setup eula and download executable

Usage

setup(executable_path = NULL)

Arguments

executable_path

optional string to a non default executable path

---

Validate the format of the barcodes

Description

Validate the format of the barcodes

Usage

validate_barcodes(barcodes)

Arguments

barcodes

a character vector

Value

A list with two elements:

• success: a logical value indicating success (TRUE) or failure (FALSE)

• msg: an optional error message (NULL if success is TRUE)

---

Validate the seurat clusters

Description

Validate the seurat clusters

Usage

validate_clusters(clusters, barcode_count)

Arguments

clusters	list of factors that hold information for each barcode
barcode_count	number of barcodes

Value

A list with two elements:

• success: a logical value indicating success (TRUE) or failure (FALSE)

• msg: an optional error message (NULL if success is TRUE)

---

Validate the seurat count matrix

Description

Validate the seurat count matrix

Usage

validate_count_mat(count_mat, feature_ids = NULL)

Arguments

count_mat	A sparse dgCMatrix as is generated via Matrix::rsparsematrix. Rows are features, Columns are barcodes.
feature_ids	optional character vector that specifies the feature ids of the count matrix. Typically, these are the ensemble ids.

Value

A list with two elements:

• success: a logical value indicating success (TRUE) or failure (FALSE)

• msg: an optional error message (NULL if success is TRUE)

---

Validate the seurat projections

Description

Validate the seurat projections

Usage

validate_projections(projections, barcode_count)

Arguments

projections	list of matrices, all with dimensions (barcodeCount x 2)
barcode_count	number of barcodes

Value

A list with two elements:

• success: a logical value indicating success (TRUE) or failure (FALSE)

• msg: an optional error message (NULL if success is TRUE)

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