Display Summary Information for an anglemania_object

This method provides a concise summary of an anglemania_object, including dataset and batch information, the number of intersected genes, and other relevant details.

Retrieves the list of gene expression matrices stored in the anglemania_object object.

Assigns a new list of gene expression matrices to the anglemania_object.

Retrieves the dataset key used in the anglemania_object.

Retrieves the batch key used in the anglemania_object.

Retrieves the data frame summarizing the selected anglemania gene pairs anglemania_object.

Retrieves the weights assigned to each dataset or batch in the anglemania_object

Assigns new weights to the datasets or batches in the anglemania_object.

Retrieves the list of statistical measures computed across datasets in the anglemania_object.

Assigns a new list of statistical measures to the anglemania_object.

Retrieves the vector of genes that are expressed in at least the specified number of cells across all batches.

Assigns a new vector of intersected genes to the anglemania_object.

Retrieves the list of genes selected for integration from the anglemania_object

This function adds a unique batch identifier to the metadata of a Seurat object by combining specified dataset and batch keys. This is useful for distinguishing samples during integration or analysis.

# S4 method for class 'anglemania_object'
show(object)

matrix_list(object)

# S4 method for class 'anglemania_object'
matrix_list(object)

matrix_list(object) <- value

# S4 method for class 'anglemania_object'
matrix_list(object) <- value

dataset_key(object)

# S4 method for class 'anglemania_object'
dataset_key(object)

batch_key(object)

# S4 method for class 'anglemania_object'
batch_key(object)

data_info(object)

# S4 method for class 'anglemania_object'
data_info(object)

angl_weights(object)

# S4 method for class 'anglemania_object'
angl_weights(object)

angl_weights(object) <- value

# S4 method for class 'anglemania_object'
angl_weights(object) <- value

list_stats(object)

# S4 method for class 'anglemania_object'
list_stats(object)

list_stats(object) <- value

# S4 method for class 'anglemania_object'
list_stats(object) <- value

intersect_genes(object)

# S4 method for class 'anglemania_object'
intersect_genes(object)

intersect_genes(object) <- value

# S4 method for class 'anglemania_object'
intersect_genes(object) <- value

get_anglemania_genes(object)

# S4 method for class 'anglemania_object'
get_anglemania_genes(object)

add_unique_batch_key(
  object_metadata,
  dataset_key = NA_character_,
  batch_key,
  new_unique_batch_key = "batch"
)

Arguments

object: An anglemania_object.
value: A character vector of gene names.
object_metadata: Metadata of a Seurat or SingleCellExperiment object.
dataset_key: A character string specifying the column name in the metadata that identifies the dataset. If NA, only the batch_key is used.
batch_key: A character string specifying the column name in the metadata that identifies the batch.
new_unique_batch_key: A character string for the new unique batch key to be added to the metadata. Default is "batch".

Value

Prints a summary to the console.

A list of FBM objects containing gene expression matrices.

The updated anglemania_object.

A character string representing the dataset key.

A character string representing the batch key.

A data frame containing dataset and batch information.

A named numeric vector of weights.

The updated anglemania_object.

A list containing statistical matrices such as mean z-scores and SNR z-scores

The updated anglemania_object.

A character vector of intersected gene names from multiple Seurat or SingleCellExperiment Objects.

The updated anglemania_object object.

A character vector of integration gene names.

A Seurat object with an additional metadata column containing the unique batch key.

Functions

show(anglemania_object): show anglemania_object info
matrix_list(): Access matrix list
matrix_list(object) <- value: set matrix list in anglemania_object
dataset_key(): Access dataset key of anglemania_object
batch_key(): Access batch key of anglemania_object
data_info(): Access info of selected gene pairs
angl_weights(): Access weights
angl_weights(object) <- value: Set weights
list_stats(): Access statistics of the gene-gene matrices
list_stats(object) <- value: Set statistics of the gene-gene matrices
intersect_genes(): Access the intersection of genes of all batches
intersect_genes(object) <- value: Set the intersection of genes of all batches
get_anglemania_genes(): Access the genes extracted by anglemania
add_unique_batch_key(): Temporarily add a unique batch key to the dataset

Examples

sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
angl <- anglemania(angl)
#> Computing angles and transforming to z-scores...
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
#> Computing statistics...
#> Weighting matrix_list...
#> Calculating mean...
#> Calculating sds...
#> Filtering features...
#> [1] "Selected 36 genes for integration."
show(angl)
#> anglemania_object
#> --------------
#> Dataset key: NA 
#> Batch key: batch 
#> Number of datasets: 1 
#> Total number of batches: 2 
#> Batches (showing first 5):
#> batch1, batch2 
#> Number of intersected genes: 300 
#> Intersected genes (showing first 10):
#> gene1, gene2, gene3, gene4, gene5, gene6, gene7, gene8, gene9, gene10 , ...
#> Min cells per gene: 1 
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
angl <- anglemania(angl)
#> Computing angles and transforming to z-scores...
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
#> Computing statistics...
#> Weighting matrix_list...
#> Calculating mean...
#> Calculating sds...
#> Filtering features...
#> [1] "Selected 36 genes for integration."
str(matrix_list(angl))
#> List of 2
#>  $ batch1:Reference class 'FBM' [package "bigstatsr"] with 15 fields
#>   ..$ extptr      :<externalptr> 
#>   ..$ extptr_rw   :<externalptr> 
#>   ..$ nrow        : int 300
#>   ..$ ncol        : int 300
#>   ..$ type        : Named int 8
#>   .. ..- attr(*, "names")= chr "double"
#>   ..$ backingfile : chr "/tmp/Rtmp6wGGJH/file2002511a2e92.bk"
#>   ..$ is_read_only: logi FALSE
#>   ..$ address     :<externalptr> 
#>   ..$ address_rw  :<externalptr> 
#>   ..$ bk          : chr "/tmp/Rtmp6wGGJH/file2002511a2e92.bk"
#>   ..$ rds         : chr "/tmp/Rtmp6wGGJH/file2002511a2e92.rds"
#>   ..$ is_saved    : logi FALSE
#>   ..$ type_chr    : chr "double"
#>   ..$ type_size   : int 8
#>   ..$ file_size   : num 720000
#>   ..and 22 methods, of which 8 are  possibly relevant:
#>   ..  add_columns, bm, bm.desc, check_dimensions, check_write_permissions,
#>   ..  initialize, save, show#envRefClass
#>  $ batch2:Reference class 'FBM' [package "bigstatsr"] with 15 fields
#>   ..$ extptr      :<externalptr> 
#>   ..$ extptr_rw   :<externalptr> 
#>   ..$ nrow        : int 300
#>   ..$ ncol        : int 300
#>   ..$ type        : Named int 8
#>   .. ..- attr(*, "names")= chr "double"
#>   ..$ backingfile : chr "/tmp/Rtmp6wGGJH/file20027532b8ee.bk"
#>   ..$ is_read_only: logi FALSE
#>   ..$ address     :<externalptr> 
#>   ..$ address_rw  :<externalptr> 
#>   ..$ bk          : chr "/tmp/Rtmp6wGGJH/file20027532b8ee.bk"
#>   ..$ rds         : chr "/tmp/Rtmp6wGGJH/file20027532b8ee.rds"
#>   ..$ is_saved    : logi FALSE
#>   ..$ type_chr    : chr "double"
#>   ..$ type_size   : int 8
#>   ..$ file_size   : num 720000
#>   ..and 22 methods, of which 8 are  possibly relevant:
#>   ..  add_columns, bm, bm.desc, check_dimensions, check_write_permissions,
#>   ..  initialize, save, show#envRefClass
sce <- sce_example()
angl <- create_anglemania_object(
  sce,
  dataset_key = "dataset",
  batch_key = "batch",
  min_cells_per_gene = 1
)
#> Using dataset_key: dataset
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
dataset_key(angl)
#> [1] "dataset"
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
batch_key(angl)
#> [1] "batch"
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
batch_key(angl)
#> [1] "batch"
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
batch_key(angl)
#> [1] "batch"
angl_weights(angl)
#> batch1 batch2 
#>    0.5    0.5 
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
angl <- anglemania(angl)
#> Computing angles and transforming to z-scores...
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
#> Computing statistics...
#> Weighting matrix_list...
#> Calculating mean...
#> Calculating sds...
#> Filtering features...
#> [1] "Selected 36 genes for integration."
stats <- list_stats(angl)
str(stats)
#> List of 3
#>  $ mean_zscore: num [1:300, 1:300] NA -1.474 -0.674 0.792 0.206 ...
#>  $ sds_zscore : num [1:300, 1:300] NA 0.302 0.152 0.356 0.625 ...
#>  $ sn_zscore  : num [1:300, 1:300] NA 4.88 4.444 2.228 0.329 ...

sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
intersect_genes(angl)
#>   [1] "gene1"   "gene2"   "gene3"   "gene4"   "gene5"   "gene6"   "gene7"  
#>   [8] "gene8"   "gene9"   "gene10"  "gene11"  "gene12"  "gene13"  "gene14" 
#>  [15] "gene15"  "gene16"  "gene17"  "gene18"  "gene19"  "gene20"  "gene21" 
#>  [22] "gene22"  "gene23"  "gene24"  "gene25"  "gene26"  "gene27"  "gene28" 
#>  [29] "gene29"  "gene30"  "gene31"  "gene32"  "gene33"  "gene34"  "gene35" 
#>  [36] "gene36"  "gene37"  "gene38"  "gene39"  "gene40"  "gene41"  "gene42" 
#>  [43] "gene43"  "gene44"  "gene45"  "gene46"  "gene47"  "gene48"  "gene49" 
#>  [50] "gene50"  "gene51"  "gene52"  "gene53"  "gene54"  "gene55"  "gene56" 
#>  [57] "gene57"  "gene58"  "gene59"  "gene60"  "gene61"  "gene62"  "gene63" 
#>  [64] "gene64"  "gene65"  "gene66"  "gene67"  "gene68"  "gene69"  "gene70" 
#>  [71] "gene71"  "gene72"  "gene73"  "gene74"  "gene75"  "gene76"  "gene77" 
#>  [78] "gene78"  "gene79"  "gene80"  "gene81"  "gene82"  "gene83"  "gene84" 
#>  [85] "gene85"  "gene86"  "gene87"  "gene88"  "gene89"  "gene90"  "gene91" 
#>  [92] "gene92"  "gene93"  "gene94"  "gene95"  "gene96"  "gene97"  "gene98" 
#>  [99] "gene99"  "gene100" "gene101" "gene102" "gene103" "gene104" "gene105"
#> [106] "gene106" "gene107" "gene108" "gene109" "gene110" "gene111" "gene112"
#> [113] "gene113" "gene114" "gene115" "gene116" "gene117" "gene118" "gene119"
#> [120] "gene120" "gene121" "gene122" "gene123" "gene124" "gene125" "gene126"
#> [127] "gene127" "gene128" "gene129" "gene130" "gene131" "gene132" "gene133"
#> [134] "gene134" "gene135" "gene136" "gene137" "gene138" "gene139" "gene140"
#> [141] "gene141" "gene142" "gene143" "gene144" "gene145" "gene146" "gene147"
#> [148] "gene148" "gene149" "gene150" "gene151" "gene152" "gene153" "gene154"
#> [155] "gene155" "gene156" "gene157" "gene158" "gene159" "gene160" "gene161"
#> [162] "gene162" "gene163" "gene164" "gene165" "gene166" "gene167" "gene168"
#> [169] "gene169" "gene170" "gene171" "gene172" "gene173" "gene174" "gene175"
#> [176] "gene176" "gene177" "gene178" "gene179" "gene180" "gene181" "gene182"
#> [183] "gene183" "gene184" "gene185" "gene186" "gene187" "gene188" "gene189"
#> [190] "gene190" "gene191" "gene192" "gene193" "gene194" "gene195" "gene196"
#> [197] "gene197" "gene198" "gene199" "gene200" "gene201" "gene202" "gene203"
#> [204] "gene204" "gene205" "gene206" "gene207" "gene208" "gene209" "gene210"
#> [211] "gene211" "gene212" "gene213" "gene214" "gene215" "gene216" "gene217"
#> [218] "gene218" "gene219" "gene220" "gene221" "gene222" "gene223" "gene224"
#> [225] "gene225" "gene226" "gene227" "gene228" "gene229" "gene230" "gene231"
#> [232] "gene232" "gene233" "gene234" "gene235" "gene236" "gene237" "gene238"
#> [239] "gene239" "gene240" "gene241" "gene242" "gene243" "gene244" "gene245"
#> [246] "gene246" "gene247" "gene248" "gene249" "gene250" "gene251" "gene252"
#> [253] "gene253" "gene254" "gene255" "gene256" "gene257" "gene258" "gene259"
#> [260] "gene260" "gene261" "gene262" "gene263" "gene264" "gene265" "gene266"
#> [267] "gene267" "gene268" "gene269" "gene270" "gene271" "gene272" "gene273"
#> [274] "gene274" "gene275" "gene276" "gene277" "gene278" "gene279" "gene280"
#> [281] "gene281" "gene282" "gene283" "gene284" "gene285" "gene286" "gene287"
#> [288] "gene288" "gene289" "gene290" "gene291" "gene292" "gene293" "gene294"
#> [295] "gene295" "gene296" "gene297" "gene298" "gene299" "gene300"
sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
angl <- anglemania(angl)
#> Computing angles and transforming to z-scores...
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
#> Computing statistics...
#> Weighting matrix_list...
#> Calculating mean...
#> Calculating sds...
#> Filtering features...
#> [1] "Selected 36 genes for integration."
# extract the genes identified by anglemania()
anglemania_genes <- get_anglemania_genes(angl)

sce <- sce_example()
angl <- create_anglemania_object(sce, batch_key = "batch")
#> No dataset_key specified.
#> Assuming that all samples belong to the same dataset and are separated by batch_key: batch
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |==================================================| 100% elapsed=00s, remaining~00s
angl <- create_anglemania_object(
  sce,
  dataset_key = "dataset",
  batch_key = "batch",
  min_cells_per_gene = 1
)
#> Using dataset_key: dataset
#> Extracting count matrices...
#> Filtering each batch to at least 1 cells per gene...
#> Using the intersection of filtered genes from all batches...
#> Number of genes in intersected set: 300
#> 
  |                                                  | 0 % elapsed=00s   
  |=========================                         | 50% elapsed=00s, remaining~00s
  |==================================================| 100% elapsed=00s, remaining~00s
head(SingleCellExperiment::colData(sce))
#> DataFrame with 6 rows and 2 columns
#>             batch     dataset
#>       <character> <character>
#> cell1      batch1    dataset1
#> cell2      batch1    dataset1
#> cell3      batch1    dataset1
#> cell4      batch1    dataset1
#> cell5      batch1    dataset1
#> cell6      batch1    dataset1

Arguments

Value

Functions

See also

Examples