| Type: | Package |
| Title: | Group Technical Effects |
| Version: | 1.0.0 |
| Language: | en-US |
| Date: | 2025-02-20 |
| Maintainer: | Yang Zhou <yangz@stu.hit.edu.cn> |
| Description: | Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Depends: | R (≥ 4.0.0) |
| Imports: | stats, Matrix, matrixStats, Rcpp, RcppEigen, dplyr |
| LinkingTo: | Rcpp (≥ 1.0.8), RcppEigen |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | yes |
| URL: | https://github.com/yzhou1999/GTEs, https://yzhou1999.github.io/GTEs/ |
| BugReports: | https://github.com/yzhou1999/GTEs/issues |
| Packaged: | 2025-02-26 07:54:16 UTC; server |
| Author: | Yang Zhou [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2025-02-27 16:50:10 UTC |
Compute the group technical effects.
Description
Compute the group technical effects.
Usage
Run.GroupTechEffects(X, meta, g_factor, b_factor, do.scale = FALSE)
Arguments
X |
Input data matrix. |
meta |
Input metadata (data.frame). |
g_factor |
Group variable (s). |
b_factor |
Batch variable (s). |
do.scale |
Whether to perform scaling. |
Value
A list containing the overall GTE ($OverallTechEffects) and the GTE ($GroupTechEffects) of each subgroup under the group variable.
Examples
# X is a normalized expression matrix with rows as features and columns as cells.
# meta is a data.frame with columns containing metadata such as cell type, batch, etc.
data_file <- system.file("extdata", "example_data.rds", package = "GTEs")
example_data <- readRDS(data_file)
meta_file <- system.file("extdata", "example_meta.rds", package = "GTEs")
example_meta <- readRDS(meta_file)
GTE_ct <- Run.GroupTechEffects(example_data, example_meta,
g_factor = "CellType",
b_factor = "Batch")
Select highly batch-sensitive genes (HBGs) under a group variable.
Description
Select highly batch-sensitive genes (HBGs) under a group variable.
Usage
Select.HBGs(GTE, bins = 0.1, gte.ratio = 0.95)
Arguments
GTE |
GTE result. |
bins |
Bins. |
gte.ratio |
Ratio of selected HBGs to the total GTE. |
Value
Identified HBGs.
Examples
# GTE is the result of Run.GroupTechEffects function.
data_file <- system.file("extdata", "GTE_ct.rds", package = "GTEs")
GTE_ct <- readRDS(data_file)
HBGs <- Select.HBGs(GTE_ct)
Compute one-hot matrix for given data frame and variable (s)
Description
Compute one-hot matrix for given data frame and variable (s)
Usage
group_onehot(x, ivar)
Arguments
x |
Input data frame. |
ivar |
Variable (s) for one-hot computation. |
Scale data matrix
Description
Scale data matrix
Usage
scale_data(
data.x,
do.center = TRUE,
do.scale = TRUE,
row.means = NULL,
row.sds = NULL
)
Arguments
data.x |
Input data matrix. |
do.center |
Whether center the row values. (default TRUE) |
do.scale |
Whether scale the row values. (default TRUE) |
row.means |
The provided row means to center. (default NULL) |
row.sds |
The provided row standard deviations to scale. (default NULL) |
Select HBGs using GTE vector.
Description
Select HBGs using GTE vector.
Usage
select_hbgs(gte, bins = 0.1, gte.ratio = 0.95, is.sort = TRUE)
Arguments
gte |
Named GTE vector. |
bins |
Bins. |
gte.ratio |
Ratio of selected HBGs to overall GTE. |
is.sort |
Whether to sort genes by GTE from largest to smallest. |