| Type: | Package |
| Title: | Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets |
| Version: | 0.99.6 |
| Maintainer: | Yue Lyu <yuelyu0521@gmail.com> |
| Description: | The accumulation of single-cell RNA-seq ('scRNA-seq') studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. 'SCIntRuler', a novel R package, addresses these challenges by guiding the integration of multiple 'scRNA-seq' datasets. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.0 |
| Imports: | Rcpp, Matrix, batchelor, base, Seurat, SeuratObject, MatrixGenerics, SingleCellExperiment, SummarizedExperiment, dplyr, coin, harmony, ggplot2, gridExtra, cowplot, magrittr, stats |
| LinkingTo: | Rcpp |
| URL: | https://github.com/yuelyu21/SCIntRuler, https://yuelyu21.github.io/SCIntRuler/ |
| BugReports: | https://github.com/yuelyu21/SCIntRuler/issues |
| Suggests: | BiocStyle, knitr, rmarkdown, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 4.3.0) |
| LazyData: | true |
| LazyDataCompression: | xz |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2024-07-11 06:55:10 UTC; a98455 |
| Author: | Yue Lyu  [aut,
cre] |
| Repository: | CRAN |
| Date/Publication: | 2024-07-12 15:20:08 UTC |
SCIntRuler: Integration of Single-Cell RNA-seq Datasets
Description
The SCIntRuler package addresses the challenges of integrating multiple single-cell RNA-seq (scRNA-seq) datasets. It provides tools to enhance analytical robustness by augmenting sample sizes and reducing batch discrepancies. Developed using the Seurat framework, SCIntRuler includes both existing and novel workflows for single-cell analysis.
Value
This is the main page for SCIntRuler package.
Why SCIntRuler? Integrating scRNA-seq datasets can be complex due to various factors such as batch effects and sample diversity. SCIntRuler provides a statistical metric to aid in crucial decisions regarding dataset integration, ensuring more robust and accurate analyses.
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Features
Informed Decision Making: Helps researchers decide on the necessity of data integration and the most suitable method.
Flexibility: Suitable for various scenarios, accommodating different levels of data heterogeneity.
Robustness: Enhances analytical robustness in joint analyses of merged or integrated scRNA-seq datasets.
User-Friendly: Streamlines decision-making processes, simplifying the complexities involved in scRNA-seq data integration.
Getting Started
Refer to the "Getting Started with SCIntRuler" article in the package vignettes for detailed user instructions.
Author(s)
Yue Lyu
Calculate SCIntRuler
Description
Calculate SCIntRuler
Usage
CalcuSCIR(fullcluster, seuratlist, testres, p = 0.1)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster() |
seuratlist |
A list of Seurat objects, usually can be got by SplitObject(). |
testres |
Result from function PermTest() |
p |
P-value that will be used as the cut-off, default value is 0.1 |
Value
SCIntRuler
Examples
data(sim_result)
data(sim_data_sce)
sim_data <- SCEtoSeurat(sim_data_sce)
seuratlist <- Seurat::SplitObject(sim_data, split.by = "Study")
CalcuSCIR(sim_result[[1]], seuratlist, sim_result[[4]])
Find cells indicating shared biological features across conditions
Description
Find cells indicating shared biological features across conditions
Usage
FindCell(seuratobj, seuratlist, fullcluster, distmat, firstn = 15)
Arguments
seuratobj |
The Seurat object that all samples/subjects were merged together. |
seuratlist |
A list of Seurat objects, usually can be got by SplitObject(). |
fullcluster |
A list of clusters that generated by the function GetCluster(). |
distmat |
A list of distance vectors generated by the function FindNNDist(). |
firstn |
The number of nearest cells were detected that you want to include in the permutation test. Default to be 15. |
Value
A list of two vectors: one is for which cluster of which sample will be highlighted and the second one is which cells will be selected.
Examples
data(sim_data_sce)
data(sim_result)
sim_data <- SCEtoSeurat(sim_data_sce)
seuratlist <- Seurat::SplitObject(sim_data, split.by = "Study")
FindCell(sim_data, seuratlist, sim_result[[1]], sim_result[[3]], 15)
Find the nearest neighbors
Description
Find the nearest neighbors
Usage
FindNNDist(fullcluster, normCount, meaningn = 20)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster(). |
normCount |
A list of normalized gene count matrix generated by the function NormData(). |
meaningn |
default to be 20 |
Value
A list of distance vectors
Examples
data(sim_result)
meaningn <- 20
FindNNDist(sim_result[[1]], sim_result[[2]], meaningn = meaningn)
Find the nearest neighbors
Description
Find the nearest neighbors
Usage
FindNNDistC(fullcluster, normCount, meaningn = 20)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster(). |
normCount |
A list of normalized gene count matrix generated by the function NormData(). |
meaningn |
default to be 20 |
Value
A list of distance vectors
Examples
data(sim_result)
meaningn <- 20
FindNNDistC(sim_result[[1]], sim_result[[2]], meaningn = meaningn)
Get broad and fine clusters
Description
Get broad and fine clusters
Usage
GetCluster(seuratlist, n1 = 50, n2 = 200)
Arguments
seuratlist |
A list of Seurat objects, usually can be got by SplitObject(). We also accept the SingleCellExperiment object input. |
n1 |
If the number of cells was smaller than n1, then the cluster will remain unchanged called rare cluster. The default value of n1 is 50. |
n2 |
If the count of cells within a broad cluster is more than n2, the cluster is subdivided randomly into three fine clusters. If the cell count falls within the range of n1 to n2, two fine clusters are generated randomly. Default value is 200. |
Value
A list of data frames.
Examples
data(sim_data_sce)
sim_data <- SCEtoSeurat(sim_data_sce)
seuratlist <- Seurat::SplitObject(sim_data, split.by = "Study")
fullcluster <- GetCluster(seuratlist)
Normalized RNA data matrix
Description
Normalized RNA data matrix
Usage
NormData(seuratlist)
Arguments
seuratlist |
A list of Seurat objects, usually can be got by SplitObject(). |
Value
A list of matrix.
Examples
data(sim_data_sce)
sim_data <- SCEtoSeurat(sim_data_sce)
seuratlist <- Seurat::SplitObject(sim_data, split.by = "Study")
normCount <- NormData(seuratlist)
Permutation Test
Description
Permutation Test
Usage
PermTest(fullcluster, distmat, firstn)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster() |
distmat |
A list of distance vectors generated by the function FindNNDist(). |
firstn |
The number of nearest cells were detected that you want to include in the permutation test. |
Value
A list of two lists, one is the relative within-between distance and another is p-value of permutation test. Default to be 15.
Examples
data(sim_result)
testres <- PermTest(sim_result[[1]], sim_result[[3]],15)
Plot SCIntRuler
Description
Plot SCIntRuler
Usage
PlotSCIR(fullcluster, seuratlist, testres, legendtitle = NULL, title = NULL)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster. |
seuratlist |
A list of Seurat objects, usually can be got by SplitObject(). |
testres |
Result from function PermTest() |
legendtitle |
Title of legend, default to be NULL |
title |
Title of figure, default to be NULL |
Value
A ggplot2 object
Examples
data(sim_data_sce)
data(sim_result)
sim_data <- SCEtoSeurat(sim_data_sce)
seuratlist <- Seurat::SplitObject(sim_data, split.by = "Study")
PlotSCIR(sim_result[[1]], seuratlist, sim_result[[4]])
Input and Split SingleCellExperiment Data
Description
This function takes a SingleCellExperiment object and a variable by which to split it, converts it to a Seurat object, and then splits it according to the specified variable.
Usage
SCEtoSeurat(sce)
Arguments
sce |
A SingleCellExperiment object. |
Value
A Seurat objects.
Examples
data(sim_data_sce)
# seuratlist <- InputData(sim_data_sce,"Study")
seuratobj <- SCEtoSeurat(sim_data_sce)
Get maximum number of broad clusters
Description
Get maximum number of broad clusters
Usage
SummCluster(fullcluster)
Arguments
fullcluster |
A list of clusters that generated by the function GetCluster() |
Value
A list
Examples
data(sim_result)
SCout <- SummCluster(sim_result[[1]])
Cross-Distance Matrix Calculation
Description
Computes the pairwise Euclidean distance between rows of two matrices.
Usage
crossdist(m1, m2)
Arguments
m1 |
Numeric matrix. |
m2 |
Numeric matrix. |
Value
Numeric matrix of distances.
Examples
mat1 <- matrix(1:4, ncol = 2)
mat2 <- matrix(5:8, ncol = 2)
dist_matrix <- crossdist(mat1, mat2)
My Example Dataset
Description
An example PBMC data with SingleCellExperiment format, including 3000 cells and 800 genes.
Usage
sim_data_sce
Format
An example PBMC data with SingleCellExperiment format
- int_elementMetadata
A DataFrame with 3000 rows and 1 column, storing simulated gene information.
- int_colData
A DataFrame with 800 rows and 3 columns, representing metadata for each cell.
- int_metadata
A list containing two elements that provide additional global metadata about the experiment.
- rowRanges
A CompressedGRangesList object providing genomic range data associated with each row/gene.
- colData
A DataFrame with 800 rows and 8 columns, detailing cell-level metadata.
- assays
A SimpleAssay object with matrix dimensions 3000x800, representing the gene expression matrix.
- elementMetadata
A DataFrame linked with assays, providing gene-level metadata.
Details
The "sim_data_sce" object is designed to serve as a teaching and development aid for methods that require complex single-cell expression data. It includes several typical features found in single-cell datasets, such as varied levels of gene expression and metadata describing both cells and genes.
The data within this object are entirely synthetic and should not be used for real analysis. The main use case is for testing and development of single-cell analysis methodologies.
Value
Simulation data to exemplify the usage of the method.
References
The data were generated using a combination of random number generation for expression values and curated sources for metadata to simulate realistic experimental scenarios.
Examples
data("sim_data_sce")
My Example Dataset
Description
An result example data with results from different functions.
Usage
sim_result
Format
An result example data
- fullcluster
A runable example of GetCluster, which is a list of clusters for each study.
- normCount
A runable example of NormData, which is a list of normalized RNA expression matrixs for each study.
- distmat
A runable example of FindNNDist, which is a list of distance matrixs for each study.
- testres
A runable example of CalcuSCIR, which is a list of test results for each study.
Value
Simulation data to examplify the usage of the method.
Examples
# Load the data
data("sim_result")