Type: Package
Title: Flexible Integration of Single-Cell RNA-Seq Data
Version: 0.1.2
Maintainer: Jingsi Ming <jsming@fem.ecnu.edu.cn>
Description: Provides functions for the flexible integration of heterogeneous scRNA-seq datasets across multiple tissue types, platforms, and experimental batches. Implements the method described in Ming (2022) <doi:10.1093/bib/bbac167>. The package incorporates modified 'C++' source code from the 'flashpca' library (Abraham, 2014-2016 https://github.com/gabraham/flashpca) for efficient principal component analysis, and the 'Spectra' library (Qiu, 2016-2025) for large-scale eigenvalue and singular value decomposition; see 'inst/COPYRIGHTS' for details on third-party code.
URL: https://github.com/mingjingsi/FIRM
BugReports: https://github.com/mingjingsi/FIRM/issues
License: GPL-3
Encoding: UTF-8
Copyright: See file 'inst/COPYRIGHTS' for details.
LazyData: true
LazyDataCompression: bzip2
Imports: Seurat (≥ 5.0.0), RANN
Suggests: ggplot2
LinkingTo: Rcpp, RcppArmadillo, RcppEigen
RoxygenNote: 7.3.3
NeedsCompilation: yes
Depends: R (≥ 3.5.0)
Packaged: 2026-02-16 03:46:03 UTC; dszspur
Author: Jingsi Ming [aut, cre, cph] (R package development and method implementation, ORCID: 0000-0001-7059-4156), Shuzhen Ding [ctb] (R package development and maintenance), Gad Abraham [ctb, cph] (Original 'flashpca' 'C++' code for fast randomized PCA), Yixuan Qiu [ctb, cph] ('Spectra' library for large-scale eigenvalue and SVD computation), Anna Araslanova [ctb, cph] (Author of 'LOBPCGSolver.h' in 'Spectra'), Felipe Zapata [ctb, cph] (Author of 'DavidsonSymEigsSolver.h' and 'Orthogonalization.h' in 'Spectra' (Netherlands eScience Center)), Nicolas Renaud [ctb, cph] (Author of 'RitzPairs.h' and 'SearchSpace.h' in 'Spectra' (Netherlands eScience Center)), Jens Wehner [ctb, cph] (Author of 'JDSymEigsBase.h' in 'Spectra' (Netherlands eScience Center)), Gael Guennebaud [ctb, cph] ('Eigen' library code for tridiagonal eigenvalue decomposition), Jitse Niesen [ctb, cph] ('Eigen' library code for eigenvalue computation)
Repository: CRAN
Date/Publication: 2026-02-19 19:40:02 UTC

Example single-cell datasets

Description

A list of Seurat objects and corresponding metadata from two 10x Genomics peripheral blood samples (SS2 and tenx).

Usage

ExampleData

Format

A list of 4 elements:

SS2

Seurat object from Smart-seq2 platform

tenx

Seurat object from 10x Chromium platform

meta_SS2

data.frame, cell-level metadata for SS2

meta_tenx

data.frame, cell-level metadata for tenx

Examples

data("ExampleData")
names(ExampleData)
head(ExampleData$meta_SS2)

Flexible Integration of Single-Cell RNA-Seq Data

Description

Performs unsupervised integration of two single-cell RNA-seq datasets by searching for the optimal clustering resolution pair that maximises mutual-nearest-neighbour (MNN) mixing in the combined PCA space. The final integrated expression matrix is returned after batch-effect correction.

Usage

FIRM(
  SS2,
  tenx,
  hvg1,
  hvg2,
  dims,
  all_genes = FALSE,
  res_seq_SS2 = seq(0.1, 2, 0.1),
  res_seq_tenx = seq(0.1, 2, 0.1),
  coreNum = 1,
  verbose = FALSE,
  seed = NULL
)

Arguments

SS2

Seurat object for reference dataset (e.g. Smart-seq2).

tenx

Seurat object for query dataset (e.g. 10x Genomics).

hvg1, hvg2

Character vectors giving the high-variable gene names selected in SS2 and tenx, respectively.

dims

Integer scalar, number of principal components to use during integration.

all_genes

Logical scalar. If TRUE the integration is carried out on the union of all genes; otherwise only on the intersected HVGs (default).

res_seq_SS2, res_seq_tenx

Numeric vectors of clustering resolutions to be screened for the reference and query dataset, respectively. Defaults to seq(0.1, 2, 0.1).

coreNum

Integer scalar, number of CPU cores used for parallel screening.

verbose

Logical scalar. If TRUE a list containing the integrated matrix and quality metrics is returned; otherwise only the integrated expression matrix is returned.

seed

Integer, random seed for reproducibility (optional). If NULL, uses current random state.

Details

The algorithm performs the following steps:

  1. PCA on each dataset using the intersected HVGs.

  2. SNN graph construction (via Seurat::FindNeighbors).

  3. Screening of clustering resolution pairs (res_seq_SS2 × res_seq_tenx) to maximise mutual-nearest-neighbour mixing in the joint PCA space.

  4. Batch-effect correction with FIRM_res* functions.

  5. Final integrated expression matrix is scaled and returned.

Quality control: the integrated embedding is compared with the naive PCA; if correction does not improve mixing the latter is returned.

Value

By default (verbose = FALSE) a single matrix of batch-corrected, scaled expression values with genes as rows and combined cells as columns.

If verbose = TRUE a named list is returned:

integrated

As above, the corrected expression matrix.

Metric_PCA

Mean MNN mixing score of the naive PCA (no correction).

Metric_FIRM

Mean MNN mixing score of the FIRM-corrected embedding (matrix when multiple resolution pairs were screened).

References

Ming, J., Lin, Z., Zhao, J., Wan, X., Ezran, C., Liu, S., ... & TTM Consortium. (2022). FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets. Briefings in bioinformatics, 23(5).

See Also

prep_data for Data preprocessing.

Examples


set.seed(42)

library(Seurat)
library(FIRM)

data("ExampleData")
prep_SS2  <- prep_data(ExampleData$SS2, hvg_genes = 1000)
Dataset1  <- prep_SS2$Dataset
hvg1      <- prep_SS2$hvg

prep_tenx <- prep_data(ExampleData$tenx, hvg_genes = 1000)
Dataset2  <- prep_tenx$Dataset
hvg2      <- prep_tenx$hvg

res <- FIRM(Dataset1, Dataset2, hvg1, hvg2,
           dims = 15, all_genes = FALSE, seed = 42)

dim(res)

Local structure preservation metric

Description

Compute the neighbourhood overlap between the original PCA space of each dataset and the integrated PCA space, averaged over the top neighbors nearest neighbours. High overlap indicates better local structure preservation after integration.

Usage

Local_Struct(
  SS2,
  tenx,
  integrated,
  dims = 30,
  emb_key = "pca",
  batch_key = "dataset",
  neighbors = 20
)

Arguments

SS2, tenx

Seurat objects containing the **unintegrated** PCA reduction (slot reductions$pca).

integrated

Seurat object that contains the **joint** PCA reduction and a meta-data column dataset.

dims

Number of principal components to use (default 30).

emb_key

Name of the reduction to use (default "pca").

batch_key

Name of the meta-data column that indicates dataset/batch origin (default "dataset").

neighbors

Size of the neighbourhood to evaluate (default 20).

Value

A named list with two numeric vectors:

SS2

Per-cell overlap scores for the SS2 dataset.

tenx

Per-cell overlap scores for the 10X dataset.

k-NN Mixing Metric

Description

Evaluate the degree of mixing between two or more datasets in a low-dimensional embedding.

Usage

Mixing_Metric(embedding, dataset_list, k = 5, max.k = 300, seed = NULL)

Arguments

embedding

Numeric matrix (n × d) where each row is the coordinates of one sample in the low-dimensional space (PCA, UMAP, t-SNE, …).

dataset_list

Factor or character vector of length n indicating which dataset each sample comes from.

k

Positive integer. The rank of the within-dataset neighbour to look for (default 5).

max.k

Positive integer. Total number of nearest neighbours to compute (default 300).

seed

Integer, random seed for reproducibility (optional). If NULL, uses current random state.

Value

Named numeric vector with one entry per unique level of 'dataset_list'. The entry is the median position (among 1 … max.k) of the k-th within-dataset neighbour across all samples that belong to that dataset. Values are clamped to max.k when fewer than k within-dataset neighbours exist.

Select consensus genes across multiple HVG lists

Description

Rank-based selection of genes that appear in multiple HVG lists, prioritising those present in all datasets.

Usage

SelectGene(hvg_list, gene_all = NULL, num = 4000)

Arguments

hvg_list

List of character vectors, each containing HVGs from one dataset.

gene_all

Optional character vector of all candidate genes (intersection filter).

num

Target number of genes to return (default 4 000).

Value

Character vector of selected genes.

Combine or select top HVGs from saved prep_data outputs

Description

Load HVG lists saved by prep_data and either take their union or rank-based top consensus.

Usage

Select_hvg(file_names, file_path, method, hvg_genes = 4000)

Arguments

file_names

Character vector of basenames (without "_hvg.RData").

file_path

Directory containing the *_hvg.RData files.

method

"all" for union, "top" for ranked consensus (see SelectGene).

hvg_genes

Target number of genes to return (default 4 000).

Value

Character vector of selected genes.

See Also

prep_data

Label transfer via k-NN (hard assignment)

Description

For every query cell, find its k nearest reference cells in the integrated PCA space and assign the most frequent label among them.

Usage

label_trans(
  integrated,
  ref = "10X",
  query = "SS2",
  label_key = "annotation",
  emb_key = "pca",
  batch_key = "dataset",
  k = 10,
  seed = NULL
)

Arguments

integrated

Seurat object with integrated PCA and meta-data columns dataset and annotation.

ref

Character string identifying the reference dataset (default "10X").

query

Character string identifying the query dataset (default "SS2").

label_key

Name of the meta-data column that indicates celltype

emb_key

Name of the reduction to use (default "pca").

batch_key

Name of the meta-data column that indicates dataset/batch

k

Number of nearest neighbours to vote (default 10).

seed

Integer, random seed for reproducibility (optional). If NULL, uses current random state.

Value

Named character vector: predicted label for each query cell (names are cell barcodes).

Label transfer via k-NN (soft assignment / probabilities)

Description

Identical to label_trans but returns a probability matrix instead of a hard label.

Usage

label_trans_prob(
  integrated,
  ref = "10X",
  query = "SS2",
  label_key = "annotation",
  emb_key = "pca",
  batch_key = "dataset",
  k = 10,
  seed = NULL
)

Arguments

integrated

Seurat object containing the joint embedding and meta-data.

ref

Name of the reference dataset/batch (default "10X").

query

Name of the query dataset/batch (default "SS2").

label_key

Name of the meta-data column that stores cell-type / label information (default "annotation").

emb_key

Name of the reduction to use (default "pca").

batch_key

Name of the meta-data column that indicates dataset/batch origin (default "dataset").

k

Number of nearest neighbours to consider (default 10).

seed

Integer, random seed for reproducibility (optional). If NULL, uses current random state.

Value

Numeric matrix (query-cells × unique-labels). Row sums equal 1. Row names are query cell barcodes, column names are reference labels.

Match score between query and reference neighbourhoods

Description

Compute, for each query cell, the ratio of the average within-query distance to the average query-to-reference distance in the integrated PCA space. Smaller values imply better alignment.

Usage

match_score(
  integrated,
  ref = "10X",
  query = "SS2",
  label_key = "annotation",
  emb_key = "pca",
  batch_key = "dataset",
  k = 10,
  seed = NULL
)

Arguments

integrated

Seurat object containing the joint embedding and meta-data.

ref

Name of the reference dataset/batch (default "10X").

query

Name of the query dataset/batch (default "SS2").

label_key

Name of the meta-data column that stores cell-type / label information (default "annotation").

emb_key

Name of the reduction to use (default "pca").

batch_key

Name of the meta-data column that indicates dataset/batch origin (default "dataset").

k

Number of nearest neighbours to consider (default 10).

seed

Integer, random seed for reproducibility (optional). If NULL, uses current random state.

Value

Named numeric vector of length equal to the number of query cells.

Data preprocessing for FIRM integration

Description

Performs a standard Seurat workflow: normalization, scaling and selection of the top 4 000 highly-variable genes (HVGs).

Usage

prep_data(counts, hvg_genes = 4000)

Arguments

counts

Raw count matrix (genes × cells) or dgCMatrix.

hvg_genes

Target number of genes to return (default 4 000).

Value

A named list with elements

Dataset

Seurat object after normalization, scaling and feature selection.

hvg

Character vector of the 4 000 most variable genes.

Author(s)

Jingsi Ming

See Also

FIRM for the integration step.

Examples


set.seed(42)
library(Seurat)
library(FIRM)
data("ExampleData")
prep_SS2  <- prep_data(ExampleData$SS2, hvg_genes = 1000)
Dataset1  <- prep_SS2$Dataset
hvg1      <- prep_SS2$hvg

prep_tenx <- prep_data(ExampleData$tenx, hvg_genes = 1000)
Dataset2  <- prep_tenx$Dataset
hvg2      <- prep_tenx$hvg

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