Explore data integration and batch effects
Introduction
The CellMixS package is a toolbox to explore and compare group effects in single-cell RNA-seq data. It has two major applications:
- Detection of batch effects and biases in single-cell RNA-seq
data.
- Evaluation and comparison of data integration (e.g. after batch effect correction).
For this purpose it introduces two new metrics:
- Cellspecific Mixing Score (CMS): A test for batch
effects within k-nearest neighbouring cells.
- Local Density Differences (ldfDiff): A score describing the change in relative local cell densities by data integration or projection.
It also provides implementations and wrappers for a set of metrics with a similar purpose: entropy, the inverse Simpson index (Korsunsky et al. 2018), and Seurat’s mixing metric and local structure metric (Stuart et al. 2018). Besides this, several exploratory plotting functions enable evaluation of key integration and mixing features.
Installation
CellMixS can be installed from Bioconductor as follows.
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("CellMixS")After installation the package can be loaded into R.
Getting started
Load example data
CellMixS
uses the SingleCellExperiment class from the SingleCellExperiment
Bioconductor package as the format for input data.
The package contains example data named sim50, a list of simulated single-cell RNA-seq data with varying batch effect strength and unbalanced batch sizes.
Batch effects were introduced by sampling 0%, 20% or 50% of gene expression values from a distribution with modified mean value (e.g. 0% - 50% of genes were affected by a batch effect).
All datasets consist of 3 batches, one with 250 cells and the others with half of its size (125 cells). The simulation is modified after (Büttner et al. 2019) and described in sim50.
# Load required packages
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(cowplot)
library(limma)
library(magrittr)
library(dplyr)
library(purrr)
library(ggplot2)
library(scater)
})# Load sim_list example data
sim_list <- readRDS(system.file(file.path("extdata", "sim50.rds"),
package = "CellMixS"))
names(sim_list)
#> [1] "batch0" "batch20" "batch50"
sce50 <- sim_list[["batch50"]]
class(sce50)
#> [1] "SingleCellExperiment"
#> attr(,"package")
#> [1] "SingleCellExperiment"
table(sce50[["batch"]])
#>
#> 1 2 3
#> 250 125 125Visualize batch effect
Often batch effects can already be detected by visual inspection and
simple visualization (e.g. in a normal tSNE or UMAP plot) depending on
the strength. CellMixS
contains various plotting functions to visualize group label and mixing
scores aside. Results are ggplot objects and can be further
customized using ggplot2.
Other packages, such as scater,
provide similar plotting functions and could be used instead.
# Visualize batch distribution in other elements of sim_list
batch_names <- c("batch0", "batch20")
vis_batch <- lapply(batch_names, function(name){
sce <- sim_list[[name]]
visGroup(sce, "batch") + ggtitle(paste0("sim_", name))
})
plot_grid(plotlist = vis_batch, ncol = 2)
Quantify batch effects
Cellspecific Mixing scores
Not all batch effects or group differences are obvious using visualization. Also, in single-cell experiments celltypes and cells can be affected in different ways by experimental conditions causing batch effects (e.g. some cells are more robust to storing conditions than others).
Furthermore the range of methods for data integration and batch effect removal gives rise to the question of which method performs best on which data, and thereby a need to quantify batch effects.
The cellspecific mixing score cms tests
for each cell the hypothesis that batch-specific distance distributions
towards it’s k-nearest neighbouring (knn) cells are derived from the
same unspecified underlying distribution using the Anderson-Darling test
(Scholz and Stephens 1987). Results from
the cms function are two scores cms and
cms_smooth, where the latter is the weighted mean of the cms
within each cell’s neighbourhood. They can be interpreted as the data’s
probability within an equally mixed neighbourhood. A high
cms score refers to good mixing, while a low score
indicates batch-specific bias. The test considers differences in the
number of cells from each batch. This facilitates the cms
score to differentiate between unbalanced batches (e.g. one celltype
being more abundant in a specific batch) and a biased distribution of
cells. The cms function takes a
SingleCellExperiment object (described in SingleCellExperiment)
as input and appends results to the colData slot.
Parameter
# Call cell-specific mixing score for sce50
# Note that cell_min is set to 4 due to the low number of cells and small k.
# Usually default parameters are recommended!!
sce50 <- cms(sce50, k = 30, group = "batch", res_name = "unaligned",
n_dim = 3, cell_min = 4)
head(colData(sce50))
#> DataFrame with 6 rows and 3 columns
#> batch cms_smooth.unaligned cms.unaligned
#> <factor> <numeric> <numeric>
#> cell_1 1 0.00000000 0
#> cell_2 1 0.01969896 0
#> cell_3 1 0.00000000 0
#> cell_4 1 0.00823378 0
#> cell_5 1 0.02896438 0
#> cell_6 1 0.06544070 0
# Call cell-specific mixing score for all datasets
sim_list <- lapply(batch_names, function(name){
sce <- sim_list[[name]]
sce <- cms(sce, k = 30, group = "batch", res_name = "unaligned",
n_dim = 3, cell_min = 4)
}) %>% set_names(batch_names)
# Append cms50
sim_list[["batch50"]] <- sce50Defining neighbourhoods
A key question when evaluating dataset structures is how to define
neighbourhoods, or in this case, which cells to use to calculate the
mixing. cms provides 3 options to define cells included in
each Anderson-Darling test:
- Number of knn: This is the default setting and can
be set by the parameter
k. The optimal choice depends on the application, as with a smallkfocus is on local mixing, while with a largekmixing with regard to more global structures is evaluated. In generalkshould not exceed the size of the smallest cell population as including cells from different cell populations can conflict with the underlying assumptions of distance distributions. - Density based neighbourhoods: In cases of unequal
cell population sizes the optimal number of neighbours might vary. Using
the size of the smallest population can lead to suboptimal
neighbourhoods for larger populations in these cases, as the power of
the AD-test increases with cell numbers. For these cases or others where
a local adaptation of the neighbourhood is desired the
k_minparameter is provided. It denotes the minimum number of cells that define a neighbourhood. Starting with the knn as defined bykthecmsfunction will cut neighbourhoods by their first local minimum in the overall distance distribution of the knn cells. Only cells within a distance smaller than the first local minimum are included in the AD-test, but at leastk_mincells. - Fixed minimum per batch: Another option to define a
dynamic neighbourhood is provided by the
batch_minparameter. It defines the minimum number of cells for each batch that shall be included into each neighbourhood. In this case the neighbourhoods will include an increasing number of neighbours until at leastbatch_mincells from each batch are included.
Further cms parameter settings
For smoothing, either k or (if specified)
k_min cells are included to get a weighted mean of
cms-scores. Weights are defined by the reciprocal distance
towards the respective cell, with 1 as weight of the respective cell
itself.
Another important parameter is the subspace to use to calculate cell
distances. This can be set using the dim_red parameter. By
default the PCA subspace will be used and calculated if not
present. Some data integration methods provide embeddings of a
common subspace instead of “corrected counts”. cms
scores can be calculated within these by defining them with the
dim_red argument (see @ref(di1)). In general all reduced
dimension representations can be specified, but only PCA will
be computed automatically, while other methods need to be
precomputed.
Visualize the cell mixing score
An overall summary of cms scores can be visualized as a
histogram. As cms scores are p.values from
hypothesis testing, without any batch effect the p.value histogram
should be flat. An increased number of very small p-values indicates the
presence of a batch-specific bias within data.
# p-value histogram sim30
# Combine cms results in one matrix
batch_names <- names(sim_list)
cms_mat <- batch_names %>%
map(function(name) sim_list[[name]]$cms.unaligned) %>%
bind_cols() %>% set_colnames(batch_names)
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
visHist(cms_mat, n_col = 3)
Results of cms can be visualized in a cell-specific way
and alongside any metadata.
# cms only cms20
sce20 <- sim_list[["batch20"]]
metric_plot <- visMetric(sce20, metric_var = "cms_smooth.unaligned")
# group only cms20
group_plot <- visGroup(sce20, group = "batch")
plot_grid(metric_plot, group_plot, ncol = 2)
# Add random celltype assignments as new metadata
sce20[["celltype"]] <- rep(c("CD4+", "CD8+", "CD3"), length.out = ncol(sce20)) %>%
as.factor
visOverview(sce20, "batch", other_var = "celltype")
Systematic differences (e.g. celltype differences) can be further
explored using visCluster. Here we do not expect any
systematic difference as celltypes were randomly assigned.
visCluster(sce20, metric_var = "cms.unaligned", cluster_var = "celltype")
#> Picking joint bandwidth of 0.0996
Evaluate data integration
Mixing after data integration
To remove batch effects when integrating different single-cell RNAseq
datasets, a range of methods can be used. The cms function
can be used to evaluate the effect of these methods, using a
cell-specific mixing score. Some of them (e.g. fastMNN from
the batchelor
package) provide a “common subspace” with integrated embeddings. Other
methods like limma give
“batch-corrected data” as results. Both work as input for
cms.
# MNN - embeddings are stored in the reducedDims slot of sce
reducedDimNames(sce20)
#> [1] "TSNE" "PCA" "MNN"
sce20 <- cms(sce20, k = 30, group = "batch",
dim_red = "MNN", res_name = "MNN", n_dim = 3, cell_min = 4)
# Run limma
sce20 <- scater::logNormCounts(sce20)
limma_corrected <- removeBatchEffect(logcounts(sce20), batch = sce20$batch)
# Add corrected counts to sce
assay(sce20, "lim_corrected") <- limma_corrected
# Run cms
sce20 <- cms(sce20, k = 30, group = "batch",
assay_name = "lim_corrected", res_name = "limma", n_dim = 3,
cell_min = 4)
names(colData(sce20))
#> [1] "batch" "cms_smooth.unaligned" "cms.unaligned"
#> [4] "celltype" "cms_smooth.MNN" "cms.MNN"
#> [7] "sizeFactor" "cms_smooth.limma" "cms.limma"Compare data integration methods
To compare different methods, summary plots from
visIntegration (see @ref(ldf)) and p-value histograms from
visHist can be used. Local patterns within single methods
can be explored as described above.
Here both methods limma and
fastMNN from the scran
package flattened the p.value distribution. So cells are better mixed
after batch effect removal.
Remaining batch-specific structure - ldfDiff
Besides successful batch “mixing”, data integration should also preserve the data’s internal structure and variability without adding new sources of variability or removing underlying structures. Especially for methods that result in “corrected counts” it is important to understand how much of the dataset’s internal structures are preserved.
ldfDiff calculates the differences between each cell’s
local density factor before and after data integration
(Latecki, Lazarevic, and Pokrajac 2007).
The local density factor is a relative measure of the cell density
around a cell compared to the densities within its neighbourhood. Local
density factors are calculated on the same set of k cells from the
cell’s knn before integration. In an optimal case relative densities
(according to the same set of cells) should not change by integration
and the ldfDiff score should be close to 0. In general the
overall distribution of ldfDiff should be centered around 0
without long tails.
# Prepare input
# List with single SingleCellExperiment objects
# (Important: List names need to correspond to batch levels! See ?ldfDiff)
sce_pre_list <- list("1" = sce20[,sce20$batch == "1"],
"2" = sce20[,sce20$batch == "2"],
"3" = sce20[,sce20$batch == "3"])
sce20 <- ldfDiff(sce_pre_list, sce_combined = sce20,
group = "batch", k = 70, dim_red = "PCA",
dim_combined = "MNN", assay_pre = "counts",
n_dim = 3, res_name = "MNN")
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sce20 <- ldfDiff(sce_pre_list, sce_combined = sce20,
group = "batch", k = 70, dim_red = "PCA",
dim_combined = "PCA", assay_pre = "counts",
assay_combined = "lim_corrected",
n_dim = 3, res_name = "limma")
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names(colData(sce20))
#> [1] "batch" "cms_smooth.unaligned" "cms.unaligned"
#> [4] "celltype" "cms_smooth.MNN" "cms.MNN"
#> [7] "sizeFactor" "cms_smooth.limma" "cms.limma"
#> [10] "diff_ldf.MNN" "diff_ldf.limma"Visualize ldfDiff
Results from ldfDiff can be visualized in a similar way
as results from cms.
# ldfDiff score summarized
visIntegration(sce20, metric = "diff_ldf", metric_name = "ldfDiff")
#> Picking joint bandwidth of 0.0867
ldfDiff shows a clear difference between the two
methods. While limma is
able to preserve the batch internal structure within batches,
fastMNN clearly changes it. Even if batches are well mixed
(see @ref(di2)), fastMNN does not work for batch effect
removal on these simulated data. Again this is in line with expectations
due to the small number of genes in the example data. One of MNN’s
assumptions is that batch effects should be much smaller than biological
variation, which does not hold true in this small example dataset.
Testing different metrics
Often it is useful to check different aspects of data mixing and
integration by the use of different metrics, as many of them emphasize
different features of mixing. To provide an easy interface for thorough
investigation of batch effects and data integration a wrapper function
of a variety of metrics is included into CellMixS.
evalIntegration calls one or all of cms,
ldfDiff, entropy or equivalents to
mixingMetric, localStruct from the Seurat package
or isi, a simplfied version of the local inverse Simpson
index as suggested by (Korsunsky et al.
2018). entropy calculates the Shannon entropy within
each cell’s knn describing the randomness of the batch
variable. isi calculates the inverse Simpson index within
each cell’s knn. The Simpson index describes the probability
that two entities are taken at random from the dataset and its inverse
represents the effective number of batches in the neighbourhood. A
simplified version of the distance based weightening as proposed by
(Korsunsky et al. 2018) is provided by the
weight option. As before the resulting scores are included into the
colData slot of the input SingleCellExperiment object and
can be visualized with visMetric and other plotting
functions.
sce50 <- evalIntegration(metrics = c("isi", "entropy"), sce50,
group = "batch", k = 30, n_dim = 2, cell_min = 4,
res_name = c("weighted_isi", "entropy"))
visOverview(sce50, "batch",
metric = c("cms_smooth.unaligned", "weighted_isi", "entropy"),
prefix = FALSE)
Session info
sessionInfo()
#> R version 4.4.2 (2024-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] scater_1.35.0 scuttle_1.17.0
#> [3] ggplot2_3.5.1 purrr_1.0.2
#> [5] dplyr_1.1.4 magrittr_2.0.3
#> [7] limma_3.63.2 cowplot_1.1.3
#> [9] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
#> [11] Biobase_2.67.0 GenomicRanges_1.59.1
#> [13] GenomeInfoDb_1.43.2 IRanges_2.41.1
#> [15] S4Vectors_0.45.2 BiocGenerics_0.53.3
#> [17] generics_0.1.3 MatrixGenerics_1.19.0
#> [19] matrixStats_1.4.1 CellMixS_1.23.0
#> [21] kSamples_1.2-10 SuppDists_1.1-9.8
#> [23] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 viridisLite_0.4.2 farver_2.1.2
#> [4] vipor_0.4.7 viridis_0.6.5 fastmap_1.2.0
#> [7] digest_0.6.37 rsvd_1.0.5 lifecycle_1.0.4
#> [10] statmod_1.5.0 compiler_4.4.2 rlang_1.1.4
#> [13] sass_0.4.9 tools_4.4.2 utf8_1.2.4
#> [16] yaml_2.3.10 knitr_1.49 labeling_0.4.3
#> [19] S4Arrays_1.7.1 DelayedArray_0.33.2 abind_1.4-8
#> [22] BiocParallel_1.41.0 withr_3.0.2 sys_3.4.3
#> [25] grid_4.4.2 fansi_1.0.6 beachmat_2.23.2
#> [28] colorspace_2.1-1 scales_1.3.0 ggridges_0.5.6
#> [31] cli_3.6.3 rmarkdown_2.29 crayon_1.5.3
#> [34] httr_1.4.7 ggbeeswarm_0.7.2 cachem_1.1.0
#> [37] zlibbioc_1.52.0 parallel_4.4.2 BiocManager_1.30.25
#> [40] XVector_0.47.0 vctrs_0.6.5 Matrix_1.7-1
#> [43] jsonlite_1.8.9 BiocSingular_1.23.0 BiocNeighbors_2.1.1
#> [46] ggrepel_0.9.6 irlba_2.3.5.1 beeswarm_0.4.0
#> [49] maketools_1.3.1 jquerylib_0.1.4 tidyr_1.3.1
#> [52] glue_1.8.0 codetools_0.2-20 gtable_0.3.6
#> [55] UCSC.utils_1.3.0 ScaledMatrix_1.15.0 munsell_0.5.1
#> [58] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.8.1
#> [61] GenomeInfoDbData_1.2.13 R6_2.5.1 evaluate_1.0.1
#> [64] lattice_0.22-6 bslib_0.8.0 Rcpp_1.0.13-1
#> [67] gridExtra_2.3 SparseArray_1.7.2 xfun_0.49
#> [70] buildtools_1.0.0 pkgconfig_2.0.3