The goal of
lemur is
to simplify the analysis of multi-condition single-cell data. If you
have collected a single-cell RNA-seq dataset with more than one
condition, lemur predicts for each cell and gene what the expression
data would be in all of the other conditions. Furthermore, lemur finds
neighborhoods of cells that show consistent differential expression. The
results are statistically validated using a pseudo-bulk differential
expression test on hold-out data using
glmGamPoi or
edgeR.
lemur implements a novel framework to disentangle the effects of known
covariates, latent cell states, and their interactions. At the core, is
a combination of matrix factorization and regression analysis
implemented as geodesic regression on Grassmann manifolds. We call this
latent embedding multivariate regression. For more details see our
preprint.
You can install lemur directly from Bioconductor. Just paste the
following snippet into your R console:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("lemur")Alternatively, you can install the package from Github using devtools:
devtools::install_github("const-ae/lemur")We continue working on improvements to this package. We are delighted if you decide to try out the package. Please use the Bioconductor forum or open an issue in the GitHub repository if you think you found a bug, have an idea for a cool feature, or have any questions about how LEMUR works.
A basic lemur workflow is as easy as the following.
# ... sce is a SingleCellExperiment object with your data
fit <- lemur(sce, design = ~ patient_id + condition, n_embedding = 15)
fit <- align_harmony(fit) # This step is optional
fit <- test_de(fit, contrast = cond(condition = "ctrl") - cond(condition = "panobinostat"))
nei <- find_de_neighborhoods(fit, group_by = vars(patient_id, condition))We will now go through these steps one by one.
We demonstrate lemur using data by Zhao et
al. (2021). The data
consist of tumor biopsies from five glioblastomas, each of which was
treated with the drug panobinostat and with a control. Accordingly, we
look at ten samples in a paired experimental design.
We start by loading required packages.
library("tidyverse")
library("SingleCellExperiment")
library("lemur")
set.seed(42)The lemur package ships with a reduced-size version of the
glioblastoma data, which we use in the following.
data("glioblastoma_example_data", package = "lemur")
glioblastoma_example_data
#> class: SingleCellExperiment
#> dim: 300 5000
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(300): ENSG00000210082 ENSG00000118785 ... ENSG00000167468 ENSG00000139289
#> rowData names(6): gene_id symbol ... strand. source
#> colnames(5000): CGCCAGAGCGCA AGCTTTACTGCG ... TGAACAGTGCGT TGACCGGAATGC
#> colData names(10): patient_id treatment_id ... sample_id id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):As is, the data separated by the known covariates patient_id and
condition.
orig_umap <- uwot::umap(as.matrix(t(logcounts(glioblastoma_example_data))))
as_tibble(colData(glioblastoma_example_data)) |>
mutate(umap = orig_umap) |>
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = patient_id, shape = condition), size = 0.5) +
labs(title = "UMAP of logcounts") + coord_fixed()
UMAP of the example dataset `glioblastoma_example_data`, prior to any between-condition alignment.
We fit the LEMUR model by calling the function lemur. We provide the
experimental design using a formula. The elements of the formula can
refer to columns of the colData of the SingleCellExperiment object.
We also set the number of latent dimensions, n_embedding, which has a
similar interpretation as the number of dimensions in PCA. The
test_fraction argument sets the fraction of cells which are
exclusively used to test for differential expression and not for
inferring the LEMUR parameters. It balances the sensitivity to detect
subtle patterns in the latent space against the power to detect
differentially expressed genes.
fit <- lemur(glioblastoma_example_data, design = ~ patient_id + condition,
n_embedding = 15, test_fraction = 0.5)
fit
#> class: lemur_fit
#> dim: 300 5000
#> metadata(9): n_embedding design ... use_assay row_mask
#> assays(2): counts logcounts
#> rownames(300): ENSG00000210082 ENSG00000118785 ... ENSG00000167468 ENSG00000139289
#> rowData names(6): gene_id symbol ... strand. source
#> colnames(5000): CGCCAGAGCGCA AGCTTTACTGCG ... TGAACAGTGCGT TGACCGGAATGC
#> colData names(10): patient_id treatment_id ... sample_id id
#> reducedDimNames(2): linearFit embedding
#> mainExpName: NULL
#> altExpNames(0):The lemur function returns an object of class lemur_fit, which extends
the SingleCellExperiment class. It supports subsetting and all the
usual accessor methods (e.g., nrow, assay, colData, rowData). In
addition, lemur overloads the $ operator to allow easy access to
additional fields produced by the LEMUR model. For example, the
low-dimensional embedding can be accessed using fit$embedding:
fit$embedding |> str()
#> num [1:15, 1:5000] 8.7543 0.0759 -1.0401 4.8462 0.529 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : NULL
#> ..$ : chr [1:5000] "CGCCAGAGCGCA" "AGCTTTACTGCG" "AGGATGACCGCA" "AGAACTATTTTT" ...Optionally, we can further align corresponding cells using manually
annotated cell types (align_by_grouping) or an automated alignment
procedure (e.g., align_harmony). This ensures that corresponding cells
are close to each other in the fit$embedding.
fit <- align_harmony(fit)
#> Select cells that are considered close with 'harmony'@fig-lemur_umap shows a UMAP of fit$embedding. This is similar to
working on the integrated PCA space in a traditional single-cell
analysis.
umap <- uwot::umap(t(fit$embedding))
as_tibble(fit$colData) |>
mutate(umap = umap) |>
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = patient_id), size = 0.5) +
facet_wrap(vars(condition)) + coord_fixed()
UMAPs of `fit$embedding`. The points are shown separately for the two conditions, but reside in the same latent space.
Next, let’s predict the effect of the panobinostat treatment for
each cell and each gene – even for the cells that were observed in
the control condition. The test_de function takes a lemur_fit object
and returns the object with a new slot (in SummarizedExperiment
parlance: assay) called DE. This slot contains the predicted
logarithmic fold changes between the two conditions specified in
contrast. Note that lemur implements a special notation for
contrasts. Instead of providing a contrast vector or design matrix
column names, you provide for each condition the levels, and lemur
automatically forms the contrast vector. This is intended to make the
notation more readable.
fit <- test_de(fit, contrast = cond(condition = "panobinostat") - cond(condition = "ctrl"))We can pick any gene, say GAP43, which in our data is represented by its Ensembl gene ID ENSG00000172020, and show its differential expression pattern on the UMAP plot:
df <- tibble(umap = umap) |>
mutate(de = assay(fit, "DE")["ENSG00000172020", ])
ggplot(df, aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = de)) +
scale_color_gradient2(low = "#FFD800", high= "#0056B9") + coord_fixed()
ggplot(df, aes(x = de)) + geom_histogram(bins = 100)
Differential expression (log fold changes) of GAP43
More systematically, we can now search through all the genes, and use
their expression values (assay(fit, "DE")) to search for cell
neighborhoods (sets of cells that are close together in latent space)
that show consistent differential expression. The function
find_de_neighborhoods validates the results of such a search with a
pseudobulked diferential expression test. For that, it uses the test
data (fit$test_data) that was put aside in the first call to
lemur(). In addition, find_de_neighborhoods assesses if the
difference between the conditions is significantly larger for the cells
inside the neighborhood than the cells outside the neighborhood (see
columns starting with did, short for difference-in-difference).
The group_by argument determines how the pseudobulk samples are
formed. It specifies the columns in the fit$colData that are used to
define a sample and is inspired by the group_by function in dplyr.
Typically, you provide the covariates that were used for the
experimental design plus the sample id (in this case patient_id).
neighborhoods <- find_de_neighborhoods(fit, group_by = vars(patient_id, condition))
as_tibble(neighborhoods) |>
left_join(as_tibble(rowData(fit)[,1:2]), by = c("name" = "gene_id")) |>
relocate(symbol, .before = "name") |>
arrange(pval) |>
head(5)
#> # A tibble: 5 × 14
#> symbol name neighborhood n_cells sel_statistic pval adj_pval f_statistic df1 df2 lfc
#> <chr> <chr> <I<list>> <int> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl>
#> 1 MT1X ENSG000… <chr> 2410 42.8 6.93e-6 0.00208 148. 1 6.85 3.20
#> 2 PMP2 ENSG000… <chr> 3880 -160. 3.89e-5 0.00583 86.8 1 6.85 -1.33
#> 3 NEAT1 ENSG000… <chr> 3771 59.2 2.92e-4 0.0232 45.5 1 6.85 1.86
#> 4 SKP1 ENSG000… <chr> 3196 34.1 3.59e-4 0.0232 42.5 1 6.85 0.777
#> 5 POLR2L ENSG000… <chr> 3825 115. 3.87e-4 0.0232 41.4 1 6.85 1.23
#> # ℹ 3 more variables: did_pval <dbl>, did_adj_pval <dbl>, did_lfc <dbl>To continue, we investigate one gene for which the neighborhood shows a
significant differential expression pattern: here we choose a CXCL8
(also known as interleukin 8), an important inflammation signalling
molecule. We see that it is upregulated by panobinostat in a subset of
cells (blue). We chose this gene because it (1) had a significant change
between panobinostat and negative control condition (adj_pval column)
and (2) showed much larger differential expression for the cells inside
the neighborhood than for the cells outside (did_lfc column).
sel_gene <- "ENSG00000169429" # is CXCL8
p <- tibble(umap = umap) |>
mutate(de = assay(fit, "DE")[sel_gene,]) |>
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = de)) +
scale_color_gradient2(low = "#FFD800", high= "#0056B9") +
coord_fixed()
p
Differential expression of CXCL8 superimposed on the same UMAP plot as in @fig-lemur_umap.
Next, we are going to try something ambitious: in the LEMUR model, the
cells in a neighborhood are separated from the rest of the cells by a
n_embedding from above, i.e.,
To this end, we create a helper dataframe and use the geom_density2d
function from ggplot2. To avoid the cutting of the boundary to the
extremes of the cell coordinates, add lims to the plot with an
appropriately large limit.
neighborhood_coordinates <- neighborhoods |>
dplyr::filter(name == sel_gene) |>
unnest(c(neighborhood)) |>
dplyr::rename(cell_id = neighborhood) |>
left_join(tibble(cell_id = rownames(umap), umap), by = "cell_id") |>
dplyr::select(name, cell_id, umap)
p + geom_density2d(data = neighborhood_coordinates, breaks = seq(0.2, 0.6, by = 0.1),
contour_var = "ndensity", color = "#808080") 
Same as @fig-umap_de2, with an attempt to draw a neighborhood boundary.
To summarize our results, we can make a volcano plot of the differential expression results to better understand the expression differences across all genes.
neighborhoods |>
drop_na() |>
ggplot(aes(x = lfc, y = -log10(pval))) +
geom_point(aes(col = adj_pval < 0.1)) 
Volcano plot, each point corresponds to one neighborhood.
neighborhoods |>
drop_na() |>
ggplot(aes(x = n_cells, y = -log10(pval))) +
geom_point(aes(color = adj_pval < 0.1)) 
Neighborhood size vs neighborhood significance.
The analyses up to here were conducted without using any cell type information. Often, such additional cell type information is available or can be obtained from the data by other means. For instance, here, we can distinguish the tumor cells from non-malignment other cell, using the fact that the tumor cells had a deletion of Chromosome 10 and a duplication of Chromosome 7. We build a simple classifier to distinguish the cells accordingly. (This is just to illustrate the process; for a real analysis, we would use more sophisticated methods.)
tumor_label_df <- tibble(cell_id = colnames(fit),
chr7_total_expr = colMeans(logcounts(fit)[rowData(fit)$chromosome == "7",]),
chr10_total_expr = colMeans(logcounts(fit)[rowData(fit)$chromosome == "10",])) |>
mutate(is_tumor = chr7_total_expr > 0.8 & chr10_total_expr < 2.5)
ggplot(tumor_label_df, aes(x = chr10_total_expr, y = chr7_total_expr)) +
geom_point(aes(color = is_tumor), size = 0.5) +
geom_hline(yintercept = 0.8) +
geom_vline(xintercept = 2.5) 
A simple gating strategy to find tumor cells
tibble(umap = umap) |>
mutate(is_tumor = tumor_label_df$is_tumor) |>
ggplot(aes(x = umap[,1], y = umap[,2])) +
geom_point(aes(color = is_tumor), size = 0.5) +
facet_wrap(vars(is_tumor)) + coord_fixed()
The tumor cells are enriched in parts of the big (left) blob.
We use this cell annotation to focus our neighborhood finding within the tumor cells, to find tumor subpopulations.
tumor_fit <- fit[, tumor_label_df$is_tumor]
tum_nei <- find_de_neighborhoods(tumor_fit, group_by = vars(patient_id, condition), verbose = FALSE)
as_tibble(tum_nei) |>
left_join(as_tibble(rowData(fit)[,1:2]), by = c("name" = "gene_id")) |>
dplyr::relocate(symbol, .before = "name") |>
filter(adj_pval < 0.1) |>
arrange(did_pval) |>
dplyr::select(symbol, name, neighborhood, n_cells, adj_pval, lfc, did_pval, did_lfc) |>
print(n = 10)
#> # A tibble: 39 × 8
#> symbol name neighborhood n_cells adj_pval lfc did_pval did_lfc
#> <chr> <chr> <I<list>> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 CCL3 ENSG00000277632 <chr [1,795]> 1795 0.0382 -2.79 0.00777 2.65
#> 2 CALM1 ENSG00000198668 <chr [2,077]> 2077 0.0160 1.02 0.00943 -0.731
#> 3 NAMPT ENSG00000105835 <chr [1,885]> 1885 0.0382 -1.14 0.0706 1.03
#> 4 POLR2L ENSG00000177700 <chr [2,428]> 2428 0.00922 1.23 0.0787 -0.557
#> 5 A2M ENSG00000175899 <chr [2,361]> 2361 0.0790 -1.97 0.0941 1.28
#> 6 CXCL8 ENSG00000169429 <chr [1,756]> 1756 0.0264 1.20 0.193 -0.594
#> 7 TUBA1A ENSG00000167552 <chr [2,761]> 2761 0.0782 -0.462 0.225 -0.212
#> 8 MT1X ENSG00000187193 <chr [1,972]> 1972 0.00266 3.20 0.250 -0.674
#> 9 RPS11 ENSG00000142534 <chr [2,238]> 2238 0.0959 -0.383 0.256 -0.173
#> 10 HMGB1 ENSG00000189403 <chr [2,535]> 2535 0.0382 -0.833 0.261 0.374
#> # ℹ 29 more rowsFocusing on RPS11, we see that panobinostat mostly has no effect on its
expression, except for a subpopulation of tumor cells where RPS11 was
originally upregulated and panobinostat downregulates the expression. A
small caveat: this analysis is conducted on a subset of all cells and
should be interpreted carefully. Yet, this section demonstrates how
lemur can be used to find tumor subpopulations which show differential
responses to treatments.
sel_gene <- "ENSG00000142534" # is RPS11
as_tibble(colData(fit)) |>
mutate(expr = assay(fit, "logcounts")[sel_gene,]) |>
mutate(is_tumor = tumor_label_df$is_tumor) |>
mutate(in_neighborhood = id %in% filter(tum_nei, name == sel_gene)$neighborhood[[1]]) |>
ggplot(aes(x = condition, y = expr)) +
geom_jitter(size = 0.3, stroke = 0) +
geom_point(data = . %>% summarize(expr = mean(expr), .by = c(condition, patient_id, is_tumor, in_neighborhood)),
aes(color = patient_id), size = 2) +
stat_summary(fun.data = mean_se, geom = "crossbar", color = "red") +
facet_wrap(vars(is_tumor, in_neighborhood), labeller = label_both) 
I have already integrated my data using Harmony / MNN / Seurat. Can I call lemur directly with the aligned data?
No. You need to call lemur with the unaligned data so that it can
learn how much the expression of each gene changes between conditions.
Can I call lemur with sctransformed instead of log-transformed data?
Yes. You can call lemur with any variance stabilized count matrix. Based on a previous project, I recommend to use log-transformation, but other methods will work just fine.
This is a known issue and can be caused if the data has large
compositional shifts (for example, if one cell type disappears). The
problem is that the initial linear regression step, which centers the
conditions relative to each other, overcorrects and introduces a
consistent shift in the latent space. You can either use
align_by_grouping / align_harmony to correct for this effect or
manually fix the regression coefficient to zero:
fit <- lemur(sce, design = ~ patient_id + condition, n_embedding = 15, linear_coefficient_estimator = "zero")The conditions still separate if I plot the data using UMAP / tSNE. Even after calling align_harmony / align_neighbors. What should I do?
You can try to increase n_embedding. If this still does not help,
there is little use in inferring differential expression neighborhoods.
But as I haven’t encountered such a dataset yet, I would like to try it
out myself. If you can share the data publicly, please open an issue.
Several parameters influence the duration to fit the LEMUR model and find differentially expressed neighborhoods:
- Make sure that your data is stored in memory (not a
DelayedArray) either as a sparse dgCMatrix or dense matrix. - A larger
test_fractionmeans fewer cells are used to fit the model (and more cells are used for the DE test), which speeds up many steps. - A smaller
n_embeddingreduces the latent dimensions of the fit, which makes the model less flexible, but speeds up thelemur()call. - Providing a pre-calculated set of matching cells and calling
align_groupingis faster thanalign_harmony. - Setting
selection_procedure = "contrast"infind_de_neighborhoodsoften produces better neighborhoods, but is a lot slower thanselection_procedure = "zscore". - Setting
size_factor_method = "ratio"infind_de_neighborhoodsmakes the DE more powerful, but is a lot slower thansize_factor_method = "normed_sum".
sessionInfo()
#> R version 4.4.1 (2024-06-14)
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#> Running under: macOS Sonoma 14.6.1
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#> time zone: Europe/Berlin
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#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 Biobase_2.64.0
#> [4] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1 IRanges_2.38.1
#> [7] S4Vectors_0.42.1 BiocGenerics_0.50.0 MatrixGenerics_1.16.0
#> [10] matrixStats_1.3.0 lubridate_1.9.3 forcats_1.0.0
#> [13] stringr_1.5.1 dplyr_1.1.4 purrr_1.0.2
#> [16] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
#> [19] ggplot2_3.5.1 tidyverse_2.0.0 lemur_1.2.0
#>
#> loaded via a namespace (and not attached):
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#> [16] compiler_4.4.1 munsell_0.5.1 RhpcBLASctl_0.23-42
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#> [25] MASS_7.3-61 uwot_0.2.2 DelayedArray_0.30.1
#> [28] abind_1.4-5 tidyselect_1.2.1 digest_0.6.37
#> [31] stringi_1.8.4 splines_4.4.1 labeling_0.4.3
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