CiteFuse
is a computational framework that implements a
suite of methods and tools for CITE-seq data from pre-processing through
to integrative analytics. This includes doublet detection, network-based
modality integration, cell type clustering, differential RNA and protein
expression analysis, ADT evaluation, ligand-receptor interaction
analysis, and interactive web-based visualisation of the analyses. This
vignette demonstrates the usage of CiteFuse
on a subset
data of CITE-seq data from human PBMCs as an example (Mimitou et al.,
2019).
First, install CiteFuse
using
BiocManager
.
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("CiteFuse")
data("CITEseq_example", package = "CiteFuse")
names(CITEseq_example)
#> [1] "RNA" "ADT" "HTO"
lapply(CITEseq_example, dim)
#> $RNA
#> [1] 19521 500
#>
#> $ADT
#> [1] 49 500
#>
#> $HTO
#> [1] 4 500
Here, we start from a list of three matrices of unique molecular
identifier (UMI), antibody derived tags (ADT) and hashtag
oligonucleotide (HTO) count, which have common cell names. There are 500
cells in our subsetted dataset. And characteristically of CITE-seq data,
the matrices are matched, meaning that for any given cell we know the
expression level of their RNA transcripts (genome-wide) and its
corresponding cell surface protein expression. The
preprocessing
function will utilise the three matrices and
its common cell names to create a SingleCellExperiment
object, which stores RNA data in an assay
and
ADT
and HTO
data within in the
altExp
slot.
sce_citeseq <- preprocessing(CITEseq_example)
sce_citeseq
#> class: SingleCellExperiment
#> dim: 19521 500
#> metadata(0):
#> assays(1): counts
#> rownames(19521): hg19_AL627309.1 hg19_AL669831.5 ... hg19_MT-ND6
#> hg19_MT-CYB
#> rowData names(0):
#> colnames(500): AAGCCGCGTTGTCTTT GATCGCGGTTATCGGT ... TTGGCAACACTAGTAC
#> GCTGCGAGTTGTGGCC
#> colData names(0):
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(2): ADT HTO
CiteFuse
The function normaliseExprs
is used to scale the
alternative expression. Here, we used it to perform log-transformation
of the HTO
count, by setting
transform = "log"
.
Then we can perform dimension reduction on the HTO
count
by using runTSNE
or runUMAP
, then use
visualiseDim
function to visualise the reduced dimension
plot. Our CITE-seq dataset contain data from four samples that were
pooled before sequencing. The samples were multiplexed through cell
hashing (Stoekius et al., 2018). The four clusters observed on reduced
dimension plots equate to the four different samples.
An important step in single cell data analysis is the removal of
doublets. Doublets form as a result of co-encapsulation of cells within
a droplet, leading to a hybrid transcriptome from two or more cells. In
CiteFuse, we implement a step-wise doublet detection approach to remove
doublets. We first identify the cross-sample doublets via the
crossSampleDoublets
function.
sce_citeseq <- crossSampleDoublets(sce_citeseq)
#> number of iterations= 20
#> number of iterations= 24
#> number of iterations= 46
#> number of iterations= 50
The results of the cross sample doublets are then saved in
colData
as doubletClassify_between_label
and
doubletClassify_between_class
.
table(sce_citeseq$doubletClassify_between_label)
#>
#> 1 2 3 4
#> 115 121 92 129
#> doublet/multiplet
#> 43
table(sce_citeseq$doubletClassify_between_class)
#>
#> Singlet doublet/multiplet
#> 457 43
We can then highlight the cross-sample doublets in our tSNE plot of HTO count.
Furthermore, plotHTO
function allows us to plot the
pairwise scatter HTO count. Any cells that show co-expression of
orthologocal HTOs (red) are considered as doublets.
We then identify the within-sample doublets via the
withinSampleDoublets
function.
The results of the cross sample doublets are then saved in the
colData
as doubletClassify_within_label
and
doubletClassify_within_class
.
table(sce_citeseq$doubletClassify_within_label)
#>
#> Doublets(Within)_1 Doublets(Within)_2 Doublets(Within)_3 Doublets(Within)_4
#> 3 7 4 6
#> NotDoublets(Within)
#> 480
table(sce_citeseq$doubletClassify_within_class)
#>
#> Doublet Singlet
#> 20 480
Again, we can visualise the within-sample doublets in our tSNE plot.
Finally, we can filter out the doublet cells (both within and between batches) for the downstream analysis.
sce_citeseq <- sce_citeseq[, sce_citeseq$doubletClassify_within_class == "Singlet" & sce_citeseq$doubletClassify_between_class == "Singlet"]
sce_citeseq
#> class: SingleCellExperiment
#> dim: 19521 437
#> metadata(3): doubletClassify_between_threshold
#> doubletClassify_between_resultsMat doubletClassify_within_resultsMat
#> assays(1): counts
#> rownames(19521): hg19_AL627309.1 hg19_AL669831.5 ... hg19_MT-ND6
#> hg19_MT-CYB
#> rowData names(0):
#> colnames(437): GATCGCGGTTATCGGT GGCTGGTAGAGGTTAT ... TTGGCAACACTAGTAC
#> GCTGCGAGTTGTGGCC
#> colData names(5): doubletClassify_between_label
#> doubletClassify_between_class nUMI doubletClassify_within_label
#> doubletClassify_within_class
#> reducedDimNames(2): TSNE_HTO UMAP_HTO
#> mainExpName: NULL
#> altExpNames(2): ADT HTO
The first step of analysis is to integrate the RNA and ADT matrix. We use a popular integration algorithm called similarity network fusion (SNF) to integrate the multiomic data.
sce_citeseq <- scater::logNormCounts(sce_citeseq)
sce_citeseq <- normaliseExprs(sce_citeseq, altExp_name = "ADT", transform = "log")
system.time(sce_citeseq <- CiteFuse(sce_citeseq))
#> Calculating affinity matrix
#> Performing SNF
#> user system elapsed
#> 1.847 0.921 1.494
We now proceed with the fused matrix, which is stored as
SNF_W
in our sce_citeseq
object.
CiteFuse implements two different clustering algorithms on the fused
matrix, spectral clustering and Louvain clustering. First, we perform
spectral clustering with sufficient numbers of K
and use
the eigen values to determine the optimal number of clusters.
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 20)
#> Computing Spectral Clustering
plot(SNF_W_clust$eigen_values)
Using the optimal cluster number defined from the previous step, we
can now use the spectralClutering
function to cluster the
single cells by specifying the number of clusters in K
. The
function takes a cell-to-cell similarity matrix as an input. We have
already created the fused similarity matrix from CiteFuse
.
Since the CiteFuse
function creates and stores the
similarity matries from ADT and RNA expression, as well the fused
matrix, we can use these two to compare the clustering outcomes by data
modality.
SNF_W_clust <- spectralClustering(metadata(sce_citeseq)[["SNF_W"]], K = 5)
#> Computing Spectral Clustering
sce_citeseq$SNF_W_clust <- as.factor(SNF_W_clust$labels)
SNF_W1_clust <- spectralClustering(metadata(sce_citeseq)[["ADT_W"]], K = 5)
#> Computing Spectral Clustering
sce_citeseq$ADT_clust <- as.factor(SNF_W1_clust$labels)
SNF_W2_clust <- spectralClustering(metadata(sce_citeseq)[["RNA_W"]], K = 5)
#> Computing Spectral Clustering
sce_citeseq$RNA_clust <- as.factor(SNF_W2_clust$labels)
The outcome of the clustering can be easily visualised on a reduced dimensions plot by highlighting the points by cluster label.
sce_citeseq <- reducedDimSNF(sce_citeseq,
method = "tSNE",
dimNames = "tSNE_joint")
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_clust") +
labs(title = "tSNE (SNF clustering)")
g2 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "ADT_clust") +
labs(title = "tSNE (ADT clustering)")
g3 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "RNA_clust") +
labs(title = "tSNE (RNA clustering)")
library(gridExtra)
grid.arrange(g3, g2, g1, ncol = 2)
The expression of genes and proteins can be visualised by changing
the colour_by
parameter to assess the clusters. As an
example, we highlight the plot by the RNA and ADT expression level of
CD8.
g1 <- visualiseDim(sce_citeseq, dimNames = "tSNE_joint",
colour_by = "hg19_CD8A",
data_from = "assay",
assay_name = "logcounts") +
labs(title = "tSNE: hg19_CD8A (RNA expression)")
g2 <- visualiseDim(sce_citeseq,dimNames = "tSNE_joint",
colour_by = "CD8",
data_from = "altExp",
altExp_assay_name = "logcounts") +
labs(title = "tSNE: CD8 (ADT expression)")
grid.arrange(g1, g2, ncol = 2)
As well as spectral clustering, CiteFuse can implement Louvain
clustering if users wish to use another clustering method. We use the
igraph
package, and any community detection algorithms
available in their package can be selected by changing the
method
parameter.
SNF_W_louvain <- igraphClustering(sce_citeseq, method = "louvain")
table(SNF_W_louvain)
#> SNF_W_louvain
#> 1 2 3 4 5 6 7
#> 33 139 62 87 51 29 36
sce_citeseq$SNF_W_louvain <- as.factor(SNF_W_louvain)
visualiseDim(sce_citeseq, dimNames = "tSNE_joint", colour_by = "SNF_W_louvain") +
labs(title = "tSNE (SNF louvain clustering)")
CiteFuse has a wide range of visualisation tools to facilitate
exploratory analysis of CITE-seq data. The visualiseExprs
function is an easy-to-use function to generate boxplots, violinplots,
jitter plots, density plots, and pairwise scatter/density plots of genes
and proteins expressed in the data. The plots can be grouped by using
the cluster labels stored in the sce_citeseq
object.
visualiseExprs(sce_citeseq,
plot = "boxplot",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "violin",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("hg19_CD2", "hg19_CD4", "hg19_CD8A", "hg19_CD19"))
visualiseExprs(sce_citeseq,
altExp_name = "ADT",
group_by = "SNF_W_louvain",
plot = "violin", n = 5)
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "jitter",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
visualiseExprs(sce_citeseq, altExp_name = "ADT",
plot = "density",
group_by = "SNF_W_louvain",
feature_subset = c("CD2", "CD8", "CD4", "CD19"))
CiteFuse also calculates differentially expressed (DE) genes through
the DEgenes
function. The cluster grouping to use must be
specified in the group
parameter. If
altExp_name
is not specified, RNA expression will be used
as the default expression matrix.
Results form the DE analysis is stored in sce_citeseq
as
DE_res_RNA_filter
and DE_res_ADT_filter
for
RNA and ADT expression, respectively.
# DE will be performed for RNA if altExp_name = "none"
sce_citeseq <- DEgenes(sce_citeseq,
altExp_name = "none",
group = sce_citeseq$SNF_W_louvain,
return_all = TRUE,
exprs_pct = 0.5)
sce_citeseq <- selectDEgenes(sce_citeseq,
altExp_name = "none")
datatable(format(do.call(rbind, metadata(sce_citeseq)[["DE_res_RNA_filter"]]),
digits = 2))
The DE genes can be visualised with the DEbubblePlot
and
DEcomparisonPlot
. In each case, the gene names must first
be extracted from the DE result objects.
The circlepackPlot
takes a list of all DE genes from RNA
and ADT DE analysis and will plot only the top most significant DE genes
to plot.
For the DEcomparisonPlot
, as well as a list containing
the DE genes for RNA and ADT, a feature_list
specifying the
genes and proteins of interest is required.
rna_list <- c("hg19_CD4",
"hg19_CD8A",
"hg19_HLA-DRB1",
"hg19_ITGAX",
"hg19_NCAM1",
"hg19_CD27",
"hg19_CD19")
adt_list <- c("CD4", "CD8", "MHCII (HLA-DR)", "CD11c", "CD56", "CD27", "CD19")
rna_DEgenes_all <- metadata(sce_citeseq)[["DE_res_RNA"]]
adt_DEgenes_all <- metadata(sce_citeseq)[["DE_res_ADT"]]
feature_list <- list(RNA = rna_list, ADT = adt_list)
de_list <- list(RNA = rna_DEgenes_all, ADT = adt_DEgenes_all)
DEcomparisonPlot(de_list = de_list,
feature_list = feature_list)
An important evaluation in CITE-seq data analysis is to assess the quality of each ADT and to evaluate the contribution of ADTs towards clustering outcome. CiteFuse calculates the relative importance of ADT towards clustering outcome by using a random forest model. The higher the score of an ADT, the greater its importance towards the final clustering outcome.
set.seed(2020)
sce_citeseq <- importanceADT(sce_citeseq,
group = sce_citeseq$SNF_W_louvain,
subsample = TRUE)
visImportance(sce_citeseq, plot = "boxplot")
sort(metadata(sce_citeseq)[["importanceADT"]], decreasing = TRUE)[1:20]
#> CD8 CD4 CD27 PECAM (CD31)
#> 35.221426 32.633863 29.238062 13.528691
#> CD7 CD62L CD5 CD28
#> 12.725412 11.066246 10.795823 10.643114
#> CD11b IL7Ralpha (CD127) MHCII (HLA-DR) CD44
#> 10.544490 10.207220 9.157082 8.522167
#> CD2 CD366 (tim3) HLA-A,B,C CD11c
#> 8.135853 6.232948 6.155352 5.537362
#> CD45RA CD3 PD-1 (CD279) CD19
#> 5.445998 4.904109 3.750849 3.679097
The importance scores can be visualised in a boxplot and heatmap. Our evaluation of ADT importance show that unsurprisingly CD4 and CD8 are the top two discriminating proteins in PBMCs.
Let us try clustering with only ADTs with a score greater than 5.
subset_adt <- names(which(metadata(sce_citeseq)[["importanceADT"]] > 5))
subset_adt
#> [1] "CD11b" "CD11c" "CD2"
#> [4] "CD27" "CD28" "CD366 (tim3)"
#> [7] "CD4" "CD44" "CD45RA"
#> [10] "CD5" "CD62L" "CD7"
#> [13] "CD8" "HLA-A,B,C" "IL7Ralpha (CD127)"
#> [16] "MHCII (HLA-DR)" "PECAM (CD31)"
system.time(sce_citeseq <- CiteFuse(sce_citeseq,
ADT_subset = subset_adt,
metadata_names = c("W_SNF_adtSubset1",
"W_ADT_adtSubset1",
"W_RNA")))
#> Calculating affinity matrix
#> Performing SNF
#> user system elapsed
#> 1.163 0.327 0.870
SNF_W_clust_adtSubset1 <- spectralClustering(metadata(sce_citeseq)[["W_SNF_adtSubset1"]], K = 5)
#> Computing Spectral Clustering
sce_citeseq$SNF_W_clust_adtSubset1 <- as.factor(SNF_W_clust_adtSubset1$labels)
library(mclust)
adjustedRandIndex(sce_citeseq$SNF_W_clust_adtSubset1, sce_citeseq$SNF_W_clust)
#> [1] 0.8729838
When we compare between the two clustering outcomes, we find that the adjusted rand index is approximately 0.93, where a value of 1 denotes complete concordance.
The geneADTnetwork
function plots an interaction network
between genes identified from the DE analysis. The nodes denote proteins
and RNA whilst the edges denote positive and negative correlation in
expression.
RNA_feature_subset <- unique(as.character(unlist(lapply(rna_DEgenes_all, "[[", "name"))))
ADT_feature_subset <- unique(as.character(unlist(lapply(adt_DEgenes_all, "[[", "name"))))
geneADTnetwork(sce_citeseq,
RNA_feature_subset = RNA_feature_subset,
ADT_feature_subset = ADT_feature_subset,
cor_method = "pearson",
network_layout = igraph::layout_with_fr)
#> IGRAPH 04dcb4c UN-B 72 134 --
#> + attr: name (v/c), label (v/c), class (v/c), type (v/l), shape (v/c),
#> | color (v/c), size (v/n), label.cex (v/n), label.color (v/c), value
#> | (e/n), color (e/c), weights (e/n)
#> + edges from 04dcb4c (vertex names):
#> [1] RNA_hg19_TCF7 --ADT_CD27 RNA_hg19_RPL32 --ADT_CD27
#> [3] RNA_hg19_RPS8 --ADT_CD27 RNA_hg19_LTB --ADT_CD27
#> [5] RNA_hg19_RPL13 --ADT_CD27 RNA_hg19_RPL11 --ADT_CD27
#> [7] RNA_hg19_CD27 --ADT_CD27 RNA_hg19_RPS12 --ADT_CD27
#> [9] RNA_hg19_RPL37 --ADT_CD27 RNA_hg19_IL7R --ADT_CD27
#> [11] RNA_hg19_CCL5 --ADT_CD27 RNA_hg19_GZMH --ADT_CD27
#> + ... omitted several edges
With the advent of CITE-seq, we can now predict ligand-receptor
interactions by using cell surface protein expression. CiteFuse
implements a ligandReceptorTest
to find ligand receptor
interactions between sender and receiver cells. Importantly, the ADT
count is used to predict receptor expression within receiver cells. Note
that the setting altExp_name = "RNA"
would enable users to
predict ligand-receptor interaction from RNA expression only.
data("lr_pair_subset", package = "CiteFuse")
head(lr_pair_subset)
#> [,1] [,2]
#> [1,] "hg19_IL17RA" "CD45"
#> [2,] "hg19_FAS" "CD11b"
#> [3,] "hg19_GZMK" "CD62L"
#> [4,] "hg19_CD40LG" "CD11b"
#> [5,] "hg19_FLT3LG" "CD62L"
#> [6,] "hg19_GZMA" "CD19"
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "ADT",
transform = "zi_minMax")
sce_citeseq <- normaliseExprs(sce = sce_citeseq,
altExp_name = "none",
exprs_value = "logcounts",
transform = "minMax")
sce_citeseq <- ligandReceptorTest(sce = sce_citeseq,
ligandReceptor_list = lr_pair_subset,
cluster = sce_citeseq$SNF_W_louvain,
RNA_exprs_value = "minMax",
use_alt_exp = TRUE,
altExp_name = "ADT",
altExp_exprs_value = "zi_minMax",
num_permute = 1000)
#> 100 ......200 ......300 ......400 ......500 ......600 ......700 ......800 ......900 ......1000 ......
Lastly, we will jointly analyse the current PBMC CITE-seq data, taken
from healthy human donors, and another subset of CITE-seq data from
patients with cutaneous T-cell lymphoma (CTCL), again from Mimitou et
al. (2019). The data sce_ctcl_subset
provided in our
CiteFuse
package already contains the clustering
information.
To visualise and compare gene or protein expression data, we can use
visualiseExprsList
function.
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "none",
exprs_value = "logcounts",
feature_subset = c("hg19_S100A10", "hg19_CD8A"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
visualiseExprsList(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
plot = "boxplot",
altExp_name = "ADT",
feature_subset = c("CD19", "CD8"),
group_by = c("SNF_W_louvain", "SNF_W_louvain"))
We can then perform differential expression analysis of the RNA expression level across the two clusters that have high CD19 expression in ADT.
de_res <- DEgenesCross(sce_list = list(control = sce_citeseq,
ctcl = sce_ctcl_subset),
colData_name = c("SNF_W_louvain", "SNF_W_louvain"),
group_to_test = c("2", "6"))
de_res_filter <- selectDEgenes(de_res = de_res)
de_res_filter
#> $control
#> stats.W pval p.adjust meanExprs.1 meanExprs.2
#> hg19_GNLY 28.0 5.642738e-23 5.642738e-23 0.07220401 4.585384
#> hg19_CST7 152.5 3.913672e-21 3.913672e-21 0.22512314 2.630719
#> hg19_NKG7 168.0 7.860013e-21 7.860013e-21 0.51066894 3.812262
#> hg19_GZMH 301.0 1.567852e-19 1.567852e-19 0.00000000 1.967411
#> hg19_GZMB 498.0 5.422652e-17 5.422652e-17 0.11667246 2.006243
#> hg19_CCL5 477.0 8.039376e-17 8.039376e-17 1.04348029 3.777290
#> hg19_FGFBP2 571.0 2.154067e-16 2.154067e-16 0.03101677 1.651125
#> hg19_KLRD1 598.0 6.180246e-16 6.180246e-16 0.09327555 1.564246
#> hg19_EFHD2 794.0 3.699717e-14 3.699717e-14 0.02064028 1.210523
#> hg19_PRF1 803.5 6.796491e-14 6.796491e-14 0.06473009 1.461993
#> meanPct.1 meanPct.2 meanDiff pctDiff name group
#> hg19_GNLY 0.02325581 0.9928058 4.513180 0.9695499 hg19_GNLY control
#> hg19_CST7 0.13953488 0.9928058 2.405596 0.8532709 hg19_CST7 control
#> hg19_NKG7 0.30232558 1.0000000 3.301593 0.6976744 hg19_NKG7 control
#> hg19_GZMH 0.00000000 0.8992806 1.967411 0.8992806 hg19_GZMH control
#> hg19_GZMB 0.06976744 0.8776978 1.889570 0.8079304 hg19_GZMB control
#> hg19_CCL5 0.37209302 1.0000000 2.733809 0.6279070 hg19_CCL5 control
#> hg19_FGFBP2 0.02325581 0.8201439 1.620108 0.7968881 hg19_FGFBP2 control
#> hg19_KLRD1 0.09302326 0.8273381 1.470971 0.7343149 hg19_KLRD1 control
#> hg19_EFHD2 0.02325581 0.7410072 1.189883 0.7177514 hg19_EFHD2 control
#> hg19_PRF1 0.04651163 0.7625899 1.397263 0.7160783 hg19_PRF1 control
#>
#> $ctcl
#> stats.W pval p.adjust meanExprs.1 meanExprs.2
#> hg19_LTB 372.0 3.139096e-28 3.139096e-28 0.10343246 1.978730
#> hg19_RPS26 0.0 4.172127e-23 4.172127e-23 1.64098856 5.072471
#> hg19_IL7R 780.0 1.412936e-20 1.412936e-20 0.17329085 1.651591
#> hg19_SELL 1033.5 8.045007e-20 8.045007e-20 0.07322948 1.152572
#> hg19_LEPROTL1 715.0 2.219774e-18 2.219774e-18 0.24294829 1.300745
#> hg19_EEF1B2 523.5 3.123434e-16 3.123434e-16 1.71942732 3.043623
#> hg19_HBB 1737.5 1.285550e-15 1.285550e-15 0.00000000 0.431026
#> hg19_NOSIP 1044.0 1.332385e-14 1.332385e-14 0.19852399 1.157235
#> hg19_FOS 1324.0 3.386001e-13 3.386001e-13 0.16536118 1.125337
#> hg19_NPM1 945.0 1.042899e-11 1.042899e-11 1.17875475 2.202268
#> meanPct.1 meanPct.2 meanDiff pctDiff name group
#> hg19_LTB 0.07913669 0.9069767 1.8752977 0.8278401 hg19_LTB ctcl
#> hg19_RPS26 0.89208633 1.0000000 3.4314820 0.1079137 hg19_RPS26 ctcl
#> hg19_IL7R 0.10791367 0.8139535 1.4782997 0.7060398 hg19_IL7R ctcl
#> hg19_SELL 0.05755396 0.6976744 1.0793420 0.6401205 hg19_SELL ctcl
#> hg19_LEPROTL1 0.19424460 0.9069767 1.0577972 0.7127321 hg19_LEPROTL1 ctcl
#> hg19_EEF1B2 0.86330935 0.9767442 1.3241960 0.1134348 hg19_EEF1B2 ctcl
#> hg19_HBB 0.00000000 0.4186047 0.4310260 0.4186047 hg19_HBB ctcl
#> hg19_NOSIP 0.19424460 0.7674419 0.9587114 0.5731973 hg19_NOSIP ctcl
#> hg19_FOS 0.12230216 0.6511628 0.9599762 0.5288606 hg19_FOS ctcl
#> hg19_NPM1 0.73381295 0.9534884 1.0235132 0.2196754 hg19_NPM1 ctcl
Readers unfamiliar with the workflow of converting a count matrix
into a SingleCellExperiment
object may use the
readFrom10X
function to convert count matrix from a 10X
experiment into an object that can be used for all functions in
CiteFuse.
tmpdir <- tempdir()
download.file("http://cf.10xgenomics.com/samples/cell-exp/3.1.0/connect_5k_pbmc_NGSC3_ch1/connect_5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz", file.path(tmpdir, "/5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"))
untar(file.path(tmpdir, "5k_pbmc_NGSC3_ch1_filtered_feature_bc_matrix.tar.gz"),
exdir = tmpdir)
sce_citeseq_10X <- readFrom10X(file.path(tmpdir, "filtered_feature_bc_matrix/"))
sce_citeseq_10X
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] mclust_6.1.1 gridExtra_2.3
#> [3] DT_0.33 scater_1.35.0
#> [5] ggplot2_3.5.1 scuttle_1.17.0
#> [7] SingleCellExperiment_1.29.1 SummarizedExperiment_1.37.0
#> [9] Biobase_2.67.0 GenomicRanges_1.59.1
#> [11] GenomeInfoDb_1.43.2 IRanges_2.41.2
#> [13] S4Vectors_0.45.2 BiocGenerics_0.53.3
#> [15] generics_0.1.3 MatrixGenerics_1.19.0
#> [17] matrixStats_1.4.1 CiteFuse_1.19.0
#> [19] BiocStyle_2.35.0
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 sys_3.4.3 tensorA_0.36.2.1
#> [4] jsonlite_1.8.9 magrittr_2.0.3 ggbeeswarm_0.7.2
#> [7] farver_2.1.2 rmarkdown_2.29 zlibbioc_1.52.0
#> [10] vctrs_0.6.5 memoise_2.0.1 htmltools_0.5.8.1
#> [13] S4Arrays_1.7.1 BiocNeighbors_2.1.2 Rhdf5lib_1.29.0
#> [16] SparseArray_1.7.2 rhdf5_2.51.1 sass_0.4.9
#> [19] bslib_0.8.0 htmlwidgets_1.6.4 plyr_1.8.9
#> [22] plotly_4.10.4 cachem_1.1.0 buildtools_1.0.0
#> [25] igraph_2.1.2 lifecycle_1.0.4 pkgconfig_2.0.3
#> [28] rsvd_1.0.5 Matrix_1.7-1 R6_2.5.1
#> [31] fastmap_1.2.0 GenomeInfoDbData_1.2.13 digest_0.6.37
#> [34] colorspace_2.1-1 dqrng_0.4.1 irlba_2.3.5.1
#> [37] crosstalk_1.2.1 beachmat_2.23.4 labeling_0.4.3
#> [40] randomForest_4.7-1.2 fansi_1.0.6 httr_1.4.7
#> [43] polyclip_1.10-7 abind_1.4-8 compiler_4.4.2
#> [46] withr_3.0.2 BiocParallel_1.41.0 viridis_0.6.5
#> [49] ggforce_0.4.2 MASS_7.3-61 bayesm_3.1-6
#> [52] DelayedArray_0.33.3 bluster_1.17.0 tools_4.4.2
#> [55] vipor_0.4.7 beeswarm_0.4.0 glue_1.8.0
#> [58] dbscan_1.2-0 nlme_3.1-166 rhdf5filters_1.19.0
#> [61] grid_4.4.2 Rtsne_0.17 cluster_2.1.8
#> [64] reshape2_1.4.4 gtable_0.3.6 tidyr_1.3.1
#> [67] data.table_1.16.4 BiocSingular_1.23.0 tidygraph_1.3.1
#> [70] ScaledMatrix_1.15.0 metapod_1.15.0 utf8_1.2.4
#> [73] XVector_0.47.0 ggrepel_0.9.6 pillar_1.9.0
#> [76] stringr_1.5.1 limma_3.63.2 robustbase_0.99-4-1
#> [79] splines_4.4.2 dplyr_1.1.4 tweenr_2.0.3
#> [82] lattice_0.22-6 FNN_1.1.4.1 survival_3.7-0
#> [85] compositions_2.0-8 tidyselect_1.2.1 locfit_1.5-9.10
#> [88] maketools_1.3.1 knitr_1.49 edgeR_4.5.1
#> [91] xfun_0.49 graphlayouts_1.2.1 mixtools_2.0.0
#> [94] statmod_1.5.0 DEoptimR_1.1-3-1 pheatmap_1.0.12
#> [97] stringi_1.8.4 UCSC.utils_1.3.0 lazyeval_0.2.2
#> [100] yaml_2.3.10 evaluate_1.0.1 codetools_0.2-20
#> [103] kernlab_0.9-33 ggraph_2.2.1 tibble_3.2.1
#> [106] BiocManager_1.30.25 cli_3.6.3 uwot_0.2.2
#> [109] segmented_2.1-3 munsell_0.5.1 jquerylib_0.1.4
#> [112] Rcpp_1.0.13-1 parallel_4.4.2 scran_1.35.0
#> [115] viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6
#> [118] purrr_1.0.2 crayon_1.5.3 rlang_1.1.4
#> [121] cowplot_1.1.3