Kevin Blighe 2021-08-07
In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz (Blighe 2020) attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment (Lun and Risso 2020), it has a ‘plug and play’ feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot (Wickham 2016) engine, which means that users can ‘add on’ features to these with ease.
This package just provides some additional functions for dataviz and clustering, and provides another way of identifying cell-types in clusters. It is not strictly intended as a standalone analysis package. For a comprehensive high-dimensional cytometry workflow, it is recommended to check out the work by Nowicka et al. CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. For a more comprehensive scRNA-seq workflow, please check out OSCA and Analysis of single cell RNA-seq data.
if (!requireNamespace('BiocManager', quietly = TRUE))
install.packages('BiocManager')
BiocManager::install('scDataviz')Note: to install development version:
devtools::install_github('kevinblighe/scDataviz') library(scDataviz)Here, we will utilise some of the flow cytometry data from Deep phenotyping detects a pathological CD4+ T-cell complosome signature in systemic sclerosis.
This can normally be downloadedd via git clone from your command
prompt:
git clone https://github.com/kevinblighe/scDataviz_data/ ;In a practical situation, we would normally read in this data from the
raw FCS files and then QC filter, normalise, and transform them. This
can be achieved via the processFCS function, which, by default, also
removes variables based on low variance and downsamples [randomly]
your data to 100000 variables. The user can change these via the
downsample and downsampleVar parameters. An example (not run) is
given below:
filelist <- list.files(
path = "scDataviz_data/FCS/",
pattern = "*.fcs|*.FCS",
full.names = TRUE)
filelist
metadata <- data.frame(
sample = gsub('\\ [A-Za-z0-9]*\\.fcs$', '',
gsub('scDataviz_data\\/FCS\\/\\/', '', filelist)),
group = c(rep('Healthy', 7), rep('Disease', 11)),
treatment = gsub('\\.fcs$', '',
gsub('scDataviz_data\\/FCS\\/\\/[A-Z0-9]*\\ ', '', filelist)),
row.names = filelist,
stringsAsFactors = FALSE)
metadata
inclusions <- c('Yb171Di','Nd144Di','Nd145Di',
'Er168Di','Tm169Di','Sm154Di','Yb173Di','Yb174Di',
'Lu175Di','Nd143Di')
markernames <- c('Foxp3','C3aR','CD4',
'CD46','CD25','CD3','Granzyme B','CD55',
'CD279','CD45RA')
names(markernames) <- inclusions
markernames
exclusions <- c('Time','Event_length','BCKG190Di',
'Center','Offset','Width','Residual')
sce <- processFCS(
files = filelist,
metadata = metadata,
transformation = TRUE,
transFun = function (x) asinh(x),
asinhFactor = 5,
downsample = 10000,
downsampleVar = 0.7,
colsRetain = inclusions,
colsDiscard = exclusions,
newColnames = markernames)In flow and mass cytometry, getting the correct marker names in the FCS files can be surprisingly difficult. In many cases, from experience, a facility may label the markers by their metals, such as Iridium (Ir), Ruthenium (Ru), Terbium (Tb), et cetera - this is the case for the data used in this tutorial. The true marker names may be held as pData encoded within each FCS, accessible via:
library(flowCore)
pData(parameters(
read.FCS(filelist[[4]], transformation = FALSE, emptyValue = FALSE)))Whatever the case, it is important to sort out marker naming issues prior to the experiment being conducted in order to avoid any confusion.
For this vignette, due to the fact that the raw FCS data is > 500 megabytes, we will work with a smaller pre-prepared dataset that has been downsampled to 10000 cells using the above code. This data comes included with the package.
Load the pre-prepared complosome data.
load(system.file('extdata/', 'complosome.rdata', package = 'scDataviz'))One can also create a new SingleCellExperiment object manually using any type of data, including any data from scRNA-seq produced elsewhere. Import functions for data deriving from other sources is covered in Tutorials 2 and 3 in this vignette. All functions in scDataviz additionally accept data-frames or matrices on their own, de-necessitating the reliance on the SingleCellExperiment class.
We can use the PCAtools (Blighe and Lun 2020) package for the purpose of performing PCA.
library(PCAtools)
p <- pca(assay(sce, 'scaled'), metadata = metadata(sce))
biplot(p,
x = 'PC1', y = 'PC2',
lab = NULL,
xlim = c(min(p$rotated[,'PC1'])-1, max(p$rotated[,'PC1'])+1),
ylim = c(min(p$rotated[,'PC2'])-1, max(p$rotated[,'PC2'])+1),
pointSize = 1.0,
colby = 'treatment',
legendPosition = 'right',
title = 'PCA applied to CyTOF data',
caption = paste0('10000 cells randomly selected after ',
'having filtered for low variance'))
We can add the rotated component loadings as a new reduced dimensional component to our dataset.
reducedDim(sce, 'PCA') <- p$rotatedFor more functionality via PCAtools, check the vignette: PCAtools: everything Principal Component Analysis
UMAP can be performed on the entire dataset, if your computer’s memory will permit. Currently it’s default is to use the data contained in the ‘scaled’ assay component of your SingleCellExperiment object.
sce <- performUMAP(sce)UMAP can also be stratified based on a column in your metadata, e.g., (treated versus untreated samples); however, to do this, I recommend creating separate SingleCellExperiment objects from the very start, i.e., from the the data input stage, and processing the data separately for each group.
Nota bene - advanced users may want to change the default configuration for UMAP. scDataviz currently performs UMAP via the umap package. In order to modify the default configuration, one can pull in the default config separately from the umap package and then modify these config values held in the umap.defaults variable, as per the umap vignette (see ‘Tuning UMAP’ section). For example:
config <- umap::umap.defaults
config$min_dist <- 0.5
performUMAP(sce, config = config)We can also perform UMAP on a select number of PC eigenvectors. PCAtools (Blighe and Lun 2020) can be used to infer ideal number of dimensions to use via the elbow method and Horn’s parallel analysis.
elbow <- findElbowPoint(p$variance)
horn <- parallelPCA(assay(sce, 'scaled'))
elbow## PC3
## 3
horn$n## [1] 1
For now, let’s just use 5 PCs.
sce <- performUMAP(sce, reducedDim = 'PCA', dims = c(1:5))This and the remaining sections in this tutorial are about producing great visualisations of the data and attempting to make sense of it, while not fully overlapping with functionalioty provided by other programs that operate in tis space.
With the contour plot, we are essentially looking at celluar density. It can provide for a beautiful viusualisation in a manuscript while also serving as a useful QC tool: if the density is ‘scrunched up’ into a single area in the plot space, then there are likely issues with your input data distribution. We want to see well-separated, high density ‘islands’, or, at least, gradual gradients that blend into one another across high density ‘peaks’.
ggout1 <- contourPlot(sce,
reducedDim = 'UMAP',
bins = 150,
subtitle = 'UMAP performed on expression values',
legendLabSize = 18,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18)
ggout2 <- contourPlot(sce,
reducedDim = 'UMAP_PCA',
bins = 150,
subtitle = 'UMAP performed on PC eigenvectors',
legendLabSize = 18,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18)
cowplot::plot_grid(ggout1, ggout2,
labels = c('A','B'),
ncol = 2, align = "l", label_size = 24)
Here, we randomly select some markers and then plot their expression profiles across the UMAP layouts.
markers <- sample(rownames(sce), 6)
markers## [1] "Foxp3" "CD4" "CD45RA" "CD25" "CD279" "CD46"
ggout1 <- markerExpression(sce,
markers = markers,
subtitle = 'UMAP performed on expression values',
nrow = 1, ncol = 6,
legendKeyHeight = 1.0,
legendLabSize = 18,
stripLabSize = 22,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18)
ggout2 <- markerExpression(sce,
markers = markers,
reducedDim = 'UMAP_PCA',
subtitle = 'UMAP performed on PC eigenvectors',
nrow = 1, ncol = 6,
col = c('white', 'darkblue'),
legendKeyHeight = 1.0,
legendLabSize = 18,
stripLabSize = 22,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18)
cowplot::plot_grid(ggout1, ggout2,
labels = c('A','B'),
nrow = 2, align = "l", label_size = 24)
Shading cells by metadata can be useful for identifying any batch effects, but also useful for visualising, e.g., differences across treatments.
First, let’s take a look inside the metadata that we have.
head(metadata(sce))## sample group treatment
## cell1 P00 Disease Unstim
## cell2 P00 Disease Unstim
## cell3 P04 Disease CD46
## cell4 P03 Disease CD46
## cell5 P08 Disease Unstim
## cell6 P00 Disease CD46
levels(metadata(sce)$group)## [1] "Healthy" "Disease"
levels(metadata(sce)$treatment)## [1] "CD46" "Unstim" "CD3"
ggout1 <- metadataPlot(sce,
colby = 'group',
colkey = c(Healthy = 'royalblue', Disease = 'red2'),
title = 'Disease status',
subtitle = 'UMAP performed on expression values',
legendLabSize = 16,
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
ggout2 <- metadataPlot(sce,
reducedDim = 'UMAP_PCA',
colby = 'group',
colkey = c(Healthy = 'royalblue', Disease = 'red2'),
title = 'Disease status',
subtitle = 'UMAP performed on PC eigenvectors',
legendLabSize = 16,
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
ggout3 <- metadataPlot(sce,
colby = 'treatment',
title = 'Treatment type',
subtitle = 'UMAP performed on expression values',
legendLabSize = 16,
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
ggout4 <- metadataPlot(sce,
reducedDim = 'UMAP_PCA',
colby = 'treatment',
title = 'Treatment type',
subtitle = 'UMAP performed on PC eigenvectors',
legendLabSize = 16,
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
cowplot::plot_grid(ggout1, ggout3, ggout2, ggout4,
labels = c('A','B','C','D'),
nrow = 2, ncol = 2, align = "l", label_size = 24)
This function utilises the k nearest neighbours (k-NN) approach from Seurat, which works quite well on flow cytometry and CyTOF UMAP layouts, from my experience.
sce <- clusKNN(sce,
k.param = 20,
prune.SNN = 1/15,
resolution = 0.01,
algorithm = 2,
verbose = FALSE)
sce <- clusKNN(sce,
reducedDim = 'UMAP_PCA',
clusterAssignName = 'Cluster_PCA',
k.param = 20,
prune.SNN = 1/15,
resolution = 0.01,
algorithm = 2,
verbose = FALSE)
ggout1 <- plotClusters(sce,
clusterColname = 'Cluster',
labSize = 7.0,
subtitle = 'UMAP performed on expression values',
caption = paste0('Note: clusters / communities identified via',
'\nLouvain algorithm with multilevel refinement'),
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
ggout2 <- plotClusters(sce,
clusterColname = 'Cluster_PCA',
reducedDim = 'UMAP_PCA',
labSize = 7.0,
subtitle = 'UMAP performed on PC eigenvectors',
caption = paste0('Note: clusters / communities identified via',
'\nLouvain algorithm with multilevel refinement'),
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 16,
captionLabSize = 16)
cowplot::plot_grid(ggout1, ggout2,
labels = c('A','B'),
ncol = 2, align = "l", label_size = 24)
markerExpressionPerCluster(sce,
caption = 'Cluster assignments based on UMAP performed on expression values',
stripLabSize = 22,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18) clusters <- unique(metadata(sce)[['Cluster_PCA']])
clusters## [1] 2 0 1 6 4 3 5 7
markers <- sample(rownames(sce), 5)
markers## [1] "Foxp3" "C3aR" "CD25" "CD45RA" "CD3"
markerExpressionPerCluster(sce,
clusters = clusters,
clusterAssign = metadata(sce)[['Cluster_PCA']],
markers = markers,
nrow = 2, ncol = 5,
caption = 'Cluster assignments based on UMAP performed on PC eigenvectors',
stripLabSize = 22,
axisLabSize = 22,
titleLabSize = 22,
subtitleLabSize = 18,
captionLabSize = 18)
Try all markers across a single cluster:
cluster <- sample(unique(metadata(sce)[['Cluster']]), 1)
cluster## [1] 1
markerExpressionPerCluster(sce,
clusters = cluster,
markers = rownames(sce),
stripLabSize = 20,
axisLabSize = 20,
titleLabSize = 20,
subtitleLabSize = 14,
captionLabSize = 12)
This method also calculates metacluster abundances across a chosen phenotype. The function returns a data-frame, which can then be exported to do other analyses.
markerEnrichment(sce,
method = 'quantile',
studyvarID = 'group')|
Cluster |
nCells |
TotalCells |
PercentCells |
NegMarkers |
PosMarkers |
PerCent_HD00 |
PerCent_HD01 |
PerCent_HD262 |
PerCent_P00 |
PerCent_P02 |
PerCent_P03 |
PerCent_P04 |
PerCent_P08 |
nCell_Healthy |
nCell_Disease |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
0 |
3410 |
10000 |
34.10 |
NA |
CD25+ |
0.0879765 |
9.6187683 |
25.483871 |
22.8152493 |
8.3870968 |
8.8563050 |
17.7126100 |
7.0381232 |
1200 |
2210 |
|
1 |
1928 |
10000 |
19.28 |
CD25-CD279- |
CD3+CD45RA+ |
0.0000000 |
0.1556017 |
62.085062 |
0.5705394 |
0.2074689 |
0.0000000 |
0.0518672 |
36.9294606 |
1200 |
728 |
|
2 |
1298 |
10000 |
12.98 |
Granzyme B-CD279- |
CD3+ |
15.6394453 |
7.8582435 |
2.234206 |
64.1756549 |
3.8520801 |
3.5439137 |
1.9260401 |
0.7704160 |
334 |
964 |
|
3 |
1236 |
10000 |
12.36 |
NA |
CD25+ |
6.3915858 |
1.1326861 |
1.375404 |
2.9126214 |
24.0291262 |
7.0388350 |
56.8770227 |
0.2427184 |
110 |
1126 |
|
4 |
962 |
10000 |
9.62 |
NA |
CD3+ |
16.5280665 |
26.1954262 |
7.484407 |
27.9625780 |
3.2224532 |
10.0831601 |
6.0291060 |
2.4948025 |
483 |
479 |
|
5 |
502 |
10000 |
5.02 |
CD25- |
NA |
0.3984064 |
14.9402390 |
40.836653 |
11.1553785 |
0.0000000 |
0.3984064 |
0.1992032 |
32.0717131 |
282 |
220 |
|
6 |
300 |
10000 |
3.00 |
CD46-CD279- |
NA |
0.0000000 |
41.0000000 |
2.000000 |
54.6666667 |
0.3333333 |
0.3333333 |
0.6666667 |
1.0000000 |
129 |
171 |
|
7 |
281 |
10000 |
2.81 |
NA |
Foxp3+CD25+CD3+ |
0.3558719 |
0.0000000 |
70.462633 |
2.1352313 |
0.7117438 |
0.7117438 |
0.3558719 |
25.2669039 |
199 |
82 |
|
8 |
61 |
10000 |
0.61 |
CD46- |
NA |
0.0000000 |
18.0327869 |
1.639344 |
0.0000000 |
1.6393443 |
78.6885246 |
0.0000000 |
0.0000000 |
12 |
49 |
|
9 |
22 |
10000 |
0.22 |
CD46-CD279- |
CD3+ |
0.0000000 |
18.1818182 |
4.545454 |
0.0000000 |
0.0000000 |
77.2727273 |
0.0000000 |
0.0000000 |
5 |
17 |
.
markerEnrichment(sce,
sampleAbundances = FALSE,
method = 'quantile',
studyvarID = 'treatment')|
Cluster |
nCells |
TotalCells |
PercentCells |
NegMarkers |
PosMarkers |
nCell_CD46 |
nCell_Unstim |
nCell_CD3 |
|---|---|---|---|---|---|---|---|---|
|
0 |
3410 |
10000 |
34.10 |
NA |
CD25+ |
3384 |
2 |
24 |
|
1 |
1928 |
10000 |
19.28 |
CD25-CD279- |
CD3+CD45RA+ |
3 |
1921 |
4 |
|
2 |
1298 |
10000 |
12.98 |
Granzyme B-CD279- |
CD3+ |
5 |
1172 |
121 |
|
3 |
1236 |
10000 |
12.36 |
NA |
CD25+ |
21 |
132 |
1083 |
|
4 |
962 |
10000 |
9.62 |
NA |
CD3+ |
4 |
771 |
187 |
|
5 |
502 |
10000 |
5.02 |
CD25- |
NA |
112 |
24 |
366 |
|
6 |
300 |
10000 |
3.00 |
CD46-CD279- |
NA |
288 |
10 |
2 |
|
7 |
281 |
10000 |
2.81 |
NA |
Foxp3+CD25+CD3+ |
2 |
276 |
3 |
|
8 |
61 |
10000 |
0.61 |
CD46- |
NA |
57 |
4 |
0 |
|
9 |
22 |
10000 |
0.22 |
CD46-CD279- |
CD3+ |
21 |
1 |
0 |
.
The expression signature is a quick way to visualise which markers are more or less expressed in each identified cluster of cells.
plotSignatures(sce,
labCex = 1.2,
legendCex = 1.2,
labDegree = 40)
Due to the fact that scDataviz is based on SingleCellExperiment, it
has increased interoperability with other packages, including the
popular Seurat (Stuart et al. 2018). Taking the data produced from the
Seurat
Tutorial on
Peripheral Blood Mononuclear Cells (PBMCs), we can convert this to a
SingleCellExperiment object recognisable by scDataviz via
as.SingleCellExperiment().
When deriving from the Seurat route, be sure to manually assign the
metadata slot, which is required for some functions. Also be sure to
modify the default values for assay, reducedDim, and dimColnames,
as these are assigned differently in Seurat.
sce <- as.SingleCellExperiment(pbmc)
metadata(sce) <- data.frame(colData(sce))
markerExpression(sce,
assay = 'logcounts',
reducedDim = 'UMAP',
dimColnames = c('UMAP_1','UMAP_2'),
markers = c('CD79A', 'Cd79B', 'MS4A1'))For markerEnrichment(), a typical command using an ex-Seurat object
could be:
markerEnrichment(sce,
assay = 'logcounts',
method = 'quantile',
sampleAbundances = TRUE,
sampleID = 'orig.ident',
studyvarID = 'ident',
clusterAssign = as.character(colData(sce)[['seurat_clusters']]))scDataviz will work with any numerical data, too. Here, we show a quick example of how one can import a data-matrix of randomly-generated numbers that follow a negative binomial distribution, comprising 2500 cells and 20 markers:
mat <- jitter(matrix(
MASS::rnegbin(rexp(50000, rate=.1), theta = 4.5),
ncol = 20))
colnames(mat) <- paste0('CD', 1:ncol(mat))
rownames(mat) <- paste0('cell', 1:nrow(mat))
metadata <- data.frame(
group = rep('A', nrow(mat)),
row.names = rownames(mat),
stringsAsFactors = FALSE)
head(metadata)## group
## cell1 A
## cell2 A
## cell3 A
## cell4 A
## cell5 A
## cell6 A
sce <- importData(mat,
assayname = 'normcounts',
metadata = metadata)
sce## class: SingleCellExperiment
## dim: 20 2500
## metadata(1): group
## assays(1): normcounts
## rownames(20): CD1 CD2 ... CD19 CD20
## rowData names(0):
## colnames(2500): cell1 cell2 ... cell2499 cell2500
## colData names(0):
## reducedDimNames(0):
## altExpNames(0):
This will also work without any assigned metadata; however, having no metadata limits the functionality of the package.
sce <- importData(mat,
assayname = 'normcounts',
metadata = NULL)
sce## class: SingleCellExperiment
## dim: 20 2500
## metadata(0):
## assays(1): normcounts
## rownames(20): CD1 CD2 ... CD19 CD20
## rowData names(0):
## colnames(2500): cell1 cell2 ... cell2499 cell2500
## colData names(0):
## reducedDimNames(0):
## altExpNames(0):
- Jessica Timms
- James Opzoomer
- Shahram Kordasti
- Marcel Ramos (Bioconductor)
- Lori Shepherd (Bioconductor)
- Bioinformatics CRO
- Henrik Bengtsson
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.7 LTS
##
## Matrix products: default
## BLAS: /usr/lib/atlas-base/atlas/libblas.so.3.0
## LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0
##
## locale:
## [1] LC_CTYPE=pt_BR.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=pt_BR.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=pt_BR.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] PCAtools_2.5.5 ggrepel_0.9.1
## [3] ggplot2_3.3.3 scDataviz_1.3.3
## [5] SingleCellExperiment_1.11.6 SummarizedExperiment_1.18.2
## [7] DelayedArray_0.14.1 matrixStats_0.57.0
## [9] Biobase_2.48.0 GenomicRanges_1.40.0
## [11] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [13] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [15] kableExtra_1.3.1 knitr_1.31
##
## loaded via a namespace (and not attached):
## [1] corrplot_0.84 plyr_1.8.6
## [3] igraph_1.2.6 lazyeval_0.2.2
## [5] splines_4.0.3 flowCore_2.0.1
## [7] BiocParallel_1.22.0 listenv_0.8.0
## [9] scattermore_0.7 digest_0.6.27
## [11] htmltools_0.5.1.1 magrittr_2.0.1
## [13] tensor_1.5 cluster_2.1.0
## [15] ROCR_1.0-11 globals_0.14.0
## [17] RcppParallel_5.0.2 askpass_1.1
## [19] cytolib_2.0.3 colorspace_2.0-0
## [21] rvest_0.3.6 xfun_0.20
## [23] dplyr_1.0.3 crayon_1.3.4
## [25] RCurl_1.98-1.2 jsonlite_1.7.2
## [27] spatstat_1.64-1 spatstat.data_1.7-0
## [29] survival_3.2-7 zoo_1.8-8
## [31] glue_1.4.2 polyclip_1.10-0
## [33] gtable_0.3.0 zlibbioc_1.34.0
## [35] XVector_0.28.0 webshot_0.5.2
## [37] leiden_0.3.7 BiocSingular_1.4.0
## [39] future.apply_1.7.0 abind_1.4-5
## [41] scales_1.1.1 DBI_1.1.1
## [43] miniUI_0.1.1.1 Rcpp_1.0.6
## [45] isoband_0.2.3 viridisLite_0.3.0
## [47] xtable_1.8-4 dqrng_0.2.1
## [49] reticulate_1.18 rsvd_1.0.3
## [51] umap_0.2.7.0 htmlwidgets_1.5.3
## [53] httr_1.4.2 RColorBrewer_1.1-2
## [55] ellipsis_0.3.1 Seurat_4.0.0
## [57] ica_1.0-2 farver_2.0.3
## [59] pkgconfig_2.0.3 uwot_0.1.10
## [61] deldir_0.2-9 labeling_0.4.2
## [63] tidyselect_1.1.0 rlang_0.4.10
## [65] reshape2_1.4.4 later_1.1.0.1
## [67] munsell_0.5.0 tools_4.0.3
## [69] generics_0.1.0 ggridges_0.5.3
## [71] evaluate_0.14 stringr_1.4.0
## [73] fastmap_1.1.0 yaml_2.2.1
## [75] goftest_1.2-2 fitdistrplus_1.1-3
## [77] purrr_0.3.4 RANN_2.6.1
## [79] pbapply_1.4-3 future_1.21.0
## [81] nlme_3.1-151 mime_0.9
## [83] xml2_1.3.2 compiler_4.0.3
## [85] rstudioapi_0.13 plotly_4.9.3
## [87] png_0.1-7 spatstat.utils_2.0-0
## [89] tibble_3.0.1 stringi_1.5.3
## [91] highr_0.8 RSpectra_0.16-0
## [93] lattice_0.20-41 Matrix_1.3-2
## [95] vctrs_0.3.6 pillar_1.4.7
## [97] lifecycle_0.2.0 lmtest_0.9-38
## [99] RcppAnnoy_0.0.18 data.table_1.13.6
## [101] cowplot_1.1.1 bitops_1.0-6
## [103] irlba_2.3.3 httpuv_1.5.5
## [105] patchwork_1.1.1 R6_2.5.0
## [107] promises_1.1.1 KernSmooth_2.23-18
## [109] gridExtra_2.3 RProtoBufLib_2.0.0
## [111] parallelly_1.23.0 codetools_0.2-18
## [113] MASS_7.3-53 assertthat_0.2.1
## [115] openssl_1.4.3 withr_2.4.1
## [117] SeuratObject_4.0.0 sctransform_0.3.2
## [119] GenomeInfoDbData_1.2.3 mgcv_1.8-33
## [121] grid_4.0.3 rpart_4.1-15
## [123] tidyr_1.1.2 rmarkdown_2.6
## [125] DelayedMatrixStats_1.10.1 Rtsne_0.15
## [127] shiny_1.6.0
Blighe (2020)
Blighe and Lun (2020)
Lun and Risso (2020)
Stuart et al. (2018)
Wickham (2016)
Blighe, K. 2020. “scDataviz: single cell dataviz and downstream analyses.” https://github.com/kevinblighe/scDataviz.
Blighe, K, and A Lun. 2020. “PCAtools: everything Principal Component Analysis.” https://github.com/kevinblighe/PCAtools.
Lun, A, and D Risso. 2020. “SingleCellExperiment: S4 Classes for Single Cell Data.” https://bioconductor.org/packages/SingleCellExperiment.
Stuart, Tim, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck III, Marlon Stoeckius, Peter Smibert, and Rahul Satija. 2018. “Comprehensive Integration of Single Cell Data.” bioRxiv. https://doi.org/10.1101/460147.
Wickham, H. 2016. “ggplot2: Elegant Graphics for Data Analysis.” Springer-Verlag New York, ISBN: 978-3-319-24277-4.