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In this vignette, we process fastq data from scRNA-seq (10x v2 chemistry) with to make a sparse matrix that can be used in downstream analysis with command line tools kallisto
and bustools
, as described in the kallisto bus
paper. Then we will start a standard downstream analysis with Seurat
.
If you would like to rerun this notebook, you can git clone this repository or directly download this notebook from GitHub.
The primary analysis section of this notebook demonstrates the use of command line tools kallisto
and bustools
. Please use kallisto
>= 0.45, whose binary can be downloaded here. The binary of bustools
can be found here.
After you download the binary, you should decompress the file (if it is tar.gz
) with tar -xzvf file.tar.gz
in the bash
terminal, and add the directory containing the binary to PATH
by export PATH=$PATH:/foo/bar
, where /foo/bar
here is the directory of interest. Then you can directly invoke the binary on the command line as we will do in this notebook.
We will be using the R packages below. BUSpaRse
is not yet on CRAN or Bioconductor. For Mac users, see the installation note for BUSpaRse
. BUSpaRse
will be used to generate the transcript to gene file for bustools
and to read output of bustools
into R. We will also use Seurat
version 3 which is now on CRAN.
# Install devtools if it's not already installed
if (!require(devtools)) {
install.packages("devtools")
}
# Install from GitHub
devtools::install_github("BUStools/BUSpaRse")
The package DropletUtils
will be used to estimate the number of real cells as opposed to empty droplets. It’s on Bioconductor, and here is how it should be installed:
if (!require(BiocManager)) {
install.packages("BiocManager")
}
BiocManager::install("DropletUtils")
The other R packages below are on CRAN, and can be installed with install.packages
.
library(BUSpaRse)
library(Seurat)
library(tidyverse)
library(DropletUtils)
library(Matrix)
theme_set(theme_bw())
The data set we are using here is 1k 1:1 Mixture of Fresh Frozen Human (HEK293T) and Mouse (NIH3T3) Cells from the 10x website. First, we download the fastq files (6.34 GB).
if (!file.exists("./data/hgmm_1k_fastqs.tar")) {
download.file("http://cf.10xgenomics.com/samples/cell-exp/2.1.0/hgmm_1k/hgmm_1k_fastqs.tar", destfile = "./data/hgmm_1k_fastqs.tar", quiet = TRUE)
}
Then untar this file
cd ./data
tar -xvf ./hgmm_1k_fastqs.tar
#> fastqs/
#> fastqs/hgmm_1k_S1_L001_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L001_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L001_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L002_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L002_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L002_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L003_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L003_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L003_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L004_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L004_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L004_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L005_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L005_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L005_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L006_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L006_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L006_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L007_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L007_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L007_R2_001.fastq.gz
#> fastqs/hgmm_1k_S1_L008_I1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L008_R1_001.fastq.gz
#> fastqs/hgmm_1k_S1_L008_R2_001.fastq.gz
kallisto
indexHere we use kallisto to pseudoalign the reads to the transcriptome and then to create the bus
file to be converted to a sparse matrix. The first step is to build an index of the transcriptome. This data set has both human and mouse cells, so we need both human and mouse transcriptomes. The transcriptomes downloaded here are from Ensembl version 94, released in October 2018.
# Human transcriptome
if (!file.exists("./data/hs_cdna.fa.gz")) {
download.file("ftp://ftp.ensembl.org/pub/release-94/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz", "./data/hs_cdna.fa.gz", method = "wget", quiet = TRUE)
}
# Mouse transcriptome
if (!file.exists("./data/mm_cdna.fa.gz")) {
download.file("ftp://ftp.ensembl.org/pub/release-94/fasta/mus_musculus/cdna/Mus_musculus.GRCm38.cdna.all.fa.gz", "./data/mm_cdna.fa.gz", method = "wget", quiet = TRUE)
}
# This chunk is in bash
kallisto version
#> kallisto, version 0.46.0
Actually, we don’t need to unzip the fasta files
if (!file.exists("./output/hs_mm_tr_index.idx")) {
system("kallisto index -i ./output/hs_mm_tr_index.idx ./data/hs_cdna.fa.gz ./data/mm_cdna.fa.gz")
}
kallisto bus
Here we will generate the bus
file. Here bus
stands for Barbode, UMI, Set (i.e. equivalent class). In text form, it is a table whose first column is the barcode. The second column is the UMI that are associated with the barcode. The third column is the index of the equivalence class reads with the UMI maps to (equivalence class will be explained later). The fourth column is count of reads with this barcode, UMI, and equivalence class combination, which is ignored as one UMI should stand for one molecule. See this paper for more detail.
These are the technologies supported by kallisto bus
:
system("kallisto bus --list", intern = TRUE)
#> Warning in system("kallisto bus --list", intern = TRUE): running command
#> 'kallisto bus --list' had status 1
#> [1] "List of supported single-cell technologies"
#> [2] ""
#> [3] "short name description"
#> [4] "---------- -----------"
#> [5] "10xv1 10x version 1 chemistry"
#> [6] "10xv2 10x version 2 chemistry"
#> [7] "10xv3 10x version 3 chemistry"
#> [8] "CELSeq CEL-Seq"
#> [9] "CELSeq2 CEL-Seq version 2"
#> [10] "DropSeq DropSeq"
#> [11] "inDrops inDrops"
#> [12] "SCRBSeq SCRB-Seq"
#> [13] "SureCell SureCell for ddSEQ"
#> [14] ""
#> attr(,"status")
#> [1] 1
Here we have 8 samples. Each sample has 3 files: I1
means sample index, R1
means barcode and UMI, and R2
means the piece of cDNA. The -i
argument specifies the index file we just built. The -o
argument specifies the output directory. The -x
argument specifies the sequencing technology used to generate this data set. The -t
argument specifies the number of threads used. I ran this on a server and used 8 threads.
# This chunk is in bash
cd ./data
kallisto bus -i ../output/hs_mm_tr_index.idx -o ../output/out_hgmm1k -x 10xv2 -t8 \
./fastqs/hgmm_1k_S1_L001_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L001_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L002_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L002_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L003_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L003_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L004_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L004_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L005_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L005_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L006_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L006_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L007_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L007_R2_001.fastq.gz \
./fastqs/hgmm_1k_S1_L008_R1_001.fastq.gz ./fastqs/hgmm_1k_S1_L008_R2_001.fastq.gz
#>
#> [index] k-mer length: 31
#> [index] number of targets: 302,896
#> [index] number of k-mers: 206,125,466
#> [index] number of equivalence classes: 1,252,306
#> [quant] will process sample 1: ./fastqs/hgmm_1k_S1_L001_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L001_R2_001.fastq.gz
#> [quant] will process sample 2: ./fastqs/hgmm_1k_S1_L002_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L002_R2_001.fastq.gz
#> [quant] will process sample 3: ./fastqs/hgmm_1k_S1_L003_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L003_R2_001.fastq.gz
#> [quant] will process sample 4: ./fastqs/hgmm_1k_S1_L004_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L004_R2_001.fastq.gz
#> [quant] will process sample 5: ./fastqs/hgmm_1k_S1_L005_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L005_R2_001.fastq.gz
#> [quant] will process sample 6: ./fastqs/hgmm_1k_S1_L006_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L006_R2_001.fastq.gz
#> [quant] will process sample 7: ./fastqs/hgmm_1k_S1_L007_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L007_R2_001.fastq.gz
#> [quant] will process sample 8: ./fastqs/hgmm_1k_S1_L008_R1_001.fastq.gz
#> ./fastqs/hgmm_1k_S1_L008_R2_001.fastq.gz
#> [quant] finding pseudoalignments for the reads ... done
#> [quant] processed 63,252,296 reads, 52,229,344 reads pseudoaligned
See what the outputs are
list.files("./output/out_hgmm1k/")
#> [1] "matrix.ec" "output.bus" "run_info.json" "transcripts.txt"
BUStools
For the sparse matrix, most people are interested in how many UMIs per gene per cell, we here we will quantify this from the bus
output, and to do so, we need to find which gene corresponds to each transcript. Remember in the output of kallisto bus
, there’s the file transcripts.txt
. Those are the transcripts in the transcriptome index.
Remember that we downloaded transcriptome FASTA files from Ensembl just now. In FASTA files, each entry is a sequence with a name. In Ensembl FASTA files, the sequence name has genome annotation of the corresponding sequence, so we can extract transcript IDs and corresponding gene IDs and gene names from there.
tr2g <- transcript2gene(fasta_file = c("./data/hs_cdna.fa.gz", "./data/mm_cdna.fa.gz"),
kallisto_out_path = "./output/out_hgmm1k")
#> Reading FASTA file.
#> Reading FASTA file.
#> Sorting transcripts
head(tr2g)
#> transcript gene gene_name
#> 1: ENST00000632684.1 ENSG00000282431.1 TRBD1
#> 2: ENST00000434970.2 ENSG00000237235.2 TRDD2
#> 3: ENST00000448914.1 ENSG00000228985.1 TRDD3
#> 4: ENST00000415118.1 ENSG00000223997.1 TRDD1
#> 5: ENST00000631435.1 ENSG00000282253.1 TRBD1
#> 6: ENST00000390583.1 ENSG00000211923.1 IGHD3-10
bustools
requires tr2g
to be written into a tab delimited file of a specific format: No headers, first column is transcript ID, and second column is the corresponding gene ID. Transcript IDs must be in the same order as in the kallisto
index.
# Write tr2g to format required by bustools
save_tr2g_bustools(tr2g, "./output/tr2g_hgmm.tsv")
A whitelist that contains all the barcodes known to be present in the kit is provided by 10x and comes with CellRanger. A CellRanger installation is required, though we will not run CellRanger here.
# Copy v2 chemistry whitelist to working directory
cp ~/cellranger-3.0.2/cellranger-cs/3.0.2/lib/python/cellranger/barcodes/737K-august-2016.txt \
./data/whitelist_v2.txt
Then we’re ready to make the gene count matrix. First, bustools
runs barcode error correction on the bus
file. Then, the corrected bus
file is sorted by barcode, UMI, and equivalence classes. Then the UMIs are counted and the counts are collapsed into gene level. Here the |
is pipe in bash, just like the magrittr pipe %>%
in R, that pipes the output of one command to the next.
mkdir ./output/out_hgmm1k/genecount ./tmp
bustools correct -w ./data/whitelist_v2.txt -p ./output/out_hgmm1k/output.bus | \
bustools sort -T tmp/ -t 4 -p - | \
bustools count -o ./output/out_hgmm1k/genecount/genes -g ./output/tr2g_hgmm.tsv \
-e ./output/out_hgmm1k/matrix.ec -t ./output/out_hgmm1k/transcripts.txt --genecounts -
#> mkdir: cannot create directory ‘./tmp’: File exists
#> Found 737280 barcodes in the whitelist
#> Number of hamming dist 1 barcodes = 20550336
#> Processed 52229344 bus records
#> In whitelist = 50774199
#> Corrected = 348400
#> Uncorrected = 1106745
#> Read in 51122599 number of busrecords
See what the outputs are
list.files("./output/out_hgmm1k/genecount")
#> [1] "genes.barcodes.txt" "genes.genes.txt" "genes.mtx"
Here we have text files for barcodes and gene names, and an mtx
file for the sparse gene count matrix.
Now we can load the matrix into R for analysis.
res_mat <- read_count_output("./output/out_hgmm1k/genecount",
name = "genes", tcc = FALSE)
Cool, so now we have the sparse matrix. What does it look like?
dim(res_mat)
#> [1] 76416 362063
The number of genes is as expected for two species. There’re way more cells than we expect, which is about 1000. So what’s going on?
How many UMIs per barcode?
tot_counts <- Matrix::colSums(res_mat)
summary(tot_counts)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.00 1.00 1.00 76.26 8.00 74534.00
The vast majority of “cells” have only a few UMI detected. Those are empty droplets. 10x claims to have cell capture rate of up to 65%, but in practice, depending on how many cells are in fact loaded, the rate can be much lower. A commonly used method to estimate the number of empty droplets is barcode ranking knee and inflection points, as those are often assumed to represent transition between two components of a distribution. While more sophisticated method exist (e.g. see emptyDrops
in DropletUtils
), for simplicity, we will use the barcode ranking method here. However, whichever way we go, we don’t have the ground truth.
# Compute barcode rank
bc_rank <- barcodeRanks(res_mat)
qplot(bc_rank$rank, bc_rank$total, geom = "line") +
geom_hline(yintercept = metadata(bc_rank)$knee, color = "blue", linetype = 2) +
geom_hline(yintercept = metadata(bc_rank)$inflection, color = "green", linetype = 2) +
annotate("text", x = 1000, y = 1.5 * c(metadata(bc_rank)$knee, metadata(bc_rank)$inflection),
label = c("knee", "inflection"), color = c("blue", "green")) +
scale_x_log10() +
scale_y_log10() +
labs(x = "Rank", y = "Total reads")
#> Warning: Transformation introduced infinite values in continuous y-axis
The inflection point looks like a reasonable number of cells.
# Filter the matrix
res_mat <- res_mat[, tot_counts > metadata(bc_rank)$inflection]
dim(res_mat)
#> [1] 76416 1094
How many cells are from humans and how many from mice? The number of cells with mixed species indicates doublet rate.
gene_species <- ifelse(str_detect(rownames(res_mat), "^ENSMUSG"), "mouse", "human")
mouse_inds <- gene_species == "mouse"
human_inds <- gene_species == "human"
# mark cells as mouse or human
cell_species <- tibble(n_mouse_umi = Matrix::colSums(res_mat[mouse_inds,]),
n_human_umi = Matrix::colSums(res_mat[human_inds,]),
tot_umi = Matrix::colSums(res_mat),
prop_mouse = n_mouse_umi / tot_umi,
prop_human = n_human_umi / tot_umi)
# Classify species based on proportion of UMI, with cutoff of 90%
cell_species <- cell_species %>%
mutate(species = case_when(
prop_mouse > 0.9 ~ "mouse",
prop_human > 0.9 ~ "human",
TRUE ~ "mixed"
))
ggplot(cell_species, aes(n_human_umi, n_mouse_umi, color = species)) +
geom_point(size = 0.5)
Great, looks like the vast majority of cells are not mixed.
cell_species %>%
dplyr::count(species) %>%
mutate(proportion = n / ncol(res_mat))
#> # A tibble: 3 x 3
#> species n proportion
#> <chr> <int> <dbl>
#> 1 human 566 0.517
#> 2 mixed 3 0.00274
#> 3 mouse 525 0.480
Great, only about 0.3% of cells here are doublets, which is lower than the ~1% 10x lists. Doublet rate tends to be lower when cell concentration is lower. However, doublets can still be formed with cells from the same species, so the number of mixed species “cells” is only a lower bound of doublet rate.
seu <- CreateSeuratObject(res_mat, min.cells = 3) %>%
NormalizeData(verbose = FALSE) %>%
ScaleData(verbose = FALSE) %>%
FindVariableFeatures(verbose = FALSE)
# Add species to meta data
seu <- AddMetaData(seu, metadata = cell_species$species, col.name = "species")
See how number of total counts and number of genes expressed are distributed.
VlnPlot(seu, c("nCount_RNA", "nFeature_RNA"), group.by = "species",
pt.size = 0.1)
Another QC plot
ggplot(seu@meta.data, aes(nCount_RNA, nFeature_RNA, color = species)) +
geom_point(alpha = 0.7, size = 0.5) +
labs(x = "Total UMI counts per cell", y = "Number of genes detected")
Version | Author | Date |
---|---|---|
9fa20ae | Lambda Moses | 2019-06-22 |
The mixed species doublets do look different from human and mouse cells.
seu <- RunPCA(seu, verbose = FALSE, npcs = 30)
ElbowPlot(seu, ndims = 30)
DimPlot(seu, reduction = "pca", pt.size = 0.5, group.by = "species")
The first PC separates species, as expected. Also as expected, the doublets are in between human and mouse cells in this plot.
seu <- RunTSNE(seu, dims = 1:20, check_duplicates = FALSE)
DimPlot(seu, reduction = "tsne", pt.size = 0.5, group.by = "species")
The species separate, and the few doublets form its own cluster, as expected.
sessionInfo()
#> R version 3.5.2 (2018-12-20)
#> Platform: x86_64-redhat-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [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
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] Matrix_1.2-15 DropletUtils_1.5.3
#> [3] SingleCellExperiment_1.4.1 SummarizedExperiment_1.12.0
#> [5] DelayedArray_0.8.0 BiocParallel_1.16.6
#> [7] matrixStats_0.54.0 Biobase_2.42.0
#> [9] GenomicRanges_1.34.0 GenomeInfoDb_1.18.2
#> [11] IRanges_2.16.0 S4Vectors_0.20.1
#> [13] BiocGenerics_0.28.0 forcats_0.4.0
#> [15] stringr_1.4.0 dplyr_0.8.1
#> [17] purrr_0.3.2 readr_1.3.1
#> [19] tidyr_0.8.3 tibble_2.1.3
#> [21] ggplot2_3.2.0 tidyverse_1.2.1
#> [23] Seurat_3.0.2 BUSpaRse_0.99.4
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.3.1 backports_1.1.4
#> [3] workflowr_1.4.0 plyr_1.8.4
#> [5] igraph_1.2.4.1 lazyeval_0.2.2
#> [7] splines_3.5.2 listenv_0.7.0
#> [9] digest_0.6.19 htmltools_0.3.6
#> [11] fansi_0.4.0 gdata_2.18.0
#> [13] magrittr_1.5 memoise_1.1.0
#> [15] cluster_2.0.7-1 ROCR_1.0-7
#> [17] limma_3.38.3 globals_0.12.4
#> [19] Biostrings_2.50.2 modelr_0.1.4
#> [21] RcppParallel_4.4.3 R.utils_2.9.0
#> [23] prettyunits_1.0.2 colorspace_1.4-1
#> [25] rvest_0.3.4 blob_1.1.1
#> [27] ggrepel_0.8.1 haven_2.1.0
#> [29] xfun_0.7 crayon_1.3.4
#> [31] RCurl_1.95-4.12 jsonlite_1.6
#> [33] zeallot_0.1.0 survival_2.44-1.1
#> [35] zoo_1.8-5 ape_5.3
#> [37] glue_1.3.1 gtable_0.3.0
#> [39] zlibbioc_1.28.0 XVector_0.22.0
#> [41] plyranges_1.2.0 Rhdf5lib_1.4.3
#> [43] future.apply_1.3.0 HDF5Array_1.10.1
#> [45] scales_1.0.0 edgeR_3.24.3
#> [47] DBI_1.0.0 bibtex_0.4.2
#> [49] Rcpp_1.0.1 metap_1.1
#> [51] viridisLite_0.3.0 progress_1.2.2
#> [53] dqrng_0.2.1 reticulate_1.12
#> [55] bit_1.1-14 rsvd_1.0.1
#> [57] SDMTools_1.1-221.1 tsne_0.1-3
#> [59] htmlwidgets_1.3 httr_1.4.0
#> [61] gplots_3.0.1.1 RColorBrewer_1.1-2
#> [63] ica_1.0-2 pkgconfig_2.0.2
#> [65] XML_3.98-1.20 R.methodsS3_1.7.1
#> [67] utf8_1.1.4 locfit_1.5-9.1
#> [69] labeling_0.3 tidyselect_0.2.5
#> [71] rlang_0.3.4 reshape2_1.4.3
#> [73] AnnotationDbi_1.44.0 cellranger_1.1.0
#> [75] munsell_0.5.0 tools_3.5.2
#> [77] cli_1.1.0 generics_0.0.2
#> [79] RSQLite_2.1.1 broom_0.5.2
#> [81] ggridges_0.5.1 evaluate_0.13
#> [83] yaml_2.2.0 npsurv_0.4-0
#> [85] knitr_1.22 bit64_0.9-7
#> [87] fs_1.3.1 fitdistrplus_1.0-14
#> [89] caTools_1.17.1.2 RANN_2.6.1
#> [91] pbapply_1.4-0 future_1.13.0
#> [93] nlme_3.1-137 whisker_0.3-2
#> [95] R.oo_1.22.0 xml2_1.2.0
#> [97] biomaRt_2.38.0 rstudioapi_0.10
#> [99] compiler_3.5.2 plotly_4.9.0
#> [101] png_0.1-7 lsei_1.2-0
#> [103] stringi_1.4.3 lattice_0.20-38
#> [105] vctrs_0.1.0 pillar_1.4.1
#> [107] Rdpack_0.11-0 lmtest_0.9-37
#> [109] data.table_1.12.2 cowplot_0.9.4
#> [111] bitops_1.0-6 irlba_2.3.3
#> [113] gbRd_0.4-11 rtracklayer_1.42.2
#> [115] R6_2.4.0 KernSmooth_2.23-15
#> [117] gridExtra_2.3 codetools_0.2-15
#> [119] MASS_7.3-51.4 gtools_3.8.1
#> [121] assertthat_0.2.1 rhdf5_2.26.2
#> [123] rprojroot_1.3-2 withr_2.1.2
#> [125] GenomicAlignments_1.18.1 sctransform_0.2.0
#> [127] Rsamtools_1.34.1 GenomeInfoDbData_1.2.0
#> [129] hms_0.4.2 grid_3.5.2
#> [131] rmarkdown_1.12 Rtsne_0.15
#> [133] git2r_0.25.2 lubridate_1.7.4