ScRNA

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Resource

Workshops

Library preparation

  • Smart-Seq2
  • Drop-Seq
  • Fluidigm-C1

Pipeline

Public data

scRNAseq package

scRNAseq package from Bioconductor.

ExperimentHub package used by scRNAseq, DuoClustering2018 and other packages.

SRAToolkit

sinteractive  --gres=lscratch:800  --cpus-per-task=6 --mem=40g
  # the required memory can be checked by using the '''jobload''' command
  # the required local disk space can be obtained by 'ssh cnXXX' and run 'du -sh /lscratch/JobID'
  #    where the JobID is obtained through the jobload command

module load sratoolkit

fasterq-dump -t /lscratch/$SLURM_JOBID SRR2048331 # 1 min, 3.3G fastq file
fastq-dump --split-3 SRR2048331                   # 2 min 30 sec
pigz -p6 SRR12148211*.fastq  # vs gzip. 6 threads are used here 
                             # do not need to split threads,
                             # do not need to run separately

fastq-dump --split-3 --gzip SRR2048331   # 658M SRR2048331.fastq.gz (vs 626.9Mb in sra format)

fastq-dump --split-3 --gzip SRR12148213  # 862M + 2.5G for fastq.gz files (vs 2Gb in sra format)
                                         # 6.5G + 15G for fastq files
                                         # fastq/fastq.gz = 15/2.5 = 6
                                         # fastq.gz/sr = 3.4/2 = 1.7
# This is equivalent to 
# prefetch SRR12148213; cd SRR12148213
# fastq-dump --split-3 --gzip SRR12148213.sra # 45 min
# The 'prefetch' will create a new folder SRR12148213 and download SRR12148213.sra into it.
# Note the file sizes from _1.fastq and _2.fastq are quite different. 
# Another run. Check number of reads.
$ zcat SRR12148214_1.fastq.gz | wc -l
150635300
$ zcat SRR12148214_2.fastq.gz | wc -l
150635300

fasterq-dump -t /lscratch/$SLURM_JOB_ID \
  --split-files \
  -O /lscratch/$SLURM_JOBID SRR13526458; \
  pigz -p6 /lscratch/$SLURM_JOBID/SRR13526458*.fastq; \
  cp /lscratch/$SLURM_JOBID/SRR13526458*.fastq.gz ~/PDACPDX

sbatch --gres=lscratch:800 --mem=40g --cpus-per-task=6 --time=12:00:00 myscript

And the fasterq-dump's wiki and the help page 

$ fasterq-dump --help

Usage: fasterq-dump [ options ] [ accessions(s)... ]

Parameters:

  accessions(s)                    list of accessions to process


Options:

  -o|--outfile <path>              full path of outputfile (overrides usage
                                     of current directory and given accession)
  -O|--outdir <path>               path for outputfile (overrides usage of
                                     current directory, but uses given
                                     accession)
  -b|--bufsize <size>              size of file-buffer (dflt=1MB, takes
                                     number or number and unit)
  -c|--curcache <size>             size of cursor-cache (dflt=10MB, takes
                                     number or number and unit)
  -m|--mem <size>                  memory limit for sorting (dflt=100MB,
                                     takes number or number and unit)
  -t|--temp <path>                 path to directory for temp. files
                                     (dflt=current dir.)
  -e|--threads <count>             how many threads to use (dflt=6)
  -p|--progress                    show progress (not possible if stdout used)
  -x|--details                     print details of all options selected
  -s|--split-spot                  split spots into reads
  -S|--split-files                 write reads into different files
  -3|--split-3                     writes single reads into special file
     --concatenate-reads           writes whole spots into one file
  -Z|--stdout                      print output to stdout
  -f|--force                       force overwrite of existing file(s)
  -N|--rowid-as-name               use rowid as name (avoids using the name
                                     column)
     --skip-technical              skip technical reads
     --include-technical           explicitly include technical reads
  -P|--print-read-nr               include read-number in defline
  -M|--min-read-len <count>        filter by sequence-lenght
     --table <name>                which seq-table to use in case of pacbio
     --strict                      terminate on invalid read
  -B|--bases <bases>               filter output by matching against given
                                     bases
  -A|--append                      append to output-file, instead of
                                     overwriting it
     --ngc <path>                  <path> to ngc file
     --perm <path>                 <path> to permission file
     --location <location>         location in cloud
     --cart <path>                 <path> to cart file
     --disable-multithreading      disable multithreading
  -V|--version                     Display the version of the program
  -v|--verbose                     Increase the verbosity of the program
                                     status messages. Use multiple times for
                                     more verbosity.
  -L|--log-level <level>           Logging level as number or enum string.
                                     One of
                                     (fatal|sys|int|err|warn|info|debug) or
                                     (0-6) Current/default is warn
     --option-file file            Read more options and parameters from the
                                     file.
  -h|--help                        print this message

"fasterq-dump" version 2.10.9

Note that fastq.gz vs fastq file size is about 1:8 ratio. 

$ ls -lhogt | sed 's/^[^ ][^ ]*  *[^ ][^ ]* //'
 23G May  7 11:35 SRR10156301_2.fastq
 18G May  7 11:35 SRR10156301_1.fastq
3.1G May  7 11:02 SRR10156301_2.fastq.gz
1.4G May  7 11:02 SRR10156301_1.fastq.gz

Simulation

A benchmark study of simulation methods for single-cell RNA sequencing data, Blog

Splatter: Simulation Of Single-Cell RNA Sequencing Data

biorxiv. Genome Biol. 2017, splatter package. We can simulate data using 1) estimated parameters from an existing SingleCellExperiment object, or 2) user's specified parameters. 

suppressPackageStartupMessages({
  library(splatter)
  library(scater)
})

library(scRNAseq)
sce2 <- MacoskoRetinaData()
sce2

ncells <- 1000
params <- splatEstimate(as.matrix(assays(sce2)$counts[, 1:ncells]))
sim <- splatSimulate(params)

comparison <- compareSCEs(list(MacoskoRetina = sce2[, 1:ncells], Splat = sim))
ggplot(comparison$ColData, aes(x = sum, y = detected, colour = Dataset)) +
    geom_point()

Preprocess

What does single-cell RNA sequencing look like? Ideal droplet containing both a cell and RNA-capture reagents.

fastq file format, UMI

  • Single-cell RNA-seq data - raw data to count matrix
    • Reads with different UMI are biological duplicates - each read should be counted.
    • Reads with the same UMI are are technical duplicates - the UMIs should be collapsed to be counted as a single read.
  • Read1 contains barcode (represents cells) and UMI (represents reads), Read2 contains transcript sequence.
  • One case from scruff vignette.

Cell ranger

  • Chromium Single Cell Gene Expression
  • pbmc3k. The data is saved in filtered_gene_bc_matrices/hg19 directory with 3 files, barcodes.tsv (45K), genes.tsv (798K) and matrix.mtx (27M).
  • Getting started with Cell Ranger by Dave Tang
  • Workshop from UC Davis
  • *10x genomics single-cell RNAseq analysis from SRA data using Cell Ranger and Seurat, bioinformaticsworkbook.org (useful)
  • how to get/make/find fastq file from cellranger?
  • filtered_feature_bc_matrix vs raw_feature_bc_matrix. "Filtered feature-barcode matrix" contains only detected cellular barcodes. <raw_feature_bc_matrix> contains mainly empty barcodes. That is, the difference is the number of cells.
  • https://hpc.nih.gov/apps/cellranger.html
    • Reference genome 
      module load cellranger/5.0.0
echo $CELLRANGER_REF/refdata-gex-GRCh38-2020-A
# /fdb/cellranger/refdata-gex-GRCh38-2020-A
ls /fdb/cellranger/refdata-gex-GRCh38-2020-A
# fasta  genes  pickle  reference.json  star

ls /fdb/cellranger/
# refdata-cellranger-1.1.0         refdata-cellranger-GRCh38-1.2.0           refdata-cellranger-vdj-GRCh38-alts-ensembl-2.0.0
# refdata-cellranger-1.2.0         refdata-cellranger-GRCh38-3.0.0           refdata-cellranger-vdj-GRCh38-alts-ensembl-3.1.0
# refdata-cellranger-2.0.0         refdata-cellranger-GRCh38-and-mm10-3.1.0  refdata-cellranger-vdj-GRCh38-alts-ensembl-4.0.0
# refdata-cellranger-2020-A        refdata-cellranger-hg19-1.2.0             refdata-cellranger-vdj-GRCh38-alts-ensembl-5.0.0
# refdata-cellranger-2.1.0         refdata-cellranger-hg19-3.0.0             refdata-cellranger-vdj-GRCm38-alts-ensembl-2.2.0
# refdata-cellranger-2.2.0         refdata-cellranger-hg19_and_mm10-1.2.0    refdata-cellranger-vdj-GRCm38-alts-ensembl-3.1.0
# refdata-cellranger-3.0.0         refdata-cellranger-hg19_and_mm10-2.1.0    refdata-cellranger-vdj-GRCm38-alts-ensembl-4.0.0
# refdata-cellranger-3.1.0         refdata-cellranger-hg19-and-mm10-3.0.0    refdata-cellranger-vdj-GRCm38-alts-ensembl-5.0.0
# refdata-cellranger-4.0.0         refdata-cellranger-mm10-1.2.0             refdata-gex-GRCh38-2020-A
# refdata-cellranger-5.0.0         refdata-cellranger-mm10-2.1.0             refdata-gex-GRCh38_and_mm10-2020-A
# refdata-cellranger-ercc92-1.2.0  refdata-cellranger-mm10-3.0.0             refdata-gex-mm10-2020-A

ls /fdb/cellranger/refdata-gex-GRCh38-2020-A/genes
# genes.gtf 

# One column contains "gene_name"
head -n 6 /fdb/cellranger/refdata-gex-GRCh38-2020-A/genes/genes.gtf
##description: evidence-based annotation of the human genome (GRCh38), version 32 (Ensembl 98)
##provider: GENCODE
##contact: [email protected]
##format: gtf
##date: 2019-09-05
chr1	HAVANA	gene	29554	31109	.	+	.	gene_id "ENSG00000243485"; gene_version "5"; gene_type "lncRNA"; gene_name "MIR1302-2HG"; level 2; hgnc_id "HGNC:52482"; tag "ncRNA_host"; havana_gene "OTTHUMG00000000959.2";
    • cellranger mkfastq wraps Illumina's bcl2fastq to correctly demultiplex Chromium-prepared sequencing samples and to convert barcode and read data to FASTQ files.
    • cellranger count takes FASTQ files from cellranger mkfastq and performs alignment, filtering, and UMI counting. It uses the Chromium cellular barcodes to generate gene-cell matrices and perform clustering and gene expression analysis.
    • cellranger aggr aggregates results from cellranger count. How does cellranger aggr normalize for sequencing depth among multiple libraries?
    • cellranger reanalyze takes feature-barcode matrices produced by cellranger count or aggr and re-runs the dimensionality reduction, clustering, and gene expression algorithms.
    • The example code will generate an output directory with 
      $ tree s1/outs

├── analysis
│   ├── clustering
│   │   ├── graphclust
│   │   │   └── clusters.csv
│   │   ├── kmeans_10_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_2_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_3_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_4_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_5_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_6_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_7_clusters
│   │   │   └── clusters.csv
│   │   ├── kmeans_8_clusters
│   │   │   └── clusters.csv
│   │   └── kmeans_9_clusters
│   │       └── clusters.csv
│   ├── diffexp
│   │   ├── graphclust
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_10_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_2_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_3_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_4_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_5_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_6_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_7_clusters
│   │   │   └── differential_expression.csv
│   │   ├── kmeans_8_clusters
│   │   │   └── differential_expression.csv
│   │   └── kmeans_9_clusters
│   │       └── differential_expression.csv
│   ├── pca
│   │   └── 10_components
│   │       ├── components.csv
│   │       ├── dispersion.csv
│   │       ├── features_selected.csv
│   │       ├── projection.csv
│   │       └── variance.csv
│   ├── tsne
│   │   └── 2_components
│   │       └── projection.csv
│   └── umap
│       └── 2_components
│           └── projection.csv
├── cloupe.cloupe
├── filtered_feature_bc_matrix
│   ├── barcodes.tsv.gz
│   ├── features.tsv.gz
│   └── matrix.mtx.gz   #  36601 x 25 after dim(Read10X(data.dir ="filtered_feature_bc_matrix"))
├── filtered_feature_bc_matrix.h5
├── metrics_summary.csv
├── molecule_info.h5
├── possorted_genome_bam.bam
├── possorted_genome_bam.bam.bai
├── raw_feature_bc_matrix
│   ├── barcodes.tsv.gz
│   ├── features.tsv.gz
│   └── matrix.mtx.gz  # 36601 x 737280
├── raw_feature_bc_matrix.h5
└── web_summary.html

31 directories, 41 files

      # The 2nd column contains gene symbol
$ zcat s1/outs/filtered_feature_bc_matrix/features.tsv.gz | head -5
ENSG00000243485	MIR1302-2HG	Gene Expression
ENSG00000237613	FAM138A	Gene Expression
ENSG00000186092	OR4F5	Gene Expression
ENSG00000238009	AL627309.1	Gene Expression
ENSG00000239945	AL627309.3	Gene Expression

# R
> x <- Read10X("s1/outs/filtered_feature_bc_matrix")
> dim(x)
[1] 36601    25
> x[1:5, 1:3]
5 x 3 sparse Matrix of class "dgCMatrix"
            AACGTTGGTTAAAGTG-1 AGTAGTCAGAGCTATA-1 ATCTGCCCATACTCTT-1
MIR1302-2HG                  .                  .                  .
FAM138A                      .                  .                  .
OR4F5                        .                  .                  .
AL627309.1                   .                  .                  .
AL627309.3                   .                  .                  .
> length(unique(rownames(x)))
[1] 36601
  • Why do we have Barcodes with Counts that are not Called Cells? Ambient RNA, A barcode is called a cell if Total UMI count > M/10 is called a cell, M = Cell with maximum total UMI counts (99th percentile of the top N)
  • Reads are considered duplicated, if they map to the same gene and have the same UMI. Instead of counting reads we will count number of unique UMIs per gene.

10x Genomics fragment schematic

10x Genomics fragment schematic

10x Molecule Info. This is referenced by the DropletUtils::read10xMolInfo() function.

plot

This is an example (GSM4088780) of part of "web_summary.html" output from running cellranger count 2.1.1.

Cellrangersum.png

We can create a similar plot using the DropletUtils::barcodeRanks() function. Here is the plot from running the example.

BarcodeRanks.png

  • 11,100 barcoded droplets
  • totals =colSums(mat). It has 877 unique values. Range is [0, 2344].
  • lower=100. Only cells satisfies the cutoff will be used in fitting the curve and find left.edge, right.edge, knee, inflection.
  • run.totals 877 values. Range [0, 2344]
  • run.rank 877 values. Range [1, 10900.5]
  • out = 11,100 x 3. Columns include rank, total, fitted
  • left.edge=68, rigth.edge=653. Associate with the x-axis. Relative to x, y.
  • knee=657, inflection=321 (inflection <- 10^(y[right.edge])). Used in the y-axis
  • In cellranger count, y-axis = UMI counts, x-axis = Barcodes.
  • In cellranger count, green points = cells, gray points = background.

emptyDrops() and DropletQC

emptyDrops() 

set.seed(0)
my.counts <- DropletUtils:::simCounts()

# Identify likely cell-containing droplets.
out <- emptyDrops(my.counts)
out
# DataFrame with 11100 rows and 5 columns
#           Total   LogProb    PValue   Limited        FDR
#       <integer> <numeric> <numeric> <logical>  <numeric>
# 1             2        NA        NA        NA         NA
# 2             9        NA        NA        NA         NA
# ...         ...       ...       ...       ...        ...
# 11099       191  -228.763 9.999e-05      TRUE 0.00013799
# 11100       198  -233.043 9.999e-05      TRUE 0.00013799

is.cell <- out$FDR <= 0.01
sum(is.cell, na.rm=TRUE)  # 943

DropletQC – improved identification of empty droplets and damaged cells in single-cell RNA-seq data

estimateAmbience()

estimateAmbience().

This function obtains an estimate of the composition of the ambient pool of RNA based on the barcodes with total UMI counts less than or equal to "lower". The default approach is to assume that all barcodes with total counts less than or equal to "lower" are empty.

It returns a numeric vector of length equal to nrow(m), containing the estimated the transcript proportions in the ambient solution. 

set.seed(0)
my.counts <- DropletUtils:::simCounts()
dim(my.counts) # [1]   100 11100

ambience <- estimateAmbience(my.counts)
head(ambience)
#        GENE1        GENE2        GENE3        GENE4        GENE5        GENE6 
# 0.0002213677 0.0003984648 0.0006331183 0.0008146427 0.0010935705 0.0012042561
length(ambience)  #  [1] 100

removeAmbience()

removeAmbience()

Estimate and remove the ambient profile from a count matrix, given pre-existing groupings of similar cells. This function is largely intended for plot beautification rather than real analysis.

It returns a numeric matrix-like object of the same dimensions as y, containing the counts after removing the ambient contamination. 

ngenes <- 1000
set.seed(1)
ambient <- runif(ngenes, 0, 0.1)  # length = 1000
cells <- c(runif(100) * 10, integer(900))  # length = 1000
y <- matrix(rpois(ngenes * 100, cells + ambient), nrow=ngenes)

# Pretending that all cells are in one group, in this example.
removed <- removeAmbience(y, ambient, groups=rep(1, ncol(y)))
dim(y)       # [1] 1000  100
dim(removed) # [1] 1000  100
summary(as.vector(y))
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  0.0000  0.0000  0.0000  0.5423  0.0000 20.0000 
summary(as.vector(removed))
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   0.000   0.000   0.000   0.515   0.000  20.000

Use tools other than cellranger

See Chapter 3 Processing Raw scRNA-seq Data and Chapter 4 Construction of expression matrix.

(non-UMI) Kallisto-Bustools or Alevin, featureCounts, RSEM. Note Kallisto can do both alignment and gene counting.

(UMI-based) UMI-tools & zUMIs, zUMIs: A fast and flexible pipeline to process RNA sequencing data with UMIs

PDX

Seurat*

  • Introduction vignette
    Purpose Code Comment
    Setup the Seurat Object pbmc.data <- Read10X(data.dir = "../data/pbmc3k/filtered_gene_bc_matrices/hg19/")
    pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
    pbmc[["RNA"]]@counts     		Note the @ operator
    pbmc.data[c("CD3D", "TCL1A", "MS4A1"), 1:30]
    dense.size <- object.size(as.matrix(pbmc.data))
    QC pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
    VlnPlot(pbmc, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
    plot1 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "percent.mt")
    plot2 <- FeatureScatter(pbmc, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
    plot1 + plot2
    pbmc <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)     		select cells with number of features in the range of (200,2500)
    Normalizing the data pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
    pbmc[["RNA"]]@data     		Note the @ operator
    feature selection pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000)
    top10 <- head(VariableFeatures(pbmc), 10)
    plot1 <- VariableFeaturePlot(pbmc)
    plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
    plot1 + plot2
    Scaling the data all.genes <- rownames(pbmc)
    pbmc <- ScaleData(pbmc, features = all.genes)
    pbmc[["RNA"]]@scale.data     		Note the @ operator
    Perform linear dimensional reduction pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc))
    print(pbmc[["pca"]], dims = 1:5, nfeatures = 5)
    VizDimLoadings(pbmc, dims = 1:2, reduction = "pca")
    DimPlot(pbmc, reduction = "pca")
    DimHeatmap(pbmc, dims = 1, cells = 500, balanced = TRUE)
    DimHeatmap(pbmc, dims = 1:15, cells = 500, balanced = TRUE)
    Determine the ‘dimensionality’ of the dataset pbmc <- JackStraw(pbmc, num.replicate = 100)
    pbmc <- ScoreJackStraw(pbmc, dims = 1:20)
    Determine the ‘dimensionality’ of the dataset pbmc <- JackStraw(pbmc, num.replicate = 100)
    pbmc <- ScoreJackStraw(pbmc, dims = 1:20)
    JackStrawPlot(pbmc, dims = 1:15)
    ElbowPlot(pbmc)
    Cluster the cells pbmc <- FindNeighbors(pbmc, dims = 1:10)
    pbmc <- FindClusters(pbmc, resolution = 0.5)
    head(Idents(pbmc), 5)
    Non-linear transformation: UMAP/tSNE pbmc <- RunUMAP(pbmc, dims = 1:10)
    DimPlot(pbmc, reduction = "umap")
    Finding differentially expressed features cluster1.markers <- FindMarkers(pbmc, ident.1 = 2, min.pct = 0.25)
    head(cluster1.markers, n = 5)
    cluster5.markers <- FindMarkers(pbmc, ident.1 = 5, ident.2 = c(0, 3), min.pct = 0.25)
    head(cluster5.markers, n = 5)
    pbmc.markers <- FindAllMarkers(pbmc, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
    pbmc.markers %>% group_by(cluster) %>% top_n(n = 2, wt = avg_log2FC)
    cluster1.markers <- FindMarkers(pbmc, ident.1 = 0, logfc.threshold = 0.25, test.use = "roc", only.pos = TRUE)
    VlnPlot(pbmc, features = c("MS4A1", "CD79A"))
    VlnPlot(pbmc, features = c("NKG7", "PF4"), slot = "counts", log = TRUE)
    FeaturePlot(pbmc, features = c("MS4A1", "GNLY", "CD3E", "CD14", "FCER1A", "FCGR3A", "LYZ", "PPBP", "CD8A"))
    top10 <- pbmc.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
    DoHeatmap(pbmc, features = top10$gene) + NoLegend()
    Assigning cell type identity to clusters new.cluster.ids <- c("Naive CD4 T", "CD14+ Mono", "Memory CD4 T", "B", "CD8 T", "FCGR3A+ Mono", "NK", "DC", "Platelet")
    names(new.cluster.ids) <- levels(pbmc)
    pbmc <- RenameIdents(pbmc, new.cluster.ids)
    DimPlot(pbmc, reduction = "umap", label = TRUE, pt.size = 0.5) + NoLegend()

seurat-wrappers

https://github.com/satijalab/seurat-wrappers

Seurat object

Seurat methods

QC

QC part explanation: 

> pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
> pbmc[["percent.mt"]] <- PercentageFeatureSet(pbmc, pattern = "^MT-")
> pbmc2 <- subset(pbmc, subset = nFeature_RNA > 200 & nFeature_RNA < 2500)
> pbmc
An object of class Seurat 
13714 features across 2700 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
> pbmc2
An object of class Seurat 
13714 features across 2695 samples within 1 assay 
Active assay: RNA (13714 features, 0 variable features)
> ind <- which(!colnames(pbmc[['RNA']]) %in% colnames(pbmc2[['RNA']]))
> ind
[1]  427 1060 1762 1878 2568
> tmp <- apply(pbmc[['RNA']]@data, 2, function(x) sum(x > 0)) # detected genes per cell
> range(tmp)
[1]  212 3400
> hist(tmp) # kind of normal
> which(tmp > 2500)
AGAGGTCTACAGCT-1 CCAGTCTGCGGAGA-1 GCGAAGGAGAGCTT-1 
             427             1060             1762 
GGCACGTGTGAGAA-1 TTACTCGAACGTTG-1 
            1878             2568 
> tmp2 <- colSums(pbmc[['RNA']]@data) # total counts per cell
> hist(tmp2)  # kind of normal

Normalization

  • NormalizeData(). log1p(value/colSums[cell-idx] *scale_factor). The scale_factor is 10,000 by default and this gives a good range on the normalized values (at least on the pbmc data). The cpp source code of LogNorm() in preprocessing.R. 
    R> range(log1p(pbmc[['RNA']]@data[,1]/sum(pbmc[['RNA']]@data[,1]) * 10000))
[1] 0.000000 5.753142

R> range(log1p(pbmc[['RNA']]@data[,1]/sum(pbmc[['RNA']]@data[,1]) * 1000))
[1] 0.000000 3.478712

R> range(log1p(pbmc[['RNA']]@data[,1]/sum(pbmc[['RNA']]@data[,1]) * 100000))
[1] 0.000000 8.052868

    And below is a comparison of the raw counts and normalized values: 

    R> pbmc2 <- NormalizeData(pbmc, normalization.method = "LogNormalize", 
                          scale.factor = 10000)
R> cbind(pbmc[['RNA']]@data[i, 1], pbmc2[['RNA']]@data[i, 1])
            [,1]     [,2]
MRPL20         1 1.635873
RPL22          1 1.635873
TNFRSF25       2 2.226555
EFHD2          1 1.635873
NECAP2         1 1.635873
AKR7A2         1 1.635873
CAPZB          1 1.635873
RPL11         41 5.138686
RP5-886K2.3    1 1.635873
PITHD1         1 1.635873

Integration datasets

DE analysis

Visualization

Azimuth

Azimuth - App for reference-based single-cell analysis

GUI/web applications

Asc-Seurat – Analytical single-cell Seurat-based web application

tidyseurat

tidyseurat, paper

Bioconductor packages

Description Packages
Orchestrating Single-Cell Analysis with Bioconductor (OSCA)
simpleSingleCell is an earlier version 		See the list here
single cell RNA-Seq workflow (included in OSCA now) simpleSingleCell
visualization tools for single-cell and Bulk RNA Sequencing dittoSeq
An Introduction to Single-Cell RNA-sequencing Analysis in Bioconductor (中文, based on OSCA ebook） DropletUtils, scran, scater, SingleR, BiocFileCache
An Opinionated Computational Workflow for Single-Cell RNA-seq and ATAC-seq SingleCellExperiment, DropletUtils, scater, iSEE(shiny), scran, limma, edgeR
SplatPop: Simulating Population-Scale Single-Cell RNA-sequencing Data splatter
Importing alevin scRNA-seq counts into R/Bioconductor (check 'Get started') tximeta, SingleCellExperiment, fishpond, scran, Seurat

GUI

NIDAP

https://nidap.nih.gov/ which uses Code workbook (Code Workbook is an application that allows users to analyze and transform data using an intuitive graphical interface) for the interaction. The R/python analysis code can be accessed just like GEO2R from GEO. The background material is available on here.

Partek

Past Webinars

BingleSeq

BingleSeq: a user-friendly R package for bulk and single-cell RNA-Seq data analysis

How many cells are in the human body?

30-40 trillion (1012) cells

Power analysis

  • powsimR: power analysis for bulk and single cell RNA-seq experiments. It is time-consuming and error-prone to install lots of required packages. Better to have a Docker image.
  • SCOPIT: sample size calculations for single-cell sequencing experiments
  • POWSC - Simulation, power evaluation and sample size recommendation for single-cell RNA-seq, Su 2020. 
    library(devtools)
install_github("haowulab/SC2P", build_vignettes=TRUE)
install_github("suke18/POWSC", build_vignettes = T, dependencies = T)

library(POWSC)
data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
est_Paras = Est2Phase(sce)

sim_size = c(100, 400, 1000) # A numeric vector
pow_rslt = runPOWSC(sim_size = sim_size, 
                    est_Paras = est_Paras,
                    per_DE=0.05, 
                    DE_Method = "MAST", 
                    Cell_Type = "PW") 
# Note, using our previous developed tool SC2P is faster.

packageVersion("POWSC")  # [1] '0.1.0'
help(package="POWSC")
plot.POWSC(pow_rslt, Form="II", Cell_Type = "PW") 
summary.POWSC(pow_rslt, Form="II", Cell_Type = "PW")
#      (0,10] (10,20] (20,40] (40,80] (80,160] (160,Inf]
# 100  0.0171  0.1270  0.2877  0.2740   0.4909    0.6765
# 400  0.3200  0.5373  0.6486  0.7436   0.7959    0.8429
# 1000 0.5534  0.7424  0.8095  0.9067   0.9815    0.9077

Mitochondrion

Spike-in

  • RNA spike-in. An RNA spike-in is an RNA transcript of known sequence and quantity used to calibrate measurements in RNA hybridization assays, such as DNA microarray experiments, RT-qPCR, and RNA-Seq.
  • scran vignette.
    • We perform some cursory quality control to remove cells with low total counts or high spike-in percentages.
    • An alternative approach is to normalize based on the spike-in counts. The idea is that the same quantity of spike-in RNA was added to each cell prior to library preparation. Size factors are computed to scale the counts such that the total coverage of the spike-in transcripts is equal across cells.
    • If we have spike-ins, we can use them to fit the trend (variance-mean relationship) instead.
  • Spike-in gene concentrations are known, normalization model existing technical variation by utilizing the difference between these known values and the values observed after processing. "Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey". Lytal 2020

CPM and glmpca package

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model, glmpca package. All codes are available in github. A talk by Hicks 2021.

scone package: normalization

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq.

Quality control

Doublet

  • scDblFinder
  • scrublet
  • scds
  • scrublet

Downsampling

  • downsampleMatrix() from the scuttle package
    • downsampleMatrix: Downsample a count matrix to a desired proportion
    • Source code of downsampleMatrix
    R> library(scuttle)
R> sce <- mockSCE()
R> summary(colSums(counts(sce)))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 344769  366648  374586  373876  382028  396251 

R> downsampled2 <- downsampleMatrix(counts(sce), 
                                    prop = 10000/colSums(counts(sce)), bycol=TRUE)
R> colSums(downsampled2[, 1:5])
Cell_001 Cell_002 Cell_003 Cell_004 Cell_005 
   10000    10000    10000    10000    10000
  • downsampleReads() from the DropletUtils package which imports the scuttle package. 
    R> library(DropletUtils)
# Mocking up some 10X HDF5-formatted data.
R> out <- DropletUtils:::simBasicMolInfo(tempfile())
R> a <- read10xMolInfo(out)
R> names(a)
[1] "data"  "genes"
R> dim(a$data)
[1] 9508    5
R> str(a$genes)
 chr [1:20] "ENSG1" "ENSG2" "ENSG3" "ENSG4" "ENSG5" "ENSG6" "ENSG7" "ENSG8" ...
R> a$data[1:3, ]
DataFrame with 3 rows and 5 columns
         cell       umi gem_group      gene     reads
  <character> <integer> <integer> <integer> <integer>
1        GACT    781899         1         9        10
2        CAGC    747670         1         9         9
3        GTAC    614190         1         6        10
R> unique(a$data$gene)
 [1]  9  6 15  7 12 13  1 20  4  8  3 14 17 19 18 16  2  5 10 11
R> length(unique(a$data$gene))   # 20 genes
[1] 20
R> length(unique(a$data$cell))   # 256 cells
[1] 256

# Downsampling by the reads.
R> b<- downsampleReads(out, barcode.length=4, prop=0.5)
R> dim(b)
[1]  20 256
R> b[1:3, 1:5]
3 x 5 sparse Matrix of class "dgCMatrix"
      AAAA-1 AAAC-1 AAAG-1 AAAT-1 AACA-1
ENSG1      .      1      .      1      5
ENSG2      1      1      1      4      .
ENSG3      1      1      .      1      4
R> class(b)
[1] "dgCMatrix"
attr(,"package")
[1] "Matrix"
R> 4^4   # ATCG for each character of the barcode
[1] 256
R> 20*256  # So the combinations of (cell, gene) are not unique
[1] 5120
R> length(unique(a$data$umi)) # So UMIs are unique
[1] 9508
R> length(unique(a$data$gem_group))
[1] 1

    Change prop parameter. Notice the result of downsampling reads does not change much. 

    R> b1 <- downsampleReads(out, barcode.length=4, prop=.9999)
R> colSums(b1)[1:5]
AAAA-1 AAAC-1 AAAG-1 AAAT-1 AACA-1 
    41     37     28     36     35 
R> b2 <- downsampleReads(out, barcode.length=4, prop=0.2)
R> colSums(b2)[1:5]
AAAA-1 AAAC-1 AAAG-1 AAAT-1 AACA-1 
    37     31     23     31     28 
R> b5 <- downsampleReads(out, barcode.length=4, prop=0.5)
R> colSums(b5)[1:5]
AAAA-1 AAAC-1 AAAG-1 AAAT-1 AACA-1 
    41     37     28     36     35 
R> sum(b1)
[1] 9508
R> sum(b2)
[1] 8245
R> sum(b5)
[1] 9448

Normalization

  • Introduction to single-cell RNA-seq. If using a 3’ or 5’ droplet-based method, the length of the gene will not affect the analysis because only the 5’ or 3’ end of the transcript is sequenced. However, if using full-length sequencing, the transcript length should be accounted for.
  • Bulk normalization methods
    • DESeq2: median of ratios (MoR) approach
    • edgeR: trimmed mean of M values
  • Count models for normalization of single-cell RNA-seq data. See Single Cell Genomics Day 2021

Cell type deconvolution

Batch correction

  • Question: scRNA-seq and the batch effects. The biggest problem I found is, since you have no idea what's the real data suppose to be.
  • https://www.biostars.org/p/316204/
  • Over-correction can happen.
    • Single-cell RNA-seq Analysis on NIDAP - Tutorial Part 2 40:00 (nih network is required). Groups differ by a single experimental modification (eg a knockoff gene or cells of similar lineage used to determine differentiation pattern). However batch correction is necessary in cases such as spike-in cells where we expect these cells should be clustered together.
  • Mutual nearest neighbors/MNN. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors Haghverdi 2018...
    • One assumption is the batch effect is almost orthogonal to the biological subspace. It also assumes the batch effect variation in much smaller than the biological effect variation between different cell types.
    • It is preferable to perform DE analyses using the uncorrected expression values with blocking on the batch, as discussed in Section 11.4... We suggest limiting the use of per-gene corrected values to visualization, e.g., when coloring points on a t-SNE plot by per-cell expression.
    • Sadly the R code of the paper does not have the file ('MAP2.csv') and the code does not work anymore (e.g. mnnCorrect() does not have $angles component). Check out the mnnpy module which still has an option to return angles.
  • SMNN can eliminate the overcorrection between different cell types & allow that batch effects are non-orthogonal to the biological differences. github source code. Note that the package is only available on github and it depends on the python mnnpy module. 
    library(reticulate)
use_python("/home/rstudio/.conda/envs/sc_env/bin/python")
py_config()  # confirm

library("SMNN")
data("data_SMNN")
dim(data_SMNN$batch1.mat)  # [1] 3000  400
data_SMNN$batch1.mat[1:5, 1:5]
dim(data_SMNN$batch2.mat)  # [1] 3000  500
# Maker genes
markers <- c("Col1a1", "Pdgfra", "Ptprc", "Pecam1")
# Corresponding cell type labels for each marker gene
cluster.info <- c(1, 1, 2, 3)
matched_clusters <- unifiedClusterLabelling(data_SMNN$batch1.mat, 
                                            data_SMNN$batch2.mat, 
                                            features.use = markers, 
                                            cluster.labels = cluster.info, 
                                            min.perc = 0.3)

corrected.results <- SMNNcorrect(batches = list(data_SMNN$batch1.mat, data_SMNN$batch2.mat), 
                                 batch.cluster.labels = matched_clusters, 
                                 k=20, 
                                 sigma=1, 
                                 cos.norm.in=TRUE, 
                                 cos.norm.out=TRUE, 
                                 subset.genes=rownames(data_SMNN$batch2.mat))
# NOTE: subset.genes parameter was added to fix a bug when I'm running the function
# [1] "Data preparation ..."
# Error: C stack usage  555609078676 is too close to the limit

> traceback()
3: py_call_impl(callable, dots$args, dots$keywords)
2: mnnpy$utils$transform_input_data(datas = batches.t, cos_norm_in = cos.norm.in, 
       cos_norm_out = cos.norm.out, var_index = as.character(c(0:ncol(batches.t1))), 
       var_subset = subset.index, n_jobs = n.jobs)
1: SMNNcorrect(batches = list(data_SMNN$batch1.mat, data_SMNN$batch2.mat), 
       batch.cluster.labels = matched_clusters, k = 20, sigma = 1, 
       cos.norm.in = TRUE, cos.norm.out = TRUE, subset.genes = rownames(data_SMNN$batch2.mat)[1:100])
  • https://broadinstitute.github.io/2019_scWorkshop/correcting-batch-effects.html
    • Seurat (CCA): RunMultiCCA() seems not available any more. Check out Integration and Label Transfer in Seurat 3 and Introduction to scRNA-seq integration FindIntegrationAnchors() & IntegrateData(). 
      # Create 2D UMAP DR plot for data before integration
# It can be seen there is a need to do batch effect correction
ifnb2 <- ifnb
ifnb2 <- NormalizeData(ifnb2)
ifnb2 <- FindVariableFeatures(ifnb2, selection.method = "vst", nfeatures = 2000)
ifnb2 <- ScaleData(ifnb2, verbose = FALSE)
ifnb2 <- RunPCA(ifnb2, npcs = 30, verbose = FALSE)
ifnb2 <- RunUMAP(ifnb2, reduction = "pca", dims = 1:30)
ifnb2 <- FindNeighbors(ifnb2, reduction = "pca", dims = 1:30)
ifnb2 <- FindClusters(ifnb2, resolution = 0.5)

# Compared to the 2D plot before and after integration
DimPlot(ifnb2, reduction = "umap", group.by = "stim") # not good, ideally two groups
                     # should be mixed together.
                     # It seems the blue color is on top of the salmon color.
DimPlot(immune.combined, reduction = "umap", group.by = "stim")
                     # Two groups are mixed. 

# Compare the cluster plots before and after integration
DimPlot(ifnb2, reduction = "umap", split.by = "stim") # not good, ideally the cluster
                     # pattern should be similar in both groups 
DimPlot(immune.combined, reduction = "umap", split.by = "stim") #good, the cluster
                     # patterns are similar in both groups

      FindConservedMarkers() and [email protected], ?FindConservedMarkers. ident.1 & ident.2 are used to define cells from the @active.ident component. For example, we can it to find genes that are conserved markers irrespective of stimulation/group condition in a certain cluster. Together with FeaturePlot() we can see the gene expression in 2D space (one plot per feature). The DotPlot() function with the split.by parameter can be useful for viewing conserved cell type markers across conditions 

      table([email protected]$stim, [email protected])

table(pbmc_small$groups, [email protected])
    • liger LIGER (NMF): liger:: optimizeALS(). Note that in order to use the R package, it is necessary to install hdf5 or libhdf5-dev library. Vignettes are available on github source but not on CRAN package.
    • Harmony: RunHarmony()

Clustering

# after I upgrade R from 4.0.x to 4.1.x
R> library(DuoClustering2018)
snapshotDate(): 2021-05-18
Warning message:
DEPRECATION: As of ExperimentHub (>1.17.2), default caching location has changed.
  Problematic cache: /Users/XXX/Library/Caches/ExperimentHub
  See https://bioconductor.org/packages/devel/bioc/vignettes/ExperimentHub/inst/doc/ExperimentHub.html#default-caching-location-update

NMF

cNMF

https://github.com/dylkot/cNMF/

seurat::BuildClusterTree

Model

DE analysis

GSEA

AddModuleScore

library(Seurat)
data("pbmc_small")
cd_features <- list(c('CD79B', 'CD79A', 'CD19', 'CD180', 'CD200', 'CD3D',
  'CD2', 'CD3E', 'CD7', 'CD8A', 'CD14', 'CD1C', 'CD68', 'CD9', 'CD247'))
pbmc_small <- AddModuleScore(
  object = pbmc_small,
  features = cd_features,
  ctrl = 5,
  name = 'CD_Features'
)
head(pbmc_small, 3)

                  orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8
ATGCCAGAACGACT SeuratProject         70           47               0
CATGGCCTGTGCAT SeuratProject         85           52               0
GAACCTGATGAACC SeuratProject         87           50               1
               letter.idents groups RNA_snn_res.1 CD_Features1
ATGCCAGAACGACT             A     g2             0    0.4420928
CATGGCCTGTGCAT             A     g1             0    0.4285719
GAACCTGATGAACC             B     g2             0    0.9217770

Tweedieverse

Cell type annotation

Cell-level metadata

Cell-level metadata are indispensable for documenting single-cell sequencing datasets Puntambekar 2021. Shiny app. Source code.

GSE137710 is a good example.

Cell subtypes

Single-cell RNA sequencing reveals specific cell subtypes that influence survival and determine molecular subtype classification of ovarian cancers

Some cell types

Integrating datasets

scanpy python package

Trajectory/pseudotime analysis

simulation

splatSimulatePaths() from splatter

monocle package for pseudotime analysis

sincell package

SCell

SCell – integrated analysis of single-cell RNA-seq data

GEVM

Detection of high variability in gene expression from single-cell RNA-seq profiling. Two mouse scRNA-seq data sets were obtained from Gene Expression Omnibus (GSE65525 and GSE60361).

NMFEM

Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization

Scater

Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

NDRindex

NDRindex: a method for the quality assessment of single-cell RNA-Seq preprocessing data

DEsingle

DEsingle – detecting three types of differential expression in single-cell RNA-seq data

totalVI

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Copy number by infercnv

Simpler plot 

data(infercnv_data_example) # data.frame 8252 x 20, genes x samples
data(infercnv_annots_example) # data.frame 20 x 1, 10 normal and 10 tumor
data(infercnv_genes_example) # data.frame 10338 x 3
infercnv_genes_example[1:3, ]
#             V2     V3     V4
# WASH7P    chr1  14363  29806
# LINC00115 chr1 761586 762902
# NOC2L     chr1 879584 894689
infercnv_object_example <- infercnv::CreateInfercnvObject(raw_counts_matrix=infercnv_data_example, 
                                                          gene_order_file=infercnv_genes_example,
                                                          annotations_file=infercnv_annots_example,
                                                          ref_group_names=c("normal"))

infercnv_object_example <- infercnv::run(infercnv_object_example,
                                         cutoff=1,
                                         out_dir=tempfile(), 
                                         cluster_by_groups=TRUE, 
                                         denoise=TRUE,
                                         HMM=FALSE,
                                         num_threads=2)
system("ls /var/folders/2q/slryb0rx4tj97t66v7l6pwvr_z6g3s/T//RtmpEuGgVy/file182d24b626381")
# 01_incoming_data.infercnv_obj                     infercnv.21_denoised.observations_dendrogram.txt
# 02_reduced_by_cutoff.infercnv_obj                 infercnv.21_denoised.png
# 03_normalized_by_depth.infercnv_obj               infercnv.21_denoised.references.txt
# 04_logtransformed.infercnv_obj                    infercnv.heatmap_thresholds.txt
# 08_remove_ref_avg_from_obs_logFC.infercnv_obj     infercnv.observation_groupings.txt
# 09_apply_max_centered_expr_threshold.infercnv_obj infercnv.observations.txt
# 10_smoothed_by_chr.infercnv_obj                   infercnv.observations_dendrogram.txt
# 11_recentered_cells_by_chr.infercnv_obj           infercnv.png
# 12_remove_ref_avg_from_obs_adjust.infercnv_obj    infercnv.preliminary.heatmap_thresholds.txt
# 14_invert_log_transform.infercnv_obj              infercnv.preliminary.observation_groupings.txt
# 15_no_subclustering.infercnv_obj                  infercnv.preliminary.observations.txt
# 21_denoise.NF_NA.SD_1.5.NL_FALSE.infercnv_obj     infercnv.preliminary.observations_dendrogram.txt
# expr.infercnv.21_denoised.dat                     infercnv.preliminary.png
# expr.infercnv.dat                                 infercnv.preliminary.references.txt
# expr.infercnv.preliminary.dat                     infercnv.references.txt
# infercnv.21_denoised.heatmap_thresholds.txt       preliminary.infercnv_obj
# infercnv.21_denoised.observation_groupings.txt    run.final.infercnv_obj
# infercnv.21_denoised.observations.txt

# Optional, what's the purpose of this plot function
#           when the last command already generated the heatmap?
plot_cnv(infercnv_object_example,
         out_dir=tempfile(),
         obs_title="Observations (Cells)",
         ref_title="References (Cells)",
         cluster_by_groups=TRUE,
         x.center=1,
         x.range="auto",
         hclust_method='ward.D',
         color_safe_pal=FALSE,
         output_filename="infercnv",
         output_format="png",
         png_res=300,
         dynamic_resize=0)
# ...
list.files("/var/folders/2q/slryb0rx4tj97t66v7l6pwvr_z6g3s/T//RtmpEuGgVy/file182d25df0c4ed")
# [1] "infercnv.heatmap_thresholds.txt"      "infercnv.observation_groupings.txt"  
# [3] "infercnv.observations_dendrogram.txt" "infercnv.observations.txt"           
# [5] "infercnv.png"                         "infercnv.references.txt"

More complicated plot 

infercnv_obj = CreateInfercnvObject(
  raw_counts_matrix=system.file("extdata", "oligodendroglioma_expression_downsampled.counts.matrix.gz", package = "infercnv"),
  annotations_file=system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv"),
  delim="\t", gene_order_file=system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv"),
  ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)")) 

infercnv_obj = infercnv::run(infercnv_obj,
                             cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
                             out_dir=tempfile(), 
                             cluster_by_groups=TRUE, 
                             denoise=TRUE,
                             HMM=TRUE) # HMM=TRUE will increase lots of computation
# 	STEP 1: incoming data
# 	STEP 02: Removing lowly expressed genes
# INFO [2021-08-03 13:38:51] Removing 264 genes from matrix as below mean expr threshold: 1
# INFO [2021-08-03 13:38:51] validating infercnv_obj
# INFO [2021-08-03 13:38:51] There are 5049 genes and 184 cells remaining in the expr matrix.
# 	STEP 03: normalization by sequencing depth
# ...
# (Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : singular gradient
# ), couldn't fit logistic, but no worries, going to use a spline
# (Error in nls(y ~ .logistic_midpt_slope(x, midpt = x0, slope = k), data = df, : singular gradient
# ), couldn't fit logistic, but no worries, going to use a spline
# Error in if (runif(1) <= padj) { : missing value where TRUE/FALSE needed

x <- read.delim(system.file("extdata", "oligodendroglioma_annotations_downsampled.txt", package = "infercnv"), header=F)
dim(x)  #  [1] 184   2;   184 cells only
unique(x[,2])  # 6 groups. Two of them represent references cells and the other 4 are the obs cells.
# [1] "Microglia/Macrophage"             "Oligodendrocytes (non-malignant)"
# [3] "malignant_MGH36"                  "malignant_MGH53"
# [5] "malignant_97"                     "malignant_93"
x[1:3, ]
#             V1                   V2
# 1 MGH54_P2_C12 Microglia/Macrophage
# 2 MGH36_P6_F03 Microglia/Macrophage
# 3 MGH53_P4_H08 Microglia/Macrophage

xx <- read.delim(system.file("extdata", "gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt", package = "infercnv"), header=F)
dim(xx)  #  [1] 10338     4
xx[1:2, ]
#          V1   V2     V3     V4
# 1    WASH7P chr1  14363  29806
# 2 LINC00115 chr1 761586 762902

Infercnv.png

GPU

NVIDIA Researchers Use AI to Spot Active Areas in Cell DNA

Single Cells and Single Nuclei

Webinars

SITC-NCI Computational Immuno-oncology Webinar Series 2021

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