ScRNA

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Resource

Public data

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

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

Preprocess

Cell ranger

  • 10X Genomics
  • 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).
  • https://hpc.nih.gov/apps/cellranger.html
    • 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.
    • 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
├── 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
├── raw_feature_bc_matrix.h5
└── web_summary.html

31 directories, 41 files

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 object

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

Azimuth

Azimuth - App for reference-based single-cell analysis

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

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.

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()

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

Cell type deconvolution

Model

DE analysis

GSEA

twosigma package and the paper TWO-SIGMA-G: A New Competitive Gene Set Testing Framework for scRNA-seq Data Accounting for Inter-Gene and Cell-Cell Correlation Burden et ap.

Cell type annotation

scanpy python package

monocle package

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

Splatter: Simulation Of Single-Cell RNA Sequencing Data

http://www.biorxiv.org/content/early/2017/07/24/133173?rss=1

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

GPU

NVIDIA Researchers Use AI to Spot Active Areas in Cell DNA

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