RunSeurat: Run Seurat Pipeline
In Theob0t/scEasyPip: Automated Seurat Pipeline for scRNA-seq analysis

This function run the following steps of the Seurat pipeline :

Create Seurat Object
QC
Normalization
Dimensional Reduction
Clustering
Visualization
Differential Gene Expression

RunSeurat(
  data.dir = getwd(),
  object = NULL,
  output.dir = getwd(),
  min.features = 200,
  min.cells = 3,
  project.name = "project_name",
  mt.pattern = "^mt-",
  max.percent.mt = 15,
  max.features = NULL,
  max.nCount = NULL,
  sctransform = FALSE,
  logtransform = TRUE,
  no.plot = FALSE,
  vars.to.regress = c("percent.mt", "nCount_RNA"),
  ndims = 50,
  dims = 1:35,
  resolution = seq(0.1, 1, 0.1),
  cellcycle = TRUE,
  genes.FeaturePlot = NULL,
  genes.DotPlot = NULL,
  find.all.markers = TRUE,
  only.pos = TRUE,
  min.pct = 0.25,
  logfc.threshold = 0.25,
  test.use = "MAST",
  save.rds = TRUE,
  integrated.assay = FALSE,
  tf.activity = FALSE,
  species = "mouse",
  dorothea.confidence = c("A", "B", "C"),
  ...
)

`data.dir`	Directory containing the matrix.mtx, genes.tsv (or features.tsv), and barcodes.tsv files provided by 10X. A vector or named vector can be given in order to load several data directories. If a named vector is given, the cell barcode names will be prefixed with the name.
`object`	A Seurat object
`output.dir`	Path to the destination folder of saved files
`min.features`	Include cells where at least this many features are detected.
`min.cells`	Include features detected in at least this many cells. Will subset the counts matrix as well. To reintroduce excluded features, create a new object with a lower cutoff.
`project.name`	Name of the project/object used for titles in plots
`mt.pattern`	Regex pattern of the mitochondrial genes ('^MT-' or '^mt-')
`max.percent.mt`	Mitochondrial counts threshold (default set to 15)
`max.features`	Maximum number of gene per cell (default 99-quantile)
`max.nCount`	Maximum number of reads per cell (default 99-quantile)
`sctransform`	If set, use SCTransform normalization
`logtransform`	Run the default log-normalization from Seurat
`no.plot`	If set, run the pipeline without saving the plots
`vars.to.regress`	Variables to regress out in a second non-regularized linear regression. For example, percent.mito. Default is NULL
`ndims`	Number of dimensions to plot standard deviation for
`dims`	Which dimensions to use as input features, used only if `features` is NULL
`resolution`	Value of the resolution parameter, use a value above (below) 1.0 if you want to obtain a larger (smaller) number of communities.
`cellcycle`	Run CellCycle Scoring from Seurat
`genes.FeaturePlot`	A list of genes for Seurat FeaturePlot
`genes.DotPlot`	A list of genes for Seurat DotPlot
`find.all.markers`	Run Differential Gene Expression
`only.pos`	Only return positive markers (FALSE by default)
`min.pct`	only test genes that are detected in a minimum fraction of min.pct cells in either of the two populations. Meant to speed up the function by not testing genes that are very infrequently expressed. Default is 0.1
`logfc.threshold`	Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals.
`test.use`	Denotes which test to use. Available options are: "wilcox" : Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test (default) "bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013) "roc" : Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes. "t" : Identify differentially expressed genes between two groups of cells using the Student's t-test. "negbinom" : Identifies differentially expressed genes between two groups of cells using a negative binomial generalized linear model. Use only for UMI-based datasets "poisson" : Identifies differentially expressed genes between two groups of cells using a poisson generalized linear model. Use only for UMI-based datasets "LR" : Uses a logistic regression framework to determine differentially expressed genes. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test. "MAST" : Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing. "DESeq2" : Identifies differentially expressed genes between two groups of cells based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).This test does not support pre-filtering of genes based on average difference (or percent detection rate) between cell groups. However, genes may be pre-filtered based on their minimum detection rate (min.pct) across both cell groups. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html
`save.rds`	Save final Seurat object in RDS format (default set to True)
`integrated.assay`	If set, run the pipeline on the integrated assay
`tf.activity`	Run Seurat pipeline on TF activity
`species`	Mouse or human
`dorothea.confidence`	Confidence levels of regulons
`...`	Arguments passed to `as.sparse`