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New Release v1.4.0 #120

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merged 12 commits into from
Mar 9, 2021
Merged

New Release v1.4.0 #120

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3805704
Update ATACRunSeurat with Signac functions, and update Incorporate wi…
DongqingSun Feb 23, 2021
3968083
Update DESCRIPTION and NAMESPACE for changes in ATAC functions
DongqingSun Feb 23, 2021
bdefd82
Update packages in conda meta file
DongqingSun Feb 23, 2021
ef0cdc2
Merge pull request #9 from DongqingSun96/fix/Seurat4
baigal628 Feb 28, 2021
078b737
updated lisa ref
baigal628 Feb 28, 2021
155627d
Update Package version
baigal628 Mar 1, 2021
f772101
add outdir path to the MAESTRO R functions
baigal628 Mar 5, 2021
7b73166
add outdir variables to MAESTRO R
baigal628 Mar 5, 2021
7ce27ed
update docs as 1.4.0
baigal628 Mar 5, 2021
2481dc0
update docs as 1.4.0
baigal628 Mar 5, 2021
8ffc85d
update docs as 1.4.0
baigal628 Mar 5, 2021
f561ee1
fix bugs in path change
baigal628 Mar 6, 2021
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add outdir path to the MAESTRO R functions
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@baigal628
baigal628 committed Mar 5, 2021
commit f7721010cd5ef500e385e6d0466ca624d0d3cac8
6 changes: 3 additions & 3 deletions R/RNAAnnotateCelltype.R
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Expand Up @@ -32,7 +32,7 @@
#' @importFrom ggplot2 ggsave
#' @export

RNAAnnotateCelltype <- function(RNA, genes, signatures = "human.immune.CIBERSORT", min.score = 0, orig.ident = NULL){
RNAAnnotateCelltype <- function(RNA, genes, signatures = "human.immune.CIBERSORT", min.score = 0, orig.ident = NULL, outdir = "."){
if(is.null(orig.ident)){
if(class(signatures) == "character"){
signatures_name = signatures
Expand Down Expand Up @@ -84,11 +84,11 @@ RNAAnnotateCelltype <- function(RNA, genes, signatures = "human.immune.CIBERSORT
from = current.cluster.ids, to = new.cluster.ids)
}
p = DimPlot(object = RNA, label = TRUE, pt.size = 0.2, group.by = "assign.ident", label.size = 3, repel = T)
ggsave(paste0(RNA@project.name, "_annotated.png"), p, width=6, height=4)}
ggsave(file.path(outdir, paste0(RNA@project.name, "_annotated.png")), p, width=6, height=4)}
else{
RNA$assign.ident <- orig.ident
p = DimPlot(object = RNA, label = TRUE, pt.size = 0.2, group.by = "assign.ident", label.size = 3, repel = T)
ggsave(file.path(paste0(RNA@project.name, "_original.png")), p, width=5.5, height=4)}
ggsave(file.path(outdir, paste0(RNA@project.name, "_original.png")), p, width=5.5, height=4)}

return(RNA)
}
4 changes: 2 additions & 2 deletions R/RNAAnnotateTranscriptionFactor.R
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Expand Up @@ -39,7 +39,7 @@


RNAAnnotateTranscriptionFactor <- function(RNA, genes, cluster = NULL, project = RNA@project.name, lisa.mode = "multi",
method = "LISA", lisa.path = "lisa", rabit.path, organism = "GRCh38", top.tf = 10)
method = "LISA", lisa.path = "lisa", rabit.path, organism = "GRCh38", top.tf = 10, outdir=".")
{
if(organism == "GRCh38"){
org = "hsa"
Expand Down Expand Up @@ -221,7 +221,7 @@ RunLISAMulti <- function(genes, project, organism = "GRCh38", lisa.path = lisa.p

cluster_markers_list <- split(genes, genes$cluster)

outputDir <- paste0(project, ".lisa")
outputDir <- file.path(outdir, paste0(project, ".lisa"))
if (!file.exists(outputDir)) dir.create(path=outputDir)

for(i in names(cluster_markers_list))
Expand Down
30 changes: 15 additions & 15 deletions R/RNARunSeurat.R
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Expand Up @@ -49,31 +49,31 @@
#' @export

RNARunSeurat <- function(inputMat, type = "matrix", project = "MAESTRO.scRNA.Seurat", orig.ident = NULL, min.c = 10, min.g = 200, mito = FALSE,
variable.genes = 2000, organism = "GRCh38", dims.use = 1:15, cluster.res = 0.6, only.pos = FALSE, genes.test.use = "presto",
mito.cutoff = 0.2, variable.genes = 2000, organism = "GRCh38", dims.use = 1:15, cluster.res = 0.6, only.pos = FALSE, genes.test.use = "presto",
genes.cutoff = 1E-5, genes.pct = 0.1, genes.logfc = 0.25,
runpca.agrs = list(), findneighbors.args = list(),
findclusters.args = list(), ...)
findclusters.args = list(), outdir = ".", ...)
{
if(type == "matrix"){SeuratObj <- CreateSeuratObject(inputMat, project = project, min.cells = min.c, min.features = min.g)}
if(type == "object"){SeuratObj <- inputMat}

#=========Mitochondria and Spike-in========
if(mito){
message("Check the mitochondria and spike-in percentage ...")
if(organism=="GRCh38"){
mito.genes <- grep("^MT-", rownames(GetAssayData(object = SeuratObj)), value = TRUE)
ercc.genes <- grep("^ERCC", rownames(GetAssayData(object = SeuratObj)), value = TRUE)}
mito.genes <- grep("^MT-", rownames(GetAssayData(object = SeuratObj)), value = TRUE)
ercc.genes <- grep("^ERCC", rownames(GetAssayData(object = SeuratObj)), value = TRUE)}
else{
mito.genes <- grep("^mt-", rownames(GetAssayData(object = SeuratObj)), value = TRUE)
ercc.genes <- grep("^ercc", rownames(GetAssayData(object = SeuratObj)), value = TRUE)}
mito.genes <- grep("^mt-", rownames(GetAssayData(object = SeuratObj)), value = TRUE)
ercc.genes <- grep("^ercc", rownames(GetAssayData(object = SeuratObj)), value = TRUE)}
percent.mito <- Matrix::colSums(GetAssayData(object = SeuratObj)[mito.genes, ])/Matrix::colSums(GetAssayData(object = SeuratObj))
percent.ercc <- Matrix::colSums(GetAssayData(object = SeuratObj)[ercc.genes, ])/Matrix::colSums(GetAssayData(object = SeuratObj))
SeuratObj$percent.mito <- percent.mito
SeuratObj$percent.ercc <- percent.ercc
p1 = VlnPlot(SeuratObj, c("percent.mito","percent.ercc"), ncol = 2)
ggsave(paste0(SeuratObj@project.name, ".spikein.png"), p1, width=6, height=4.5)
ggsave(file.path(outdir, paste0(SeuratObj@project.name, ".spikein.png")), p1, width=6, height=4.5)

SeuratObj <- subset(x = SeuratObj, subset = percent.mito < 0.05)
SeuratObj <- subset(x = SeuratObj, subset = percent.mito < mito.cutoff)
SeuratObj <- subset(x = SeuratObj, subset = percent.ercc < 0.05)
vars.to.regress = c("nCount_RNA","percent.mito","percent.ercc")}
else{
Expand All @@ -90,7 +90,7 @@ RNARunSeurat <- function(inputMat, type = "matrix", project = "MAESTRO.scRNA.Seu
SeuratObj <- fastDoCall("RunPCA", c(object = SeuratObj, features = VariableFeatures(SeuratObj), runpca.agrs))
# SeuratObj <- RunPCA(object = SeuratObj, features = VariableFeatures(SeuratObj))
p2 = ElbowPlot(object = SeuratObj, ndims = SeuratObj@commands$RunPCA.RNA@params$npcs)
ggsave(file.path(paste0(SeuratObj@project.name, "_PCElbowPlot.png")), p2, width=10, height=4)
ggsave(file.path(outdir, paste0(SeuratObj@project.name, "_PCElbowPlot.png")), p2, width=10, height=4)

#=========UMAP===========
message("UMAP analysis ...")
Expand All @@ -100,17 +100,17 @@ RNARunSeurat <- function(inputMat, type = "matrix", project = "MAESTRO.scRNA.Seu
# SeuratObj <- FindNeighbors(object = SeuratObj, reduction = "pca", dims = dims.use)
# SeuratObj <- FindClusters(object = SeuratObj, resolution = cluster.res)
p3 = DimPlot(object = SeuratObj, label = TRUE, pt.size = 0.2)
ggsave(file.path(paste0(SeuratObj@project.name, "_cluster.png")), p3, width=5, height=4)
ggsave(file.path(outdir,paste0(SeuratObj@project.name, "_cluster.png")), p3, width=5, height=4)
if(!is.null(orig.ident)){
SeuratObj$orig.ident <- orig.ident
p4 = DimPlot(object = SeuratObj, label = TRUE, pt.size = 0.2, group.by = "orig.ident", label.size = 3)
ggsave(file.path(paste0(SeuratObj@project.name, "_original.png")), p4, width=5, height=4)}

ggsave(file.path(outdir, paste0(SeuratObj@project.name, "_original.png")), p4, width=5, height=4)}
#=========DE analysis===========
message("Identify cluster specific genes ...")
cluster.genes <- FindAllMarkersMAESTRO(object = SeuratObj, min.pct = genes.pct, logfc.threshold = genes.logfc, test.use = genes.test.use, only.pos = only.pos)
cluster.genes <- cluster.genes[cluster.genes$p_val_adj<genes.cutoff, ]
write.table(cluster.genes, paste0(SeuratObj@project.name, "_DiffGenes.tsv"), quote = F, sep = "\t")

write.table(cluster.genes, file.path(outdir, paste0(SeuratObj@project.name, "_DiffGenes.tsv")), quote = F, sep = "\t")
return(list(RNA=SeuratObj, genes=cluster.genes))
}