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Strand Lab
sc-TissueMapper
Commits
672e8547
Commit
672e8547
authored
Feb 27, 2019
by
Gervaise Henry
🤠
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Update run script for musAdPrF
parent
8194e9b0
Changes
1
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32 deletions
+32
-32
r.scripts/sc-TissueMapper_RUN.R
r.scripts/sc-TissueMapper_RUN.R
+32
-32
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r.scripts/sc-TissueMapper_RUN.R
View file @
672e8547
...
...
@@ -27,9 +27,9 @@ scFolders()
sc10x
<-
scLoad
(
p
=
project.name
,
cellranger
=
3
,
ref
=
"mm10"
)
lg
=
50
0
hg
=
3
000
hm
=
0.
05
lg
=
25
0
hg
=
2
000
hm
=
0.
1
results
<-
scQC
(
sc10x
,
lg
=
lg
,
hg
=
hg
,
hm
=
hm
,
sub
=
FALSE
,
sp
=
"mu"
)
sc10x
<-
results
[[
1
]]
counts.cell.raw
<-
results
[[
2
]]
...
...
@@ -41,53 +41,53 @@ rm(lg)
rm
(
hg
)
rm
(
hm
)
results
<-
scCellCycle
(
sc10x
,
sub
=
FALSE
,
sp
=
"mu"
)
sc10x
<-
results
[[
1
]]
genes.s
<-
results
[[
2
]]
genes.g2m
<-
results
[[
3
]]
rm
(
results
)
#
results <- scCellCycle(sc10x,sub=FALSE,sp="mu")
#
sc10x <- results[[1]]
#
genes.s <- results[[2]]
#
genes.g2m <- results[[3]]
#
rm(results)
sc10x.l
<-
list
()
sc10x.l
[[
"
Pd"
]]
<-
subset
(
sc10x
,
subset
=
Donor
==
"Donor
"
)
sc10x.l
[[
"
Pb"
]]
<-
subset
(
sc10x
,
subset
=
BPH
==
"BPH
"
)
sc10x.l
[[
"
Pc"
]]
<-
subset
(
sc10x
,
subset
=
Cancer
==
"Cancer
"
)
sc10x.l
[[
"
mu1"
]]
<-
subset
(
sc10x
,
subset
=
samples
==
"musAd001_PrF
"
)
sc10x.l
[[
"
mu2"
]]
<-
subset
(
sc10x
,
subset
=
samples
==
"musAd002_PrF
"
)
sc10x.l
[[
"
mu3"
]]
<-
subset
(
sc10x
,
subset
=
samples
==
"musAd003_PrF_St
"
)
sc10x
<-
scCCA
(
sc10x.l
)
rm
(
sc10x.l
)
sc10x
@
project.name
<-
project.name
#gc()
sc10x
<-
ScaleData
(
object
=
sc10x
,
vars.to.regress
=
c
(
"nFeature_RNA"
,
"percent.mito"
,
"S.Score"
,
"G2M.Score"
),
do.par
=
TRUE
,
num.cores
=
45
,
verbose
=
FALSE
)
#
sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
#gc()
#
gc()
#
sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
sc10x
<-
ScaleData
(
object
=
sc10x
,
vars.to.regress
=
c
(
"nFeature_RNA"
,
"percent.mito"
),
do.par
=
TRUE
,
num.cores
=
45
,
verbose
=
FALSE
)
#
gc()
results
<-
scPC
(
sc10x
,
pc
=
50
,
hpc
=
0.9
,
file
=
"pre.stress"
,
print
=
"2"
,
cca
=
TRUE
)
sc10x
<-
results
[[
1
]]
pc.use.prestress
<-
results
[[
2
]]
rm
(
results
)
sc10x
<-
scCluster
(
sc10x
,
res
=
0.5
,
red
=
"pca"
,
dim
=
pc.use.prestress
,
print
=
"2"
,
folder
=
"pre.stress"
)
#
sc10x <- scCluster(sc10x,res=0.5,red="pca",dim=pc.use.prestress,print="2",folder="pre.stress")
genes.stress
<-
read_delim
(
"./genesets/genes.deg.Stress.csv"
,
","
,
escape_double
=
FALSE
,
trim_ws
=
TRUE
,
col_names
=
TRUE
)
genes.stress
<-
genes.stress
[
1
]
colnames
(
genes.stress
)
<-
"scDWS.Stress"
#
genes.stress <- read_delim("./genesets/genes.deg.Stress.csv",",",escape_double=FALSE,trim_ws=TRUE,col_names=TRUE)
#
genes.stress <- genes.stress[1]
#
colnames(genes.stress) <- "scDWS.Stress"
results
<-
scScore
(
sc10x
,
score
=
"Stress"
,
geneset
=
as.list
(
genes.stress
),
cut.pt
=
0.75
,
anchor
=
c
(
"EGR1"
,
"FOS"
,
"JUN"
))
sc10x.preStress
<-
results
[[
1
]]
sc10x
<-
results
[[
2
]]
rm
(
results
)
#
results <- scScore(sc10x,score="Stress",geneset=as.list(genes.stress),cut.pt=0.75,anchor=c("EGR1","FOS","JUN"))
#
sc10x.preStress <- results[[1]]
#
sc10x <- results[[2]]
#
rm(results)
#gc()
sc10x
<-
ScaleData
(
object
=
sc10x
,
vars.to.regress
=
c
(
"nFeature_RNA"
,
"percent.mito"
,
"S.Score"
,
"G2M.Score"
),
do.par
=
TRUE
,
num.cores
=
45
,
verbose
=
FALSE
)
#sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
#gc()
#
gc()
#
sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito","S.Score","G2M.Score"),do.par=TRUE,num.cores=45,verbose=FALSE)
#
sc10x <- ScaleData(object=sc10x,vars.to.regress=c("nFeature_RNA","percent.mito"),do.par=TRUE,num.cores=45,verbose=FALSE)
#
gc()
results
<-
scPC
(
sc10x
,
pc
=
50
,
hpc
=
0.9
,
file
=
"post.stress"
,
print
=
"2"
,
cca
=
TRUE
)
sc10x
<-
results
[[
1
]]
pc.use.poststress
<-
results
[[
2
]]
rm
(
results
)
#
results <- scPC(sc10x,pc=50,hpc=0.9,file="post.stress",print="2",cca=TRUE)
#
sc10x <- results[[1]]
#
pc.use.poststress <- results[[2]]
#
rm(results)
res
<-
c
(
seq
(
0.1
,
0.5
,
0.1
),
0.75
,
seq
(
1
,
5
,
1
))
sc10x
<-
scCluster
(
sc10x
,
res
=
res
,
red
=
"pca"
,
dim
=
pc.use.p
ost
stress
,
print
=
"2"
,
folder
=
"ALL"
)
sc10x
<-
scCluster
(
sc10x
,
res
=
res
,
red
=
"pca"
,
dim
=
pc.use.p
re
stress
,
print
=
"2"
,
folder
=
"ALL"
)
save
(
sc10x
,
file
=
paste0
(
"./analysis/sc10x.raw.rda"
))
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