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inst/doc/MARVEL.R
In MARVEL: Revealing Splicing Dynamics at Single-Cell Resolution
## ----echo=FALSE---------------------------------------------------------------
library(knitr)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load MARVEL package
library(MARVEL)
# Load adjunct packages for this tutorial
library(ggplot2)
library(gridExtra)
## ---- eval = FALSE------------------------------------------------------------
# # Load adjunct packages to support additional functionalities
# library(AnnotationDbi) # GO analysis
# library(clusterProfiler)
# library(org.Hs.eg.db)
# library(org.Mm.eg.db)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load adjunct packages to support additional functionalities
library(plyr) # General data processing
library(ggrepel) # General plotting
library(parallel) # To enable multi-thread during RI PSI computation
library(textclean) # AFE, ALE detection
library(fitdistrplus) # Modality analysis: Fit beta distribution
library(FactoMineR) # PCA: Reduce dimension
library(factoextra) # PCA: Retrieve eigenvalues
library(kSamples) # Anderson-Darling (AD) statistical test
library(twosamples) # D Test Statistic (DTS) statistical test
library(stringr) # Plot GO results
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load saved MARVEL object
marvel.demo <- readRDS(system.file("extdata/data", "marvel.demo.rds", package="MARVEL"))
class(marvel.demo)
## ---- message=FALSE, warning=FALSE--------------------------------------------
SplicePheno <- marvel.demo$SplicePheno
head(SplicePheno)
## ---- eval = FALSE------------------------------------------------------------
# # STAR in 1st pass mode
# STAR --runThreadN 16 \
# --genomeDir GRCh38_GENCODE_genome_STAR_indexed \
# --readFilesCommand zcat \
# --readFilesIn ERR1562083_1_val_1.fq.gz ERR1562083_2_val_2.fq.gz \
# --outFileNamePrefix SJ/ERR1562083. \
# --outSAMtype None
#
# # STAR in 2nd pass mode
# STAR --runThreadN 16 \
# --genomeDir GRCh38_GENCODE_genome_STAR_indexed \
# --readFilesCommand zcat \
# --readFilesIn ERR1562083_1_val_1.fq.gz ERR1562083_2_val_2.fq.gz \
# --outFileNamePrefix ERR1562083. \
# --sjdbFileChrStartEnd SJ/*SJ.out.tab \
# --outSAMtype BAM SortedByCoordinate \
# --outSAMattributes NH HI AS nM XS \
# --quantMode TranscriptomeSAM
## ---- message=FALSE, warning=FALSE--------------------------------------------
SpliceJunction <- marvel.demo$SpliceJunction
SpliceJunction[1:5,1:5]
## ---- eval = FALSE------------------------------------------------------------
# rmats \
# --b1 path_to_BAM_sample_1.txt \
# --b2 path_to_BAM_sample_2.txt \
# --gtf gencode.v31.annotation.gtf \
# --od rMATS/ \
# --tmp rMATS/ \
# -t paired \
# --readLength 125 \
# --variable-read-length \
# --nthread 8 \
# --statoff
## ---- message=FALSE, warning=FALSE--------------------------------------------
SpliceFeature <-marvel.demo$SpliceFeature
lapply(SpliceFeature, head)
## ---- eval = FALSE------------------------------------------------------------
# bedtools coverage \
# -g GRCh38.primary_assembly.genome_bedtools.txt \
# -split \
# -sorted \
# -a RI_Coordinates.bed \
# -b ERR1562083.Aligned.sortedByCoord.out.bam > \
# ERR1562083.txt \
# -d
## ---- message=FALSE, warning=FALSE--------------------------------------------
IntronCounts <- marvel.demo$IntronCounts
IntronCounts[1:5,1:5]
## ---- eval = FALSE------------------------------------------------------------
# rsem-calculate-expression --bam \
# --paired-end \
# -p 8 \
# ERR1562083.Aligned.toTranscriptome.out.bam \
# GRCh38_GENCODE_genome_RSEM_indexed/gencode.v31 \
# ERR1562083
## ---- message=FALSE, warning=FALSE--------------------------------------------
Exp <- marvel.demo$Exp
Exp[1:5,1:5]
## ---- message=FALSE, warning=FALSE--------------------------------------------
GeneFeature <- marvel.demo$GeneFeature
head(GeneFeature)
## ---- message=FALSE, warning=FALSE--------------------------------------------
marvel <- CreateMarvelObject(SpliceJunction=SpliceJunction,
SplicePheno=SplicePheno,
SpliceFeature=SpliceFeature,
IntronCounts=IntronCounts,
GeneFeature=GeneFeature,
Exp=Exp
)
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "PSI_Validation.png", package="MARVEL"))
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "PSI_Computation.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Check splicing junction data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="SJ")
## ---- eval = FALSE------------------------------------------------------------
# # Validate, filter, compute SE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# UnevenCoverageMultiplier=10,
# EventType="SE"
# )
#
# # Validate, filter, compute MXE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# UnevenCoverageMultiplier=10,
# EventType="MXE"
# )
#
# # Validate, filter, compute RI splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="RI",
# thread=4
# )
#
# # Validate, filter, compute A5SS splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="A5SS"
# )
#
# # Validate, filter, compute A3SS splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="A3SS"
# )
#
# # Validate, filter, compute AFE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="AFE"
# )
#
# # Validate, filter, compute ALE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="ALE"
# )
## ---- message=FALSE, warning=FALSE--------------------------------------------
marvel.demo <- TransformExpValues(MarvelObject=marvel.demo,
offset=1,
transformation="log2",
threshold.lower=1
)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Check splicing data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="splicing")
# Check gene data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="gene")
# Cross-check splicing and gene data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="splicing and gene")
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample ids
sample.ids <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Tabulate expressed events
marvel.demo <- CountEvents(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5
)
# Output (1): Plot
marvel.demo$N.Events$Plot
# Output (2): Table
marvel.demo$N.Events$Table
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample ids
sample.ids <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Tabulate expressed events
marvel.demo <- CountEvents(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5
)
# Output (1): Plot
marvel.demo$N.Events$Plot
# Output (2): Table
marvel.demo$N.Events$Table
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "Modality.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample IDs
sample.ids <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Assign modality
marvel.demo <- AssignModality(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5,
seed=1
)
marvel.demo$Modality$Results[1:5, c("tran_id", "event_type", "gene_id", "gene_short_name", "modality.bimodal.adj")]
# Tabulate modality proportion (overall)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=FALSE
)
marvel.demo$Modality$Prop$DoughnutChart$Plot
marvel.demo$Modality$Prop$DoughnutChart$Table
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Tabulate modality proportion (by event type)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=TRUE,
prop.test="fisher",
prop.adj="fdr",
xlabels.size=8
)
marvel.demo$Modality$Prop$BarChart$Plot
head(marvel.demo$Modality$Prop$BarChart$Table)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample IDs
sample.ids <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Assign modality
marvel.demo <- AssignModality(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5,
seed=1
)
marvel.demo$Modality$Results[1:5, c("tran_id", "event_type", "gene_id", "gene_short_name", "modality.bimodal.adj")]
# Tabulate modality proportion (overall)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=FALSE
)
marvel.demo$Modality$Prop$DoughnutChart$Plot
marvel.demo$Modality$Prop$DoughnutChart$Table
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Tabulate modality proportion (by event type)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=TRUE,
prop.test="fisher",
prop.adj="fdr",
xlabels.size=8
)
marvel.demo$Modality$Prop$BarChart$Plot
head(marvel.demo$Modality$Prop$BarChart$Table)
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "DE.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Define cell groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Cell group 1 (reference)
cell.group.g1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Cell group 2
cell.group.g2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# DE analysis
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
min.cells=3,
method="t.test",
method.adjust="fdr",
level="gene",
show.progress=FALSE
)
marvel.demo$DE$Exp$Table[1:5, ]
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot DE results
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
pval=0.10,
log2fc=0.5,
point.size=0.1,
level="gene.global",
anno=FALSE
)
marvel.demo$DE$Exp.Global$Plot
marvel.demo$DE$Exp.Global$Summary
head(marvel.demo$DE$Exp.Global$Table[,c("gene_id", "gene_short_name", "sig")])
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot DE results with annotation of selected genes
# Retrieve DE output table
results <- marvel.demo$DE$Exp$Table
# Retrieve top genes
index <- which(results$log2fc > 2 | results$log2fc < -2)
gene_short_names <- results[index, "gene_short_name"]
# Plot
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
pval=0.10,
log2fc=0.5,
point.size=0.1,
xlabel.size=10,
level="gene.global",
anno=TRUE,
anno.gene_short_name=gene_short_names
)
marvel.demo$DE$Exp.Global$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
min.cells=5,
method=c("ad", "dts"),
method.adjust="fdr",
level="splicing",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "ALE", "AFE"),
show.progress=FALSE
)
head(marvel.demo$DE$PSI$Table[["ad"]])
head(marvel.demo$DE$PSI$Table[["dts"]])
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method="ad",
pval=0.10,
level="splicing.distance",
anno=TRUE,
anno.tran_id=marvel.demo$DE$PSI$Table[["ad"]]$tran_id[c(1:10)]
)
marvel.demo$DE$PSI$Plot[["ad"]]
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
psi.method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
method.de.gene="t.test",
method.adjust.de.gene="fdr",
downsample=FALSE,
show.progress=FALSE,
level="gene.spliced"
)
head(marvel.demo$DE$Exp.Spliced$Table)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot: No annotation
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5,
point.size=0.1,
xlabel.size=8,
level="gene.spliced",
anno=FALSE
)
marvel.demo$DE$Exp.Spliced$Plot
marvel.demo$DE$Exp.Spliced$Summary
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot: Annotate top genes
results <- marvel.demo$DE$Exp.Spliced$Table
index <- which((results$log2fc > 2 | results$log2fc < -2) & -log10(results$p.val.adj) > 1)
gene_short_names <- results[index, "gene_short_name"]
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5,
point.size=0.1,
xlabel.size=8,
level="gene.spliced",
anno=TRUE,
anno.gene_short_name=gene_short_names
)
marvel.demo$DE$Exp.Spliced$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Define sample groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Retrieve DE genes
# Retrieve DE result table
results.de.exp <- marvel.demo$DE$Exp$Table
# Retrieve relevant gene_ids
index <- which(results.de.exp$p.val.adj < 0.10 & abs(results.de.exp$log2fc) > 0.5)
gene_ids <- results.de.exp[index, "gene_id"]
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=gene_ids,
point.size=2.5,
level="gene"
)
marvel.demo$PCA$Exp$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Retrieve DE tran_ids
method <- c("ad", "dts")
tran_ids.list <- list()
for(i in 1:length(method)) {
results.de.psi <- marvel.demo$DE$PSI$Table[[method[i]]]
index <- which(results.de.psi$p.val.adj < 0.10 & results.de.psi$outlier==FALSE)
tran_ids <- results.de.psi[index, "tran_id"]
tran_ids.list[[i]] <- tran_ids
}
tran_ids <- unique(unlist(tran_ids.list))
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
marvel.demo$PCA$PSI$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Retrieve relevant gene_ids
results.de.exp <- marvel.demo$DE$Exp$Table
index <- which(results.de.exp$p.val.adj < 0.10 & abs(results.de.exp$log2fc) > 0.5)
gene_ids <- results.de.exp[-index, "gene_id"]
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=gene_ids,
point.size=2.5,
level="gene"
)
marvel.demo$PCA$Exp$Plot
## ---- message=FALSE, warning=FALSE, fig.width=7, fig.height=8, fig.align="center"----
# Retrieve non-DE gene_ids
results.de.exp <- marvel.demo$DE$Exp$Table
index <- which(results.de.exp$p.val.adj > 0.10 )
gene_ids <- results.de.exp[, "gene_id"]
# Retrieve tran_ids
df.feature <- do.call(rbind.data.frame, marvel.demo$SpliceFeatureValidated)
df.feature <- df.feature[which(df.feature$gene_id %in% gene_ids), ]
# Reduce dimension: All DE splicing events
tran_ids <- df.feature$tran_id
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.all <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: SE
tran_ids <- df.feature[which(df.feature$event_type=="SE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.se <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: MXE
tran_ids <- df.feature[which(df.feature$event_type=="MXE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.mxe <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: RI
tran_ids <- df.feature[which(df.feature$event_type=="RI"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.ri <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: A5SS
tran_ids <- df.feature[which(df.feature$event_type=="A5SS"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.a5ss <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: A3SS
tran_ids <- df.feature[which(df.feature$event_type=="A3SS"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.a3ss <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: AFE
tran_ids <- df.feature[which(df.feature$event_type=="AFE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.afe <- marvel.demo$PCA$PSI$Plot
# Reduce dimension:
tran_ids <- df.feature[which(df.feature$event_type=="ALE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.ale <- marvel.demo$PCA$PSI$Plot
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.all, plot.se,
plot.mxe, plot.ri,
plot.a5ss, plot.a3ss,
plot.afe, plot.ale,
nrow=4)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Define sample groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Assign modality dynamics
marvel.demo <- ModalityChange(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10)
)
marvel.demo$DE$Modality$Plot
head(marvel.demo$DE$Modality$Table)
marvel.demo$DE$Modality$Plot.Stats
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr4:108620569:108620600|108620656:108620712:+@chr4:108621951:108622024"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr12:110502049:110502117:-@chr12:110499535:110499546:-@chr12:110496012:110496203"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr9:35685269:35685339:-@chr9:35685064:35685139:-@chr9:35684732:35684807:-@chr9:35684488:35684550"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr11:85981129:85981228:-@chr11:85978070:85978093:-@chr11:85976623:85976682"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr17:8383254:8382781|8383157:-@chr17:8382143:8382315"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr17:8383157:8383193|8382781:8383164:-@chr17:8382143:8382315"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr15:24962114:24962209:+@chr15:24967029:24967152:+@chr15:24967932:24968082"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr8:144792587:144792245|144792366:-@chr8:144791992:144792140"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr5:150449703:150449739|150449492:150449696:-@chr5:150447585:150447735"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr12:56725340:56724962|56725263:-@chr12:56724452:56724523"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr10:78037194:78037304:+@chr10:78037439:78037441:+@chr10:78040204:78040225"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr10:78037194:78037304:+@chr10:78037439|78040204:78040225"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
marvel.demo <- IsoSwitch(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5
)
marvel.demo$DE$Cor$Plot
head(marvel.demo$DE$Cor$Table)
marvel.demo$DE$Cor$Plot.Stats
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Define cell groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="CMC2"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr16:80981806:80981877:-@chr16:80980808:80980879|80976003:80976179"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="HNRNPC"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr14:21231072:21230958|21230997:-@chr14:21230319:21230366"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="APOO"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chrX:23840313:23840377:-@chrX:23833353:23833612|23833367:23833510"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="BUB3"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr10:123162612:123162828:+@chr10:123163820:123170467|123165047:123165365"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="AC004086.1"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr12:112409641:112409411|112409587:-@chr12:112408420:112408656"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="ACP1"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr2:271866:271939:+@chr2:272037:272150:+@chr2:272192:272305:+@chr2:275140:275201"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- eval = FALSE------------------------------------------------------------
# marvel.demo <- BioPathways(MarvelObject=marvel.demo,
# method=c("ad", "dts"),
# pval=0.10,
# species="human"
# )
#
# head(marvel.demo$DE$BioPathways$Table)
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Plot top pathways
df <- marvel.demo$DE$BioPathways$Table
go.terms <- df$Description[c(1:10)]
marvel.demo <- BioPathways.Plot(MarvelObject=marvel.demo,
go.terms=go.terms,
y.label.size=10
)
marvel.demo$DE$BioPathways$Plot
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## ----echo=FALSE---------------------------------------------------------------
library(knitr)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load MARVEL package
library(MARVEL)
# Load adjunct packages for this tutorial
library(ggplot2)
library(gridExtra)
## ---- eval = FALSE------------------------------------------------------------
# # Load adjunct packages to support additional functionalities
# library(AnnotationDbi) # GO analysis
# library(clusterProfiler)
# library(org.Hs.eg.db)
# library(org.Mm.eg.db)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load adjunct packages to support additional functionalities
library(plyr) # General data processing
library(ggrepel) # General plotting
library(parallel) # To enable multi-thread during RI PSI computation
library(textclean) # AFE, ALE detection
library(fitdistrplus) # Modality analysis: Fit beta distribution
library(FactoMineR) # PCA: Reduce dimension
library(factoextra) # PCA: Retrieve eigenvalues
library(kSamples) # Anderson-Darling (AD) statistical test
library(twosamples) # D Test Statistic (DTS) statistical test
library(stringr) # Plot GO results
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Load saved MARVEL object
marvel.demo <- readRDS(system.file("extdata/data", "marvel.demo.rds", package="MARVEL"))
class(marvel.demo)
## ---- message=FALSE, warning=FALSE--------------------------------------------
SplicePheno <- marvel.demo$SplicePheno
head(SplicePheno)
## ---- eval = FALSE------------------------------------------------------------
# # STAR in 1st pass mode
# STAR --runThreadN 16 \
# --genomeDir GRCh38_GENCODE_genome_STAR_indexed \
# --readFilesCommand zcat \
# --readFilesIn ERR1562083_1_val_1.fq.gz ERR1562083_2_val_2.fq.gz \
# --outFileNamePrefix SJ/ERR1562083. \
# --outSAMtype None
#
# # STAR in 2nd pass mode
# STAR --runThreadN 16 \
# --genomeDir GRCh38_GENCODE_genome_STAR_indexed \
# --readFilesCommand zcat \
# --readFilesIn ERR1562083_1_val_1.fq.gz ERR1562083_2_val_2.fq.gz \
# --outFileNamePrefix ERR1562083. \
# --sjdbFileChrStartEnd SJ/*SJ.out.tab \
# --outSAMtype BAM SortedByCoordinate \
# --outSAMattributes NH HI AS nM XS \
# --quantMode TranscriptomeSAM
## ---- message=FALSE, warning=FALSE--------------------------------------------
SpliceJunction <- marvel.demo$SpliceJunction
SpliceJunction[1:5,1:5]
## ---- eval = FALSE------------------------------------------------------------
# rmats \
# --b1 path_to_BAM_sample_1.txt \
# --b2 path_to_BAM_sample_2.txt \
# --gtf gencode.v31.annotation.gtf \
# --od rMATS/ \
# --tmp rMATS/ \
# -t paired \
# --readLength 125 \
# --variable-read-length \
# --nthread 8 \
# --statoff
## ---- message=FALSE, warning=FALSE--------------------------------------------
SpliceFeature <-marvel.demo$SpliceFeature
lapply(SpliceFeature, head)
## ---- eval = FALSE------------------------------------------------------------
# bedtools coverage \
# -g GRCh38.primary_assembly.genome_bedtools.txt \
# -split \
# -sorted \
# -a RI_Coordinates.bed \
# -b ERR1562083.Aligned.sortedByCoord.out.bam > \
# ERR1562083.txt \
# -d
## ---- message=FALSE, warning=FALSE--------------------------------------------
IntronCounts <- marvel.demo$IntronCounts
IntronCounts[1:5,1:5]
## ---- eval = FALSE------------------------------------------------------------
# rsem-calculate-expression --bam \
# --paired-end \
# -p 8 \
# ERR1562083.Aligned.toTranscriptome.out.bam \
# GRCh38_GENCODE_genome_RSEM_indexed/gencode.v31 \
# ERR1562083
## ---- message=FALSE, warning=FALSE--------------------------------------------
Exp <- marvel.demo$Exp
Exp[1:5,1:5]
## ---- message=FALSE, warning=FALSE--------------------------------------------
GeneFeature <- marvel.demo$GeneFeature
head(GeneFeature)
## ---- message=FALSE, warning=FALSE--------------------------------------------
marvel <- CreateMarvelObject(SpliceJunction=SpliceJunction,
SplicePheno=SplicePheno,
SpliceFeature=SpliceFeature,
IntronCounts=IntronCounts,
GeneFeature=GeneFeature,
Exp=Exp
)
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "PSI_Validation.png", package="MARVEL"))
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "PSI_Computation.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Check splicing junction data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="SJ")
## ---- eval = FALSE------------------------------------------------------------
# # Validate, filter, compute SE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# UnevenCoverageMultiplier=10,
# EventType="SE"
# )
#
# # Validate, filter, compute MXE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# UnevenCoverageMultiplier=10,
# EventType="MXE"
# )
#
# # Validate, filter, compute RI splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="RI",
# thread=4
# )
#
# # Validate, filter, compute A5SS splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="A5SS"
# )
#
# # Validate, filter, compute A3SS splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="A3SS"
# )
#
# # Validate, filter, compute AFE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="AFE"
# )
#
# # Validate, filter, compute ALE splicing events
# marvel.demo <- ComputePSI(MarvelObject=marvel.demo,
# CoverageThreshold=10,
# EventType="ALE"
# )
## ---- message=FALSE, warning=FALSE--------------------------------------------
marvel.demo <- TransformExpValues(MarvelObject=marvel.demo,
offset=1,
transformation="log2",
threshold.lower=1
)
## ---- message=FALSE, warning=FALSE--------------------------------------------
# Check splicing data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="splicing")
# Check gene data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="gene")
# Cross-check splicing and gene data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="splicing and gene")
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample ids
sample.ids <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Tabulate expressed events
marvel.demo <- CountEvents(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5
)
# Output (1): Plot
marvel.demo$N.Events$Plot
# Output (2): Table
marvel.demo$N.Events$Table
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample ids
sample.ids <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Tabulate expressed events
marvel.demo <- CountEvents(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5
)
# Output (1): Plot
marvel.demo$N.Events$Plot
# Output (2): Table
marvel.demo$N.Events$Table
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "Modality.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample IDs
sample.ids <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Assign modality
marvel.demo <- AssignModality(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5,
seed=1
)
marvel.demo$Modality$Results[1:5, c("tran_id", "event_type", "gene_id", "gene_short_name", "modality.bimodal.adj")]
# Tabulate modality proportion (overall)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=FALSE
)
marvel.demo$Modality$Prop$DoughnutChart$Plot
marvel.demo$Modality$Prop$DoughnutChart$Table
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Tabulate modality proportion (by event type)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=TRUE,
prop.test="fisher",
prop.adj="fdr",
xlabels.size=8
)
marvel.demo$Modality$Prop$BarChart$Plot
head(marvel.demo$Modality$Prop$BarChart$Table)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Define sample IDs
sample.ids <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Assign modality
marvel.demo <- AssignModality(MarvelObject=marvel.demo,
sample.ids=sample.ids,
min.cells=5,
seed=1
)
marvel.demo$Modality$Results[1:5, c("tran_id", "event_type", "gene_id", "gene_short_name", "modality.bimodal.adj")]
# Tabulate modality proportion (overall)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=FALSE
)
marvel.demo$Modality$Prop$DoughnutChart$Plot
marvel.demo$Modality$Prop$DoughnutChart$Table
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Tabulate modality proportion (by event type)
marvel.demo <- PropModality(MarvelObject=marvel.demo,
modality.column="modality.bimodal.adj",
modality.type="extended",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "AFE", "ALE"),
across.event.type=TRUE,
prop.test="fisher",
prop.adj="fdr",
xlabels.size=8
)
marvel.demo$Modality$Prop$BarChart$Plot
head(marvel.demo$Modality$Prop$BarChart$Table)
## ---- echo=FALSE--------------------------------------------------------------
include_graphics(system.file("extdata/figures", "DE.png", package="MARVEL"))
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Define cell groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Cell group 1 (reference)
cell.group.g1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Cell group 2
cell.group.g2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# DE analysis
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
min.cells=3,
method="t.test",
method.adjust="fdr",
level="gene",
show.progress=FALSE
)
marvel.demo$DE$Exp$Table[1:5, ]
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot DE results
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
pval=0.10,
log2fc=0.5,
point.size=0.1,
level="gene.global",
anno=FALSE
)
marvel.demo$DE$Exp.Global$Plot
marvel.demo$DE$Exp.Global$Summary
head(marvel.demo$DE$Exp.Global$Table[,c("gene_id", "gene_short_name", "sig")])
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot DE results with annotation of selected genes
# Retrieve DE output table
results <- marvel.demo$DE$Exp$Table
# Retrieve top genes
index <- which(results$log2fc > 2 | results$log2fc < -2)
gene_short_names <- results[index, "gene_short_name"]
# Plot
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
pval=0.10,
log2fc=0.5,
point.size=0.1,
xlabel.size=10,
level="gene.global",
anno=TRUE,
anno.gene_short_name=gene_short_names
)
marvel.demo$DE$Exp.Global$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
min.cells=5,
method=c("ad", "dts"),
method.adjust="fdr",
level="splicing",
event.type=c("SE", "MXE", "RI", "A5SS", "A3SS", "ALE", "AFE"),
show.progress=FALSE
)
head(marvel.demo$DE$PSI$Table[["ad"]])
head(marvel.demo$DE$PSI$Table[["dts"]])
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method="ad",
pval=0.10,
level="splicing.distance",
anno=TRUE,
anno.tran_id=marvel.demo$DE$PSI$Table[["ad"]]$tran_id[c(1:10)]
)
marvel.demo$DE$PSI$Plot[["ad"]]
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
marvel.demo <- CompareValues(MarvelObject=marvel.demo,
cell.group.g1=cell.group.g1,
cell.group.g2=cell.group.g2,
psi.method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
method.de.gene="t.test",
method.adjust.de.gene="fdr",
downsample=FALSE,
show.progress=FALSE,
level="gene.spliced"
)
head(marvel.demo$DE$Exp.Spliced$Table)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot: No annotation
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5,
point.size=0.1,
xlabel.size=8,
level="gene.spliced",
anno=FALSE
)
marvel.demo$DE$Exp.Spliced$Plot
marvel.demo$DE$Exp.Spliced$Summary
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=4, fig.align="center"----
# Plot: Annotate top genes
results <- marvel.demo$DE$Exp.Spliced$Table
index <- which((results$log2fc > 2 | results$log2fc < -2) & -log10(results$p.val.adj) > 1)
gene_short_names <- results[index, "gene_short_name"]
marvel.demo <- PlotDEValues(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5,
point.size=0.1,
xlabel.size=8,
level="gene.spliced",
anno=TRUE,
anno.gene_short_name=gene_short_names
)
marvel.demo$DE$Exp.Spliced$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Define sample groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Retrieve DE genes
# Retrieve DE result table
results.de.exp <- marvel.demo$DE$Exp$Table
# Retrieve relevant gene_ids
index <- which(results.de.exp$p.val.adj < 0.10 & abs(results.de.exp$log2fc) > 0.5)
gene_ids <- results.de.exp[index, "gene_id"]
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=gene_ids,
point.size=2.5,
level="gene"
)
marvel.demo$PCA$Exp$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Retrieve DE tran_ids
method <- c("ad", "dts")
tran_ids.list <- list()
for(i in 1:length(method)) {
results.de.psi <- marvel.demo$DE$PSI$Table[[method[i]]]
index <- which(results.de.psi$p.val.adj < 0.10 & results.de.psi$outlier==FALSE)
tran_ids <- results.de.psi[index, "tran_id"]
tran_ids.list[[i]] <- tran_ids
}
tran_ids <- unique(unlist(tran_ids.list))
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
marvel.demo$PCA$PSI$Plot
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Retrieve relevant gene_ids
results.de.exp <- marvel.demo$DE$Exp$Table
index <- which(results.de.exp$p.val.adj < 0.10 & abs(results.de.exp$log2fc) > 0.5)
gene_ids <- results.de.exp[-index, "gene_id"]
# Reduce dimension
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=gene_ids,
point.size=2.5,
level="gene"
)
marvel.demo$PCA$Exp$Plot
## ---- message=FALSE, warning=FALSE, fig.width=7, fig.height=8, fig.align="center"----
# Retrieve non-DE gene_ids
results.de.exp <- marvel.demo$DE$Exp$Table
index <- which(results.de.exp$p.val.adj > 0.10 )
gene_ids <- results.de.exp[, "gene_id"]
# Retrieve tran_ids
df.feature <- do.call(rbind.data.frame, marvel.demo$SpliceFeatureValidated)
df.feature <- df.feature[which(df.feature$gene_id %in% gene_ids), ]
# Reduce dimension: All DE splicing events
tran_ids <- df.feature$tran_id
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.all <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: SE
tran_ids <- df.feature[which(df.feature$event_type=="SE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.se <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: MXE
tran_ids <- df.feature[which(df.feature$event_type=="MXE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.mxe <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: RI
tran_ids <- df.feature[which(df.feature$event_type=="RI"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.ri <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: A5SS
tran_ids <- df.feature[which(df.feature$event_type=="A5SS"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.a5ss <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: A3SS
tran_ids <- df.feature[which(df.feature$event_type=="A3SS"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.a3ss <- marvel.demo$PCA$PSI$Plot
# Reduce dimension: AFE
tran_ids <- df.feature[which(df.feature$event_type=="AFE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.afe <- marvel.demo$PCA$PSI$Plot
# Reduce dimension:
tran_ids <- df.feature[which(df.feature$event_type=="ALE"), "tran_id"]
marvel.demo <- RunPCA(MarvelObject=marvel.demo,
cell.group.column="cell.type",
cell.group.order=c("iPSC", "Endoderm"),
cell.group.colors=NULL,
min.cells=5,
features=tran_ids,
point.size=2.5,
level="splicing",
method.impute="random",
seed=1
)
plot.ale <- marvel.demo$PCA$PSI$Plot
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.all, plot.se,
plot.mxe, plot.ri,
plot.a5ss, plot.a3ss,
plot.afe, plot.ale,
nrow=4)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
# Define sample groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Assign modality dynamics
marvel.demo <- ModalityChange(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10)
)
marvel.demo$DE$Modality$Plot
head(marvel.demo$DE$Modality$Table)
marvel.demo$DE$Modality$Plot.Stats
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr4:108620569:108620600|108620656:108620712:+@chr4:108621951:108622024"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr12:110502049:110502117:-@chr12:110499535:110499546:-@chr12:110496012:110496203"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr9:35685269:35685339:-@chr9:35685064:35685139:-@chr9:35684732:35684807:-@chr9:35684488:35684550"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr11:85981129:85981228:-@chr11:85978070:85978093:-@chr11:85976623:85976682"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr17:8383254:8382781|8383157:-@chr17:8382143:8382315"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr17:8383157:8383193|8382781:8383164:-@chr17:8382143:8382315"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr15:24962114:24962209:+@chr15:24967029:24967152:+@chr15:24967932:24968082"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr8:144792587:144792245|144792366:-@chr8:144791992:144792140"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=8, fig.height=2, fig.align="center"----
# Example 1
tran_id <- "chr5:150449703:150449739|150449492:150449696:-@chr5:150447585:150447735"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.1 <- marvel.demo$adhocPlot$PSI
# Example 2
tran_id <- "chr12:56725340:56724962|56725263:-@chr12:56724452:56724523"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.2 <- marvel.demo$adhocPlot$PSI
# Example 3
tran_id <- "chr10:78037194:78037304:+@chr10:78037439:78037441:+@chr10:78040204:78040225"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.3 <- marvel.demo$adhocPlot$PSI
# Example 4
tran_id <- "chr10:78037194:78037304:+@chr10:78037439|78040204:78040225"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=5,
level="splicing",
min.cells=5
)
plot.4 <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1, plot.2,
plot.3, plot.4,
nrow=1)
## ---- message=FALSE, warning=FALSE, fig.width=4, fig.height=3, fig.align="center"----
marvel.demo <- IsoSwitch(MarvelObject=marvel.demo,
method=c("ad", "dts"),
psi.pval=c(0.10, 0.10),
psi.delta=0,
gene.pval=0.10,
gene.log2fc=0.5
)
marvel.demo$DE$Cor$Plot
head(marvel.demo$DE$Cor$Table)
marvel.demo$DE$Cor$Plot.Stats
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Define cell groups
# Retrieve sample metadata
df.pheno <- marvel.demo$SplicePheno
# Group 1
sample.ids.1 <- df.pheno[which(df.pheno$cell.type=="iPSC"), "sample.id"]
# Group 2
sample.ids.2 <- df.pheno[which(df.pheno$cell.type=="Endoderm"), "sample.id"]
# Merge
cell.group.list <- list("iPSC"=sample.ids.1,
"Endoderm"=sample.ids.2
)
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="CMC2"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr16:80981806:80981877:-@chr16:80980808:80980879|80976003:80976179"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="HNRNPC"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr14:21231072:21230958|21230997:-@chr14:21230319:21230366"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="APOO"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chrX:23840313:23840377:-@chrX:23833353:23833612|23833367:23833510"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="BUB3"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr10:123162612:123162828:+@chr10:123163820:123170467|123165047:123165365"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- message=FALSE, warning=FALSE, fig.width=5, fig.height=5, fig.align="center"----
# Example 1
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="AC004086.1"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.1_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr12:112409641:112409411|112409587:-@chr12:112408420:112408656"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.1_splicing <- marvel.demo$adhocPlot$PSI
# Example 2
# Gene
df.feature <- marvel.demo$GeneFeature
gene_id <- df.feature[which(df.feature$gene_short_name=="ACP1"), "gene_id"]
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=gene_id,
maintitle="gene_short_name",
xlabels.size=7,
level="gene"
)
plot.2_gene <- marvel.demo$adhocPlot$Exp
# Splicing
tran_id <- "chr2:271866:271939:+@chr2:272037:272150:+@chr2:272192:272305:+@chr2:275140:275201"
marvel.demo <- PlotValues(MarvelObject=marvel.demo,
cell.group.list=cell.group.list,
feature=tran_id,
xlabels.size=7,
level="splicing",
min.cells=5
)
plot.2_splicing <- marvel.demo$adhocPlot$PSI
# Arrange and view plots
# Read plots from right to left for each row
grid.arrange(plot.1_gene, plot.1_splicing,
plot.2_gene, plot.2_splicing,
nrow=2)
## ---- eval = FALSE------------------------------------------------------------
# marvel.demo <- BioPathways(MarvelObject=marvel.demo,
# method=c("ad", "dts"),
# pval=0.10,
# species="human"
# )
#
# head(marvel.demo$DE$BioPathways$Table)
## ---- message=FALSE, warning=FALSE, fig.width=6, fig.height=4, fig.align="center"----
# Plot top pathways
df <- marvel.demo$DE$BioPathways$Table
go.terms <- df$Description[c(1:10)]
marvel.demo <- BioPathways.Plot(MarvelObject=marvel.demo,
go.terms=go.terms,
y.label.size=10
)
marvel.demo$DE$BioPathways$Plot
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