MARVEL

In MARVEL: Revealing Splicing Dynamics at Single-Cell Resolution

library(knitr)

Installation

| MARVEL is available on CRAN and also on Github. To access features in beta-testing phase, please install the package from Github: https://github.com/wenweixiong/MARVEL.

Introduction

| This tutorial demonstrates the application of MARVEL for integrated gene and splicing analysis of single-cell RNA-sequencing data. The dataset used to demonstrate the utility of MARVEL here includes induced pluripotent stem cells (iPSCs) and iPSC-induced endoderm cells (Linker et al., 2019). For conciseness, only a subset of the original data will be used here, and only the most salient functions will be demonstrated here. For the complete functionalities of MARVEL, please refer to https://wenweixiong.github.io/MARVEL_Plate.html and https://wenweixiong.github.io/MARVEL_Droplet.html.

Load package

# Load MARVEL package
library(MARVEL)

# Load adjunct packages for this tutorial
library(ggplot2)
library(gridExtra)

# Load adjunct packages to support additional functionalities
library(AnnotationDbi) # GO analysis
library(clusterProfiler)
library(org.Hs.eg.db)
library(org.Mm.eg.db)

# 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

Input files

| The input files have been saved in a MARVEL object, and will be elaborated below.

# Load saved MARVEL object
marvel.demo <- readRDS(system.file("extdata/data", "marvel.demo.rds", package="MARVEL"))
class(marvel.demo)

Sample metadata

| This is a tab-delimited file created by the user whereby the rows represent the sample (cell) IDs and columns represent the cell information such as cell type, donor ID etc.. Compulsory column is sample.id while all other columns are optional.

SplicePheno <- marvel.demo$SplicePheno
head(SplicePheno)

Splice junction counts matrix

| The rows of this matrix represent the splice junction coordinates, the columns represent the sample IDs, and the values represent the splice junction counts. The first column should be named coord.intron. | Here, the splice junction counts were quantified using the STAR aligner version 2.6.1d in 2-pass mode (Dobin et al., 2013). An example code for one sample (ERR1562083) below. Note a separate folder SJ is created here to contain the splice junction count files (SJ.out.tab) generated from 1st pass mode to be used for 2nd pass mode.

# 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

| Once the individual splice junction count files have been generated, they should be collated and read into R as follows:

SpliceJunction <- marvel.demo$SpliceJunction
SpliceJunction[1:5,1:5]

Splicing event metadata

| The rows of this metadata represent the splicing events while the columns represent the splicing event information such as the transcript ID and the corresponding gene information. Compulsory columns are tran_id and gene_id. | The splicing events here were detected using rMATS version 4.1.0 (Shen et al., 2014). For preparing splicing event nomenclatures (tran_id), please refer to https://wenweixiong.github.io/Splicing_Nomenclature. Example code for running rMATS as follows. | Note that any BAM files may be specified in --b1 and --b2. This is because rMATS requires these specification for statistical testing of splicing events between the two samples. But here, we will only be using the splicing events detected (fromGTF.SE.txt, fromGTF.MXE.txt, fromGTF.RI.txt, fromGTF.A5SS.txt, fromGTF.A3SS.txt), but not the statistical test results, from this step for our downstream analysis.

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

| Once the individual splicing event files for SE, MXE, RI, A5S5, and A3SS have been generated, they may be read into R as follows:

SpliceFeature <-marvel.demo$SpliceFeature
lapply(SpliceFeature, head)

Intron count matrix

| The rows of this matrix represent intron coordinates, the columns represent the sample IDs, and the values represent the total reads mapping to the intron. These values will be used to compute the percent spliced-in (PSI) values of retained introns (RI) splicing events downstream. | Here, intron coverage was computed using Bedtools version 2.27.1 (Quinlan et al., 2010). Example code for one sample (ERR1562083) below. This code computes the counts at each base of a given intron, the sum of which, will be the total counts for the given intron. It is this total counts that is represented in the matrix. | Note for GRCh38.primary_assembly.genome_bedtools.txt, the first column consists of the chromosome name (chr1, chr2, chr3...) and the second column consists of the chromosome size or length. Additionally, the BED file RI_Coordinates.bed contains the intron coordinates from RI_featureData.txt generated from rMATS in the previous step.

bedtools coverage \
               -g GRCh38.primary_assembly.genome_bedtools.txt \
               -split \
               -sorted \
               -a RI_Coordinates.bed \
               -b ERR1562083.Aligned.sortedByCoord.out.bam > \
                  ERR1562083.txt \
               -d

| Once the individual splice junction count files have been generated, they should be collated and read into R as follows:

IntronCounts <- marvel.demo$IntronCounts
IntronCounts[1:5,1:5]

Gene expression matrix

| The rows of this matrix represent the gene IDs, the columns represent the sample IDs, and the values represent the normalised gene expression counts (e.g., RPKM/FPKM/TPM), but not yet log2-transformed. | Here, gene expression was quantified using RSEM version 1.2.31 (Li et al., 2011). Example code for one sample (ERR1562083) as follows. Here, the values returned are in transcript per million (TPM) unit.

rsem-calculate-expression --bam \
                          --paired-end \
                          -p 8 \
                          ERR1562083.Aligned.toTranscriptome.out.bam \
                          GRCh38_GENCODE_genome_RSEM_indexed/gencode.v31 \
                          ERR1562083

| Once the individual gene expression files have been generated, they should be collated and read into R as follows:

Exp <- marvel.demo$Exp
Exp[1:5,1:5]

Gene metadata

| The rows of this metadata represent the gene IDs while the columns represent the gene information such as the abbreviated gene names and gene type. Compulsory columns are gene_id, gene_short_name, and gene_type. All other columns are optional. | Here, the metadata information was parsed and retrieved from gencode.v31.annotation.gtf.

GeneFeature <- marvel.demo$GeneFeature
head(GeneFeature)

Create MARVEL object

marvel <- CreateMarvelObject(SpliceJunction=SpliceJunction,
                             SplicePheno=SplicePheno,
                             SpliceFeature=SpliceFeature,
                             IntronCounts=IntronCounts,
                             GeneFeature=GeneFeature,
                             Exp=Exp
                             )

Compute PSI

| MARVEL will compute the percent spliced-in (PSI) values for each splicing event. Only splicing event supported by splice junction reads, i.e., high-confidence splicing events, will be selected for PSI quantification. The minimum number of splice junction reads required may be specified using the CoverageThreshold option.

include_graphics(system.file("extdata/figures", "PSI_Validation.png", package="MARVEL"))

| PSI is simply the proportion of reads supporting the inclusion of the alternative exon divided by the total number of reads mapping to the splicing event, which encompasses the reads supporting the inclusion and also reads supporting the exclusion of the splicing event. This fraction is in turn converted to percentage.

include_graphics(system.file("extdata/figures", "PSI_Computation.png", package="MARVEL"))

# Check splicing junction data
marvel.demo <- CheckAlignment(MarvelObject=marvel.demo, level="SJ")

• Ensure that only MATCHED flags are reported. If any NOT MATCHED flags are reported, please double-check the input file requirements.

# 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"
                          )

| The common option across all functions for computing PSI value is CoverageThreshold. This option indicates the minimum number of splice junction reads supporting the splicing events, above which, the PSI will be computed. PSI of splicing events below this threshold will be coded as NA. | Options specific to a given splicing event are: - UnevenCoverageMultiplier Specific to computing SE and MXE. Two splice junctions are used to compute to inclusion of SE and MXE. This option represent the ratio of read coverage of one splice junction over the other. The threshold specified here, above which, the PSI will be coded as NA. - thread Specific to computing RI. Number of cores to use. This is depended on the user's device. - read.length Specific to computing RI. If the values in df.intron.counts represent number of reads, then this option should reflect the sequencing read length, e.g., 150 etc.. If the values in df.intron.counts represent total intronic coverage (here), then this option should be set to 1 (default).

Pre-flight check

| This step ensures that our data is ready for further downstream analysis, including modality assignment, differential expression analysis, dimension reduction, and functional annotation.

Transform expression values

| Gene expression values will be log2-transformed. You may skip this step if your gene expression matrix has been transformed prior to creating the MARVEL object.

marvel.demo <- TransformExpValues(MarvelObject=marvel.demo,
                                  offset=1,
                                  transformation="log2",
                                  threshold.lower=1
                                  )

Check matrices and metadata

| We will have to make sure the columns of the matrices align with the sample IDs of the sample metadata and the rows of the matrices align with the feature metadata. Finally, the columns across all matrices should align with one another.

# 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")

| Our data is ready for downstream analysis when only MATCHED flags are reported. If any NOT MATCHED flags are reported, please double-check the input file requirements.

Overview of splicing events

| Let's have an overview of the number of splicing events expressed in a given cell population, and stratify them by splicing event type.

iPSC

# 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

• min.cells option. Here, we required the splicing event to be expressed in at least 5 cells for the splicing event to be included for analysis.

Endoderm cells

# 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

Modality analysis

| The PSI distribution for a given splicing event in a given cell population may be assigned to a modality class. Modalities are simply discrete splicing patterns categories. This will enable us to understand the isoform expression pattern for a given splicing event in a given cell population. | The five main modalities are included, excluded, bimodal, middle, and multimodal (Song et al., 2017). MARVEL provides finer classification of splicing patterns by further stratifying included and excluded modalities into primary and dispersed.

include_graphics(system.file("extdata/figures", "Modality.png", package="MARVEL"))

iPSC

# 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

• min.cells option. Here, we required the splicing event to be expressed in at least 25 cells for the splicing event to be included for modality assignment. This value should match that previously defined in CountEvents function.
• The most prevalent modality types are included and excluded. This suggest iPSCs predominantly express one form of isoform, either the isoform that includes the alternative exon or the isoform that excludes the alternative exon.
• The bimodal modality constitutes a small proportion of splicing patterns. This suggests most splicing events are not expressed by two discrete sub-populations of iPSCs, i.e. one sub-population that includes the alternative exons while the other sub-population excludes the alternative exon.
• The middle modality also constitutes a small proportion of splicing patterns. This suggests that most iPSCs do not concurrently express both isoforms in the same cell, i.e., isoform that includes the alternative exon and another isoform that excludes the alternative exon.

# 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)

• By stratifying the modalities by splicing event type, we can assess if certain modalities are more enriched in a given splicing event type.
• For example, here we observed the excluded modality to be the most prevalent in retained-intron (RI).
• This suggests that most isoforms do not retain introns, and this is consistent with the role of mRNA splicing in intron removal, i.e., only a small proportion of isoforms retain their introns as a mechanism of regulating gene expression.

Endoderm

# 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

• Similar to iPSCs, the most prevalent modality types are included and excluded among endoderm cells.
• Similar to iPSCs, the bimodal, middle, and multimodal modalities constitute a small proportion of splicing patterns among endoderm cells.

# 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)

• Similar to iPSCs, we observe the excluded modality to be the most prevalent in retained-intron (RI) among endoderm cells.

Differential analysis

| Differential analysis is the cornerstone of RNA-sequencing analysis. This is the first step to identify candidate genes and isoforms for downstream experimental validation. | Statistical tests that compare the mean expression values between two cell populations, such as Wilcox, are suitable for differential gene expression analysis. | However, the mean alone will not be sufficient to detect changes in splicing patterns. For example, based on the mean alone, it may not be possible to distinguish between splicing events with bimodal, middle, and multimodal splicing patterns. Therefore, in lieu of comparing mean, MARVEL compares the overall PSI distribution between two cell populations.

include_graphics(system.file("extdata/figures", "DE.png", package="MARVEL"))

Differential gene expression analysis

# 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, ]

• min.cells option. Here, we required the gene to be expressed in at least 3 cells in either iPSCs or endoderm cells for the gene to be included for analysis.
• show.progress option. For the brevity of the tutorial, we did not track the progress of differential expression analysis. But users are advised to set this option to TRUE when running this step on their own devices.

Volcano plot: Genes

# 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")])

• pval option. The adjusted p-value, below which, the gene is considered to be differentially expressed.
• log2fc option. The absolute log2 fold change, above which, the gene is considered to be differentially expressed.

# 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

Differential splicing analysis

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"]])

• min.cells option. Here, we required the splicing event to be expressed in at least 25 cells in both iPSCs and endoderm cells for the splicing event to be included for analysis.
• method option. We recommend Anderson-Darling (ad) and D Test Statistics (dts) for comparing the overall PSI distribution between two cell populations.
• show.progress option. For the brevity of the tutorial, we did not track the progress of differential expression analysis. But users are advised to set this option to TRUE when running this step on their own devices.

Distance plot: Splicing

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"]]

• method option. Plot results for ad statistical test.
• pval option. The adjusted p-value, below which, the splicing event is considered to be differentially spliced.
• level option. When set to "splicing.distance", the distance statistic will be used to plot the DE results. Only applicable when method set to "ad" or "dts". When set to splicing.mean. The typical volcano plot is returned, and the delta option may be used.
• delta option. when level set to "splicing.mean", the absolute differences in mean PSI values between the two cell populations, above which, the splicing event is considered to be differentially spliced.

Differential (spliced) gene analysis

| Next, we will perform differential gene expression analysis only on the differentially spliced genes. This will enable us to investigate the gene-splicing relationship between iPSCs and endoderm cells downstream.

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)

• psi.method, psi.pval, and psi.delta options. For defining differentially spliced events whose corresponding genes will be included for differential gene expression analysis.
• method.de.gene and method.adjust.de.gene options. The statistical test and multiple testing method for differential gene expression analysis.

Volcano plot: Spliced genes

# 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

• method option. Merge results from ad and dts statistical tests.
• pval.psi option. The adjusted p-value, below which, the splicing event is considered to be differentially spliced.
• delta.psi option. The absolute difference in mean PSI values between the two cell populations, above which, the splicing event is considered to be differentially spliced.
• gene.pval option. The adjusted p-value, below which, the gene is considered to be differentially expressed.
• gene.log2fc option. The absolute log2 fold change, above which, the gene is considered to be differentially expressed.
• Note that 344 of 816 (42%) differentially spliced genes were not differentially expressed. Therefore, nearly half of differentially spliced genes may not be detected from differential gene expression analysis alone.

# 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

Principal component analysis

| Dimension reduction analysis such as principal component analysis (PCA) enables us to investigate if phenotypically different cell populations are transcriptomically distinct from one another. | This may be done in a supervised or unsupervised manner. The former approach uses all expressed genes or splicing events while the latter approach uses pre-determined features, such as genes and splicing event obtained from differential expression analysis. | Here, we will assess if splicing represents an additional layer of heterogeneity underlying gene expression profile. We will also demonstrate how to retrieve differentially expressed genes and differentially spliced genes from the DE analysis outputs to be used as features in PCA.

DE genes

# 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

• min.cells option. Here, we required the gene to be expressed in at least 25 cells across the overall cell populations defined in cell.group.list for the gene to be included for analysis.
• feature option. Gene IDs to be used for dimension reduction.
• As expected, using differentially expressed (DE) genes, we were able to distinguish between iPSCs and endoderm cells.

DE splicing

# 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

• min.cells option. Here, we required the splicing event to be expressed in at least 25 cells across the overall cell populations defined in cell.group.list for the splicing event to be included for analysis.
• feature option. Splicing events to be used for dimension reduction.
• method.impute option. Method to impute missing PSI values. Indicate "random" to to randomly assign any values between 0-100 to missing values (Song et el., 2017). Indicate "population.mean" to use the mean PSI value of each cell group to impute the missing values found in the corresponding cell group (Huang et al., 2021).
• seed option. Only applicable when method.impute option set to "random". This option ensures that the randomly imputed values will always be reproducible.
• As expected, using differentially spliced genes, we were able to distinguish between iPSCs and endoderm cells.

non-DE genes

# 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

• As expected, using non-DE genes, we were not able to distinguish between iPSCs and endoderm cells.
• Can splicing distinguish between iPSCs and endoderm cells. when non-DE genes couldn't?

Splicing (non-DE genes)

# 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)

• Note that while non-DE genes were not successful in distinguishing between iPSCs and endoderm cells, splicing events of non-DE genes were successful in doing so. This suggests that splicing may be an additional and invisible source of complexity underlying gene expression profile.

Modality dynamics

| Modality dynamics reveals the change in splicing pattern (modality) from one cell population (iPSCs) to another (endoderm cells). The modality dynamics from one cell population to another can be classified into three categories, namely explicit, implicit, and restricted. - Explicit modality change involves one of the main modality classess, namely included, excluded, bimodal, middle, and multimodal. For example, included to bimodal would constitute an explicity modality change. - Implicit modality change involves one of the sub- modality classess, namely primary and dispersed. For example, included-primary to included-dispersed would constitute an implicit modality change. - Restricted modality change involves limited change in splicing pattern. For example, both cell populations may have the same modality class but different mean PSI values.

| Here, we will perform modality dynamics analysis among differentially spliced events. Representative examples for each modality dynamics classification will also be shown. This section also introduces our ad hoc plot function PlotValues for plotting selected splicing events.

Assign dynamics

# 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

• method option. The statistical tests used earlier for differential splicing analysis. Here, we combined the differentially spliced events from both ad and dts tests.
• pval option. The adjusted p-value, below which, the splicing event is considered to be differentially spliced. The numeric vector should be the same length as the method option.
• Note that the most prevalent modality change from iPSCs to endoderm cells is restricted, followed by implicit and then explicit. This suggests that the alternative splicing is relatively tightly regulated because big (explicit) changes in splicing patterns are uncommon.

Explicit

# 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)

Implicit

# 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)

Restricted

# 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)

Gene-splicing dynamics

| MARVEL's integrated differential gene and splicing analysis enables us to investigate how gene expression changes relative to splicing changes when iPSCs differentiate into endoderm cells. The gene-splicing dynamics may be classified into four categories, namely coordinated, opposing, isoform-switching, and complex. - Coordinated gene-splicing relationship refers to the change in mean gene expression is in the same direction with the corresponding splicing event(s). - Opposing gene-splicing relationship refers to the change in mean gene expression is in the opposite direction to the corresponding splicing event(s). - Isoform-switching refers to genes that are differentially spliced without being differentially expressed. - Complex gene-splicing relationship refers to genes with both coordinated and opposing relationships with the corresponding splicing events.

| Here, we will explore the gene-splicing dynamics of genes that are differentially spliced between iPSCs and endoderm cells. Representative examples of each dynamic will also be shown. This section also utilises the ad hoc plotting function PlotValues for plotting selected splicing events and genes. | Please note that the function CompareValues with the level option set to gene.spliced needs to be executed prior to proceeding with gene-splicing dynamics analysis below. Kindly refer to Differential (spliced) gene analysis section of this tutorial.

Assign dynamics

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

• method option. Merge results from ad and dts statistical test.
• pval.psi option. The adjusted p-value, below which, the splicing event is considered to be differentially spliced.
• delta.psi option. The absolute difference in mean PSI values between the two cell populations, above which, the splicing event is considered to be differentially spliced.
• gene.pval option. The adjusted p-value, below which, the gene is considered to be differentially expressed.
• gene.log2fc option. The absolute log2 fold change, above which, the gene is considered to be differentially expressed.
• Here, we observed majority of differentially spliced genes underwent isoform-switching followed by coordinated, and then opposing gene expression changes relative to splicing changes.
• Note that majority of differentially spliced genes may not be inferred directly from differential gene expression analysis alone. For example, only in coordinated gene-splicing relationship that the gene and splicing changes between iPSCs and endoderm cells are in the same direction. But this category only constitute around one-quarter of gene-splicing relationships.

Coordinated

# 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)

• For CMC2 and HNRNPC, both gene expression and splicing rate for the splicing event shown here are decreased in endoderm cells relative to iPSCs.

Opposing

# 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)

• For both APOO and BUB3, the gene expression is decreased in endoderm cells relative to iPSCs.
• However, the mean splicing rates of the splicing events shown here are increased in endoderm cells relative to iPSCs.

Iso-switch

# 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)

• For both AC004086.1 and ACP1, the differences in gene expression between iPSCs and endoderm cells are not statistically significant.
• However, the corresponding splicing patterns of the splicing events shown here are significantly different between iPSCs and endoderm cells.

Gene ontology analysis

| Gene ontology analysis or pathway enrichment analysis categorises the differentially spliced genes between iPSCs and endoderm cell into biological pathways. This may identify sets of genes with similar function or belong to similar biological pathways that are concurrently spliced. | Gene ontology analysis represents one of the two functional annotation features of MARVEL. The other functional annotation feature is nonsense-mediated (NMD) analysis.

marvel.demo <- BioPathways(MarvelObject=marvel.demo,
                           method=c("ad", "dts"),
                           pval=0.10,
                           species="human"
                           )

head(marvel.demo$DE$BioPathways$Table)

• method option. Merge results from ad and dts statistical test.
• pval option. The adjusted p-value, below which, the splicing event is considered to be differentially spliced.
• species option. MARVEL also supports GO analysis of "mouse".
• custom.genes option. In lieu of specifying genes with the method and pval options, users may specify any custom set of genes using this option.

# 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

• The top biological pathways enriched among differentially spliced genes are related to transcription, translation, and metabolism.
• Users can plot the enrichment results of any biological pathways of interest arising from BioPathways function. Simply specify the custom set of pathways using the go.terms option of the BioPathways.Plot function.

Companion tool: VALERIE

| From this tutorial, we identified over 1,000 differentially spliced events. We would like to introduce VALERIE (Visulazing ALternative splicing Events from RIbonucleic acid Experiments) - a visualisation platform for visualising alternative splicing events at single-cell resolution. | The tutorial for using VALERIE for investigating these differentially spliced events can be found here: https://wenweixiong.github.io/VALERIE. The R package may be installed from Github here: https://github.com/wenweixiong/VALERIE.

MARVEL documentation built on Oct. 31, 2022, 5:07 p.m.