| title | subtitle | author | date | output | vignette |
|---|---|---|---|---|---|
TSIS: an R package to infer time-series isoform switch of alternative splicing |
User manual |
Wenbin Guo |
2017-05-24 |
BiocStyle::html_document |
%\VignetteIndexEntry{Introduction to TSIS} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
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TSIS is an R package for detecting transcript isoform switches in time-series data. Transcript isoform switches occur when a pair of alternatively spliced isoforms reverse the order of their relative expression levels as shown in Figure 1. TSIS characterizes the transcript switch by 1) defining the isoform switch time-points for any pair of transcript isoforms within a gene, 2) describing the switch using five different features or metrics, 3) filtering the results with user’s specifications and 4) visualizing the results using different plots for the user to examine further details of the switches. All the functions are available in the forms of a graphic interface implemented by Shiny App (a web application framework for R) (Chang, et al., 2016), in which users can implement the analysis easily. The tool can also be run using command lines without graphic interface. This tutorial will cover both in the following sections. This manual can be downloaded from: https://github.com/wyguo/TSIS/blob/master/vignettes/tutorial-shiny.zip.
If you use TSIS in your work, please cite:
Wenbin Guo, Cristiane P. G. Calixto, John W.S. Brown, Runxuan Zhang, "TSIS: an R package to infer alternative splicing isoform switches for time-series data", Bioinformatics, https://doi.org/10.1093/bioinformatics/btx411, 2017.
Figure 1: Isoform switch analysis methods. Expression data with 3 replicates for each condition/time-point is simulated for isoforms
Given that a pair of isoforms
- The first approach takes the average values of the replicates for each time-point for each transcript isoform. Then it searches for the cross points of the average value of two isoforms across the time-points (seen in Figure 1(B)).
- The second approach uses natural spline curves to fit the time-series data for each transcript isoform and find cross points of the fitted curves for each pair of isoforms.
In most cases, these two methods produce very similar results. However, average values of expression may lose precision by not having information of previous and following time-points. The spline method fits the time-series of expression with control points (depending on spline degree of freedom provided) and weights of several neighbours to obtain designed precision (Hastie and Chambers, 1992). The spline method is useful to find global trends in the time-series data when the data is very noisy. However, it may lack details of isoform switch in the local region. It is recommended that users use both average and spline methods to search for the switch points and examine manually when inconsistent results were produced by the above two methods.
Due to an issue with devtools, if R software is installed in a directory whose name has a space character in it, e.g. in "C:\Program Files", users may get error message "'C:\Program' is not recognized as an internal or external command". This issue has to be solved by making sure that R is installed in a directory whose name has no space characters. Users can check the R installation location by typing
R.home()install.packages(c("shiny", "shinythemes","ggplot2","plotly","zoo","gtools","devtools"), dependencies=TRUE)
Install TSIS package from Github using devtools package.
library(devtools)
devtools::install_github("wyguo/TSIS")Once installed, TSIS package can be loaded as normal.
library(TSIS)The TSIS package provides the example dataset "TSIS.data" with 30 genes and 145 isoforms, analyzed in 26 time-points, each with 9 replicates. The isoform expression is in TPM (transcript per million) format. Other types of transcript quantifications, such as read counts, Percentage Splicing In (PSI) can also be used in TSIS.
The data loaded into the TSIS App must be in *.csv format. Users can download the example datasets from https://github.com/wyguo/TSIS/tree/master/data or by typing the following codes in R console:
TSIS.data.example()The data will be saved in a folder "example data" in the working directory. Figure 3 shows the examples of input data in csv format.
To make the implementation more user friendly, TSIS analysis is integrated into a Shiny App (Chang, et al., 2016). By typing
TSIS.app(data.size.max = 100) in R console after loading TSIS package, where “data.size.max” is the maximum allowance size for loading input data. The default is 100MB. The App is opened in the default web browser. Users can upload input datasets, set parameters for switch analysis, visualize and save the results easily. The TSIS App includes three tab panels (see Figure 2(A)).
The first tab panel includes this user manual.
There are four sections in this panel (see Figure 2).), namely Input data files, Parameter settings, Density/Frequency of switch and output metrics table of isoform switch.
Figure 2: Second tab panel in TSIS Shiny App. (A) is the three tab panels of the app; (B) is the data input interface; (C) is the interface for TSIS parameter setting; (D) provides the density/frequency plots of isoform switch time and (E) shows the output of TSIS analysis.
Three *.csv format input files can be provided for TSIS analysis.
- Time-series isoform expression data with first row indicating the replicate labels and second row indicating the time-points. The remaining lines are isoform names in the first column followed by the expression values (see Figure 3(A)).
- Gene and isoform mapping table with gene names in first column and transcript isoform names in the second column (see Figure 3(B)).
- Optional. A list of isoform names of interest. Users can output subsets of results by limiting the output to a list of isoforms of interest, for example, protein coding transcripts (see Figure 3(C)).
Figure 3: The format of input csv files for (A) transcript isoform expression, (B) two column table of gene-isoform mapping and (C) A list of isoform names of interest.
Figure 2(B) and Figure 4(A) shows the data input interface for time-series isoform expression and gene-isoform mapping. By clicking the "Browse…" button, a window is open for data loading (see Figure 4(B)). Users can use the interface shown in Figure 4(C) to load the names of subset of isoforms.
Figure 4: Interface for input information. (A) Input transcript isoform expression and gene-isoform mapping data, (B) is an opened window to select files after clicking “Browser” and (C) is the interface to load isoform names of interest.
The section in Figure 2(C) and Figure 5 is used to set the parameters for TSIS. The parameters can be set by selecting or typing in corresponding boxes. The details of how to set the parameters are in the text below the “Scoring” button (Figure 5(A)). Scoring data is generated by clicking on the "Scoring" button. Processing tracking bars (Figure 5(B)) are presented at the bottom of the browser when the scoring is in progress.
Figure 5(C) is the interface for output filtering. Users can set cut-offs, such as for the probability/frequency of switch and sum of average differences, to further refine the switch results. The parameter setting details are in the text under the "Filtering" button.
Figure 5: TSIS parameter setting section. (A) is the input interface for setting parameters for scoring ; (B) is the process tracking bars and (C) is the interface for setting parameters for filtering.
The isoform switches may occur at different time-points in the time-series. To visualize the frequency and density plot of timing of switches, TSIS Shiny App provides the plot interface as shown in Figure 6. Frequency and density bar plots as well as line plots, which correspond to isoform switch time-points after scoring and filtering processes, are presented by clicking the corresponding radio buttons. The plot can be saved in html, pdf or png format.
Note: The plot is made by using plotly R package. Users can move the mouse around the plot to show plot values and select part of the plot to zoom in. More actions are available by using the tool bar in the top right corner of the plot.
Figure 6: Switch time (A) frequency and (B) density plot interface.
The output of TSIS analysis can be displayed and exported after scoring or filtering. The columns include the information of isoform names, isoform ratios to genes, the intervals before and after switch, the coordinates of switch points and five measurements to characterize the isoform switch. Table columns can be sorted by clicking the small triangles beside the column names and contents can be searched by typing text in the search box. The explanations for each column are on the top of the table (see Figure 7).
Figure 7: The output of TSIS.
Figure 8: The third tab panel of TSIS Shiny App. (A) is the switch plot section by providing a pair of isoform names. (B) is used to save top n plot into a local folder.
Any pair of switched transcript isoforms can be visualized by providing their names. Plot type options are error bar plot and ribbon plot (see functions geom_errorbar and geom_smooth in ggplot2 package for details) as shown in Figure 8(A) and example plots of G30 in Figure 9 and Figure 10. An option is provided to only label the features of switch points with probability/frequency of switch>cut-off in the time frame of interest. The plots can be saved in html (plotly format plot), png or pdf format.
Transcript isoform switch profiles can be plotted in batch by selecting top n (ranking with Score 1 probability/frequency of switch) pairs of isoforms into png or pdf format plots (see Figure 8(B)).
In addition to the Shiny App, users can use scripts to do TSIS analysis in R console. The following examples show a step-by-step tutorial of the analysis. Please refer to the function details using help function, e.g. help(iso.switch) or ?iso.switch.
##load the data
library(TSIS)
data.exp<-TSIS.data$data.exp
mapping<-TSIS.data$mapping
dim(data.exp);dim(mapping)Example 1: search intersection points with mean expression
##Scores
scores.mean2int<-iso.switch(data.exp=data.exp,mapping =mapping,
times=rep(1:26,each=9),rank=F,
min.t.points =2,min.difference=1,spline =F,
spline.df = 9,verbose = F)Example 2: search intersection points with spline method
##Scores, set spline=T and define spline degree of freedom to spline.df=9
scores.spline2int<-iso.switch(data.exp=data.exp,mapping =mapping,
times=rep(1:26,each=9),rank=F,
min.t.points =2,min.difference=1,spline =T,
spline.df = 10,verbose = F)Example 1: general filtering with cut-offs
##intersection from mean expression
scores.mean2int.filtered<-score.filter(
scores = scores.mean2int,prob.cutoff = 0.5,diff.cutoff = 1,
t.points.cutoff = 2,pval.cutoff = 0.001, cor.cutoff = 0,
data.exp = NULL,mapping = NULL,sub.isoform.list = NULL,
sub.isoform = F,max.ratio = F,x.value.limit = c(1,26)
)
scores.mean2int.filtered[1:5,]##intersection from spline method
scores.spline2int.filtered<-score.filter(
scores = scores.spline2int,prob.cutoff = 0.5,
diff.cutoff = 1,t.points.cutoff = 2,pval.cutoff = 0.01,
cor.cutoff = 0.5,data.exp = NULL,mapping = NULL,
sub.isoform.list = NULL,sub.isoform = F,max.ratio = F,
x.value.limit = c(9,17)
)Example 2: only show subset of results according to an isoform list
##intersection from mean expression
##input a list of isoform names for investigation.
sub.isoform.list<-TSIS.data$sub.isoforms
sub.isoform.list[1:10]
##assign the isoform name list to sub.isoform.list and set sub.isoform=TRUE
scores.mean2int.filtered.subset<-score.filter(
scores = scores.mean2int,prob.cutoff = 0.5,diff.cutoff = 1,
t.points.cutoff = 2,pval.cutoff = 0.01, cor.cutoff = 0.5,
data.exp = NULL,mapping = NULL,sub.isoform.list = sub.isoform.list,
sub.isoform = T,max.ratio = F,x.value.limit = c(9,17)
)Example 3: only show results of the most abundant transcript within a gene
scores.mean2int.filtered.maxratio<-score.filter(
scores = scores.mean2int,prob.cutoff = 0.5,diff.cutoff = 1,
t.points.cutoff = 2,pval.cutoff = 0.01, cor.cutoff = 0,
data.exp = data.exp,mapping = mapping,sub.isoform.list = NULL,
sub.isoform = F,max.ratio = T,x.value.limit = c(9,17)
)library(gridExtra)
g1<-switch.density(scores.mean2int.filtered$x.value,make.plotly = F,
show.line = F,plot.type = 'frequency',
title = 'Frequency of switch time' ,time.points = 1:26)
g2<-switch.density(scores.mean2int.filtered$x.value,make.plotly = F,
show.line = T,plot.type = 'density',
title = 'Density of switch time' ,time.points = 1:26)
gridExtra::grid.arrange(g1,g2,ncol=2)
plotTSIS(data2plot = data.exp,scores = scores.mean2int.filtered,
iso1 = 'G30.iso2',iso2 = 'G30.iso3',gene.name = NULL,
y.lab = 'Expression',make.plotly = F,
times=rep(1:26,each=9),prob.cutoff = 0.5,
x.lower.boundary = 9,x.upper.boundary = 17,
show.region = T,show.scores = T,
line.width =0.5,point.size = 3,
error.type = 'stderr',show.errorbar = T,errorbar.size = 0.5,
errorbar.width = 0.2,spline = F,spline.df = NULL,ribbon.plot = F)
plotTSIS(data2plot = data.exp,scores = scores.mean2int.filtered,
iso1 = 'G30.iso2',iso2 = 'G30.iso3',gene.name = NULL,
y.lab = 'Expression',make.plotly = F,
times=rep(1:26,each=9),prob.cutoff = 0.5,
x.lower.boundary = 9,x.upper.boundary = 17,
show.region = T,show.scores = T,
line.width =0.5,point.size = 3,
error.type = 'stderr',show.errorbar = T,errorbar.size = 0.5,
errorbar.width = 0.2,spline = F,spline.df = NULL,ribbon.plot = T)
Chang, W., et al. 2016. shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny
Hastie, T.J. and Tibshirani, R.J. Generalized additive models. Chapter 7 of Statistical Models in S eds. Wadsworth & Brooks/Cole 1992.
Sebestyen, E., Zawisza, M. and Eyras, E. Detection of recurrent alternative splicing switches in tumor samples reveals novel signatures of cancer. Nucleic Acids Res 2015;43(3):1345-1356.