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https://github.com/pkimes/spliceclust

visualization tools for exon/junction coverage
https://github.com/pkimes/spliceclust

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visualization tools for exon/junction coverage

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README 

        
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=======================



## Contents

1. [Introduction](#intro)

2. [SpliceGraHM Examples](#splicegrahm)

3. [SplicePCA Examples](#splicepca)

4. [SplicePCP Examples](#splicepcp)

4. [SpliceGraHM2 Examples](#splicegrahm2)



##  Introduction



This package may be used to plot exon and splice junction coverage across a large cohort

of RNA-seq samples. The plotting approach is based on the idea of transposing

expression heatmaps on splicing diagrams. The plots are generated using the `ggplot2` and

`ggbio` packages.



```{r, warning=FALSE, message=FALSE}

## load annotations packages

library("TxDb.Hsapiens.UCSC.hg19.knownGene")

library("BSgenome.Hsapiens.UCSC.hg19")



## load current package

devtools::load_all()

```



##  SpliceGraHM Examples



First we must construct a `concomp` object from a `GRangesList` object with exon and junction

boundaries and corresponding coverage values. For illustration purposes, we use a previously

constructed `GRanges` object, `klk12_lusc_gr`, containing annotation information for the KLK12 gene

locus along with coverage for 177 lung squamous cell carcinoma samples.  



```{r}

data("klk12_lusc_gr")

```



The exon and junction boundaries, and the corresponding kind label are given by:  



```{r}

ranges(klk12_lusc_gr)

mcols(klk12_lusc_gr)$kind

```



In addition to `kind`, the `klk12_lusc_gr` metadata columns also include the gene name (`gIdx`),

gene boundaries (`gStart`, `gStop`), and coverage values for the 177 samples.  



```{r}

mcols(klk12_lusc_gr)[1:5, 1:6]

```



To construct the `concomp` (connected component) object for analysis, we first convert the

`GRanges` object into a `GRangesList` object of length 2, corresponding to exon and junction

information.  



```{r}

klk12_gl <- split(klk12_lusc_gr, mcols(klk12_lusc_gr)$kind)

klk12_cc <- concomp(klk12_gl)

```



We first demonstrate the default and __basic SpliceGraHM__ (Splice Graph Heat Map) plotting procedure.



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc)

```



In the plot above, each box arranged horizontally corresponds to a contiguous exonic region

along the genome. Each box is colored by 177 horizontal lines, showing the expression level

for the 177 samples being analyzed. Note that the exons are plotted along genomic coordinates, and

log expression is shown in color.  



In addition to the boxes, the plot contains arrows which correspond to a splicing events with

sufficient support in the data (e.g. at least 8 samples, each with at least 5 reads spanning the splice

junction). The arrows are colored such that darker arrows were present in a higher proportion of the

177 samples (scale shown on right).  



The SpliceGraHM name is in reference to the fact that after removing the spacing between each exon

the plot simply reduces to a __standard heatmap__ of expression along a single gene. The corresponding

figure after removing the intronic gaps is shown below. The rectangle of color is a heatmap with rows

and columns corresponding to samples and exons, respectively.  



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc, genomic = FALSE, ex_use = 1, log_base = 2)

```



It is possible to show the coverage of each splice junction using a similar convention with rows

corresponding to samples, and with color being used for coverage.



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc, genomic = FALSE, j_incl = TRUE)

```



If annotation information is available, e.g. from UCSC KnownGenes, these can be passed to the

function to add an additional track to the plot. The appropriate `GRangesList` object to be

passed is illustrated in the following example.



```{r, fig.width=12, fig.height=4}

txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene

isActiveSeq(txdb)[seqlevels(txdb)] <- FALSE

isActiveSeq(txdb)[paste0("chr", 1:22)] <- TRUE

exbytx <- exonsBy(txdb, "tx")



splicegrahm(klk12_cc, genomic = TRUE, j_incl = TRUE, txlist = exbytx)

```



If gene names are desired, the following can be used to match the transcript ID

in `txdb` against gene symbols (e.g. in `org.Hs.eg.db`).



```{r, fig.width=12, fig.height=4}

suppressPackageStartupMessages(library("org.Hs.eg.db"))



splicegrahm(klk12_cc, genomic = TRUE, j_incl = TRUE, txlist = exbytx,

            txdb = txdb, orgdb = org.Hs.eg.db)

```



The `splicegrahm` function can now also plot gene models in non-genomic space

with an additional parameter `eps`. The `eps` parameter determines how far up/down

from the connected component to lookfor overlapping gene models. If `eps = NULL`,

all overlapping gene models are included. If `eps = 1000`, only overlapping gene

models which are fully contained within 1000bp of the connected component

are included.



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc, genomic = FALSE, j_incl = TRUE, txlist = exbytx,

            txdb = txdb, orgdb = org.Hs.eg.db, eps = NULL)

```



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc, genomic = FALSE, j_incl = TRUE, txlist = exbytx,

            txdb = txdb, orgdb = org.Hs.eg.db, eps = 1)

```



```{r, fig.width=12, fig.height=4}

splicegrahm(klk12_cc, genomic = FALSE, j_incl = TRUE, txlist = exbytx,

            txdb = txdb, orgdb = org.Hs.eg.db, eps = 1000)

```



##  SplicePCA Examples



Principal Component Analysis (PCA) is a popular exploratory analysis tool for studying low rank

structure in high-dimensional datasets by projecting the data along the directions of greatest

variation. These directions of greatest variation are often referred to as the PC "loading"

directions. The `splicepca` function is written to visualize the loading vectors of splicing

data.  



As an example, the following code can be used to visualize the first 3 PC loadings in the

KLK12 dataset from above.



```{r, fig.width=10, fig.height=4}

splicepca(klk12_cc, npc = 3)

```



In the above PC loadings plot, red and blue are used to denote the magnitude of the PC loadings

for each exon and splice junction. From the plot above, we see that the greatest source of variation

in expression at the KLK12 gene locus is the overall expression of the gene. However, the second

PC loading shows interesting behavior where the primary source of variation is attributable to the

expression of the central exon. Scrolling up a little to the UCSC KnownGenes shown included above,

we see that the presence or absence of the central exon actually corresponds to differing isoforms

annotated to this gene. Note that above the PCs are computed separately for exons and junctions

 information. As such, we obtain separate PC "scores" for the exon and junction PC loadings.

 

```{r, fig.width=6, fig.height=6}

splicepca(klk12_cc, npc = 3, scores = TRUE)

```



It is also possible to perform the PCA analysis using the concatenated exon and junction information by

setting the `pc_sep` parameter to `FALSE`, and specifying the relative "weight" of each with `ej_w`. The

exon and junction data are rescaled to each have sum-of-squares equal to the values specified by `ej_w`.

In the following example, we use equal weights for the two data sources.



```{r, fig.width=10, fig.height=4}

splicepca(klk12_cc, npc = 3, pc_sep = FALSE, ej_w = c(1, 1))

splicepca(klk12_cc, npc = 3, pc_sep = FALSE, ej_w = c(1, 1), scores = TRUE)

```



##  SplicePCP Examples



All above plots have used color to represent expression. However, low frequency or outlier events may

become lost in this particular view. To handle this problem, we also provide the option to plot the

splicing objects with vertical height (the y-axis) corresponding to expression. The name of the function is

a reference to [_parallel coordinates plots_](pcp). The same _KLK12_ data is shown below using the

`splicepcp` function.



```{r, fig.width=12, fig.height=5}

splicepcp(klk12_cc, genomic = TRUE, txlist = exbytx, txdb = txdb, orgdb = org.Hs.eg.db)

```



The plot includes 3 tracks:

  1. log-expression values for each exon or region of an exon

  2. the complete gene model and splice junctions

  3. annotated transcripts from the UCSC KnownGene database.



Currently, the function is being rewritten to also include junction coverage using a

parallel coordinates plot in a separate track.



##  SpliceGraHM2 Examples



To make direct comparison of two populations possible, we have created `splicegrahm2` which

draws two `splicegrahm` plots in a single figure. Consider, for example, the task of comparing

the behavior of splicing between the LUSC samples and head and neck squamous cell carcinoma (HNSC)

samples. We have loaded a connected component for 279 HNSC samples as `klk12_hnsc_cc`.



```{r}

data("klk12_hnsc_cc")

```



```{r, fig.width=12, fig.height=5}

splicegrahm2(klk12_cc, klk12_hnsc_cc, genomic=FALSE)

```



When gene models are also desired, they are placed in a central track for easy comparison to

the two `splicegrahm`s.



```{r, fig.width=12, fig.height=6}

splicegrahm2(klk12_cc, klk12_hnsc_cc, genomic=FALSE,

             txlist = exbytx, txdb = txdb, orgdb = org.Hs.eg.db)

```



Alternatively, the bottom plot can be drawn with the same orientation as the top plot by

setting `mirror=FALSE`.



```{r, fig.width=12, fig.height=6}

splicegrahm2(klk12_cc, klk12_hnsc_cc, genomic=FALSE, mirror=FALSE)

```



While not modified above, the option ``same_scale`` can be used to specify whether

the two plots should be drawn using the same or separate scale along the y-axis.

This can be helpful when the two populations are of substantially different sizes.



##  Session Information



```{r}

sessionInfo()

```



[pcp]: http://en.wikipedia.org/wiki/Parallel_coordinates