Skip to content

Commit

Permalink
Update 05 practical
Browse files Browse the repository at this point in the history
  • Loading branch information
@AshKernow
AshKernow committed Sep 26, 2024
1 parent 5fe8ee6 commit 8dc8227
Show file tree
Hide file tree
Showing 3 changed files with 168 additions and 1,527 deletions.
219 changes: 76 additions & 143 deletions Markdowns/05_Data_Exploration.Rmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,8 @@ output:
bibliography: ref.bib
---

```{r setup, echo=FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width = 4, fig.height = 3)
knitr::opts_knit$set(root.dir = here::here("course_files"))
```{r setup, echo = FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width = 6, fig.height = 5)
```

# Introduction
Expand Down Expand Up @@ -43,10 +42,9 @@ In this session we will:

* import our counts into R
* filter out unwanted genes
* look at the effects of variance and how to mitigate this with data
transformation
* transform the data to mitigate the effects of variance
* do some initial exploration of the raw count data using principle component
analysis
analysis and hierarchical clustering

# Data import

Expand Down Expand Up @@ -118,25 +116,10 @@ head(txi$counts)

Save the `txi` object for use in later sessions.

```{r saveData, eval=FALSE}
```{r saveData, eval = FALSE}
saveRDS(txi, file = "salmon_outputs/txi.rds")
```

### Exercise 1
>
> We have loaded in the raw counts here. These are what we need for the
> differential expression analysis. For other investigations we might want
> counts normalised to library size. `tximport` allows us to import
> "transcript per million" (TPM) scaled counts instead.
>
> 1. Create a new object called `tpm` that contains length scaled TPM
> counts. You will need to add an extra argument to the command. Use the help
> page to determine how you need to change the code: `?tximport`.
```{r solutionExercise1}
```

### A quick intro to `dplyr`

One of the most complex aspects of learning to work with data in `R` is
Expand Down Expand Up @@ -170,24 +153,20 @@ rawCounts <- round(txi$counts, 0)

## Filtering the genes

<!-- prefiltering -->

For many analysis methods it is advisable to filter out as many genes as
possible before the analysis to decrease the impact of multiple testing
correction on false discovery rates. This is normally done
by filtering out genes with low numbers of reads and thus likely to be
uninformative.

With `DESeq2` this is however not necessary as it applies `independent
filtering` during the analysis. On the other hand, some filtering for
genes that are very lowly expressed does reduce the size of the data matrix,
meaning that less memory is required and processing steps are carried out
faster. Furthermore, for the purposes of visualization it is important to remove
the genes that are not expressed in order to avoid them dominating the patterns
that we observe.

We will keep all genes where the total number of reads across all samples is
greater than 5.
Many, if not most, of the genes in our annotation will not have been detected at
meaningful levels in our samples - very low counts are most likely technical
noise rather than biology. For the purposes of visualization it is important to
remove the genes that are not expressed in order to avoid them dominating the
patterns that we observe.

The level at which you filter at this stage will not effect the differential
expression analysis. The cutoff used for filtering is a balance between removing
noise and keeping biologically relevant information. A common approach is to
remove genes that have less than a certain number of reads across all samples.
The exact level is arbitrary and will depend to some extent on nature of the
dataset (overall read depth per sample, number of samples, balance of read depth
between samples etc). We will keep all genes where the total number of reads
across all samples is greater than 5.

```{r filterGenes}
# check dimension of count matrix
Expand All @@ -196,7 +175,7 @@ dim(rawCounts)
# keeping outcome in vector of 'logicals' (ie TRUE or FALSE, or NA)
keep <- rowSums(rawCounts) > 5
# summary of test outcome: number of genes in each class:
table(keep, useNA="always")
table(keep, useNA = "always")
# subset genes where test was TRUE
filtCounts <- rawCounts[keep,]
# check dimension of new count matrix
Expand All @@ -212,114 +191,67 @@ but for visualization purposes we use transformed counts.

Why not raw counts? Two issues:

* Raw counts range is very large
* Variance increases with mean gene expression, this has impact on assessing
the relationships.
* The range of values in raw counts is very large with many small values and a few
genes with very large values. This can make it difficult to see patterns in the
data.

```{r raw_summary}
summary(filtCounts)
```

```{r raw_boxplot}
# few outliers affect distribution visualization
boxplot(filtCounts, main='Raw counts', las=2)
boxplot(filtCounts, main = 'Raw counts', las = 2)
```

* Variance increases with mean gene expression, this has impact on assessing
the relationships, e.g. by clustering.

```{r raw_mean_vs_sd}
# Raw counts mean expression Vs standard Deviation (SD)
plot(rowMeans(filtCounts), rowSds(filtCounts),
main='Raw counts: sd vs mean',
xlim=c(0,10000),
ylim=c(0,5000))
main = 'Raw counts: sd vs mean',
xlim = c(0, 10000),
ylim = c(0, 5000))
```

## Data transformation

To avoid problems posed by raw counts, they can be [transformed](http://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#data-transformations-and-visualization).
Several transformation methods exist to limit the dependence of variance on mean gene expression:
To avoid problems posed by raw counts, they can be
[transformed](http://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#data-transformations-and-visualization).
A simple log2 transformation can be used to overcome the issue of the range of
values. Note, when using a log transformation, it is important to add a small
"pseudocount" to the data to avoid taking the log of zero.

* Simple log2 transformation
* VST : variance stabilizing transformation
* rlog : regularized log transformation

### log2 transformation

Because some genes are not expressed (detected) in some samples, their count are `0`. As log2(0) returns -Inf in R which triggers errors by some functions, we add 1 to every count value to create 'pseudocounts'. The lowest value then is 1, or 0 on the log2 scale (log2(1) = 0).

```{r logTransform}
# Get log2 counts
logcounts <- log2(filtCounts + 1)
```{r log2}
logCounts <- log2(filtCounts + 1)
boxplot(logCounts, main = 'Log2 counts', las = 2)
```

We will check the distribution of read counts using a boxplot and add some
colour to see if there is any difference between sample groups.

```{r plotLogCounts}
# make a colour vector
statusCols <- case_when(sampleinfo$Status=="Infected" ~ "red",
sampleinfo$Status=="Uninfected" ~ "orange")
# Check distributions of samples using boxplots
boxplot(logcounts,
xlab="",
ylab="Log2(Counts)",
las=2,
col=statusCols,
main="Log2(Counts)")
# Let's add a blue horizontal line that corresponds to the median
abline(h=median(logcounts), col="blue")
```
However, this transformation does not account for the variance-mean
relationship. DESeq2 provides two additional functions for transforming the
data:

From the boxplots we see that overall the density distributions of raw
log-counts are not identical but still not very different. If a sample is really
far above or below the blue horizontal line (overall median) we may need to
investigate that sample further.
* `VST` : variance stabilizing transformation
* `rlog` : regularized log transformation

```{r log2_mean_vs_sd}
# Log2 counts standard deviation (sd) vs mean expression
plot(rowMeans(logcounts), rowSds(logcounts),
main='Log2 Counts: sd vs mean')
```

In contrast to raw counts, with log2 transformed counts lowly expressed genes show higher variation.

### VST : variance stabilizing transformation

Variance stabilizing transformation (VST) aims at generating a matrix of values for which variance is constant across the range of mean values, especially for low mean.
As well as log2 transforming the data, both transformations produce data which
has been normalized with respect to library size and deal with the mean-variance
relationship. The effects of the two transformations are similar. `rlog` is
preferred when there is a large difference in library size between samples,
however, it is considerably slower than `VST` and is not recommended for large
datasets. For more information on the differences between the two
transformations see the
[paper](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0550-8)
and the DESeq2 vignette.

The `vst` function computes the fitted dispersion-mean relation, derives the transformation to apply and accounts for library size.

```{r vst_counts, message=FALSE}
vst_counts <- vst(filtCounts)
# Check distributions of samples using boxplots
boxplot(vst_counts,
xlab="",
ylab="VST counts",
las=2,
col=statusCols)
# Let's add a blue horizontal line that corresponds to the median
abline(h=median(vst_counts), col="blue")
```

```{r vst_mean_vs_sd}
# VST counts standard deviation (sd) vs mean expression
plot(rowMeans(vst_counts), rowSds(vst_counts),
main='VST counts: sd vs mean')
```

### Exercise 2
>
> 1. Use the `DESeq2` function `rlog` to transform the count data. This function
> also normalises for library size.
> 2. Plot the count distribution boxplots with this data
> How has this affected the count distributions?
```{r solutionExercise2}
Our data set is small, so we will use `rlog` for the transformation.

```{r rlog}
rlogcounts <- rlog(filtCounts)
boxplot(rlogcounts, main = 'rlog counts', las = 2)
```


# Principal Component Analysis

A principal component analysis (PCA) is an example of an unsupervised analysis,
Expand All @@ -345,7 +277,7 @@ is able to recognise common statistical objects such as PCA results or linear
model results and automatically generate summary plot of the results in an
appropriate manner.

```{r pcaPlot, message = FALSE, fig.width=6.5, fig.height=5, fig.align="center"}
```{r pcaPlot, message = FALSE, fig.width = 6.5, fig.height = 5, fig.align = "center"}
library(ggfortify)
rlogcounts <- rlog(filtCounts)
Expand All @@ -359,15 +291,15 @@ autoplot(pcDat)
We can use colour and shape to identify the Cell Type and the Status of each
sample.

```{r pcaPlotWiColor, message = FALSE, fig.width=6.5, fig.height=5, fig.align="center"}
```{r pcaPlotWiColor, message = FALSE, fig.width = 6.5, fig.height = 5, fig.align = "center"}
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5)
colour = "Status",
shape = "TimePoint",
size = 5)
```

### Exercise 3
### Exercise
>
> The plot we have generated shows us the first two principle components. This
> shows us the relationship between the samples according to the two greatest
Expand All @@ -392,16 +324,16 @@ Let's identify these samples. The package `ggrepel` allows us to add text to
the plot, but ensures that points that are close together don't have their
labels overlapping (they *repel* each other).

```{r badSamples, fig.width=6.5, fig.height=5, fig.align="center"}
```{r badSamples, fig.width = 6.5, fig.height = 5, fig.align = "center"}
library(ggrepel)
# setting shape to FALSE causes the plot to default to using the labels instead of points
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5) +
geom_text_repel(aes(x=PC1, y=PC2, label=SampleName), box.padding = 0.8)
colour = "Status",
shape = "TimePoint",
size = 5) +
geom_text_repel(aes(x = PC1, y = PC2, label = SampleName), box.padding = 0.8)
```

The mislabelled samples are *SRR7657882*, which is labelled as *Infected* but
Expand All @@ -411,26 +343,26 @@ should be *Infected*. Let's fix the sample sheet.
We're going to use another `dplyr` command `mutate`.

```{r correctSampleSheet}
sampleinfo <- mutate(sampleinfo, Status=case_when(
sampleinfo <- mutate(sampleinfo, Status = case_when(
SampleName=="SRR7657882" ~ "Uninfected",
SampleName=="SRR7657873" ~ "Infected",
TRUE ~ Status))
```

...and export it so that we have the correct version for later use.

```{r, exportSampleSheet, eval=FALSE}
```{r, exportSampleSheet, eval = FALSE}
write_tsv(sampleinfo, "results/SampleInfo_Corrected.txt")
```

Let's look at the PCA now.

```{r correctedPCA, fig.width=6.5, fig.height=5, fig.align="center"}
```{r correctedPCA, fig.width = 6.5, fig.height = 5, fig.align = "center"}
autoplot(pcDat,
data = sampleinfo,
colour="Status",
shape="TimePoint",
size=5)
colour = "Status",
shape = "TimePoint",
size = 5)
```

Replicate samples from the same group cluster together in the plot, while
Expand Down Expand Up @@ -467,7 +399,7 @@ library(ggdendro)
hclDat <- t(rlogcounts) %>%
dist(method = "euclidean") %>%
hclust()
ggdendrogram(hclDat, rotate=TRUE)
ggdendrogram(hclDat, rotate = TRUE)
```

We really need to add some information about the sample groups. The simplest way
Expand All @@ -479,8 +411,9 @@ sample meta data table. We can just substitute in columns from the metadata.
```{r}
hclDat2 <- hclDat
hclDat2$labels <- str_c(sampleinfo$Status, ":", sampleinfo$TimePoint)
ggdendrogram(hclDat2, rotate=TRUE)
ggdendrogram(hclDat2, rotate = TRUE)
```

We can see from this that the infected and uninfected samples cluster separately
and that day 11 and day 33 samples cluster separately for infected samples, but
not for uninfected samples.
Expand Down
1,476 changes: 92 additions & 1,384 deletions Markdowns/05_Data_Exploration.html
View file

Large diffs are not rendered by default.

Binary file modified Markdowns/05_Data_Exploration.pdf
View file
Binary file not shown.

0 comments on commit 8dc8227

Please sign in to comment.