---
title: "Getting started: scHOT"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Getting started: scHOT}
%\usepackage[UTF-8]{inputenc}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
message = FALSE,
warning = FALSE,
comment = "#>"
)
```
# Introduction
Single cell Higher Order Testing (scHOT) is an R package that facilitates
testing changes in higher order structure along either a developmental
trajectory or across space. In this vignette, we go through two example
analyses: 1) Testing variability changes along liver trajectory, and 2) Testing
differential correlation across the mouse olfactory bulb.
```{r}
library(SingleCellExperiment)
library(ggplot2)
library(scHOT)
library(scater)
library(matrixStats)
```
# Testing variability changes along liver trajectory
The `liver` dataset contains data from two branches of a developmental
trajectory, starting from immature hepatoblasts and bifurcates into either
the hepatocyte or cholangiocyte lineages. For file size
reasons we only load up the hepatocyte lineage for a small number of genes.
In this example, we take the cells
that belong to the initial part of the trajectory, i.e. from hepatoblast to
the bifurcation point, and test for differential variability along this
trajectory for a few genes.
```{r}
data(liver)
liver_pseudotime_hep <- liver$liver_pseudotime_hep
liver_branch_hep <- liver$liver_branch_hep
first_branch_cells <- liver$first_branch_cells
```
```{r}
gene_to_test <- as.matrix(c("Birc5", "H2afz", "Tacc3"))
```
## Build the `scHOT` object
First we build the `scHOT` object, which is based on the `SingleCellExperiment`
object class. `scHOT` objects can be built either from a matrix format or from
an existing `SingleCellExperiment` object. In this case, we have matrix data
so we build the `scHOT` object using `scHOT_buildFromMatrix`. Since the liver
data represents a trajectory, we set the `positionType` as `"trajectory"`, and
provide the column name of the cell metadata (argument `cellData`) for which
the cells should be ordered.
```{r}
scHOT_traj <- scHOT_buildFromMatrix(
mat = liver_branch_hep[,first_branch_cells],
cellData = list(pseudotime = liver_pseudotime_hep[first_branch_cells]),
positionType = "trajectory",
positionColData = "pseudotime")
scHOT_traj
```
`scHOT_traj` is a `scHOT` object, but methods associated with
`SingleCellExperiment` can also be used. For example, we use the `scater`
package to plot the expression of the hepatoblast marker _Sall4_ along
pseudotime, and note that this decreases as pseudotime increases.
```{r}
scater::plotExpression(scHOT_traj, c("Sall4"),
exprs_values = "expression", x = "pseudotime")
```
## scHOT wrapper function
Now using the `scHOT` wrapper function, we can perform higher order testing on
the selected genes, provided as a one-column matrix. To do this, we also need
to set the underlying higher order function, which in this case we use weighed
variance as implemented in the `matrixStats` package. Since this function has
a weight parameter, we set `higherOrderFunctionType = "weighted"`. For basic
implementation, no other parameters need to be specified (for speed, we set
`numberPermutations` to a small value).
```{r}
scHOT_traj_wrap = scHOT(scHOT_traj,
testingScaffold = gene_to_test,
higherOrderFunction = matrixStats::weightedVar,
higherOrderFunctionType = "weighted",
numberPermutations = 50)
```
Output is saved as a `DataFrame` in the `scHOT_output` slot, accessible either
using the `slot` function, or using the `@` accessor. In particular, we can
interrogate the higher order sequence, the sequence of locally weighted
variances along the trajectory. We can see from the plot that each of these
genes increases in variability along pseudotime. Note that the plots are
based on `ggplot2` and so can be customised as desired.
```{r}
slot(scHOT_traj_wrap, "scHOT_output")
scHOT_traj_wrap@scHOT_output
slot(scHOT_traj_wrap, "scHOT_output")$higherOrderSequence
plotHigherOrderSequence(scHOT_traj_wrap, gene_to_test)
plotOrderedExpression(scHOT_traj_wrap, gene_to_test) +
facet_wrap(~gene, scales = "free_y")
```
## scHOT step-by-step
Now, we can perform the same testing but step-by-step, with description of the
parameter selection at each step.
First, we add the testing scaffold. This is the set of genes for which we wish
to perform higher order testing.
```{r}
scHOT_traj@testingScaffold
scHOT_traj <- scHOT_addTestingScaffold(scHOT_traj, gene_to_test)
scHOT_traj@testingScaffold
```
Next, we provide parameters to build a weighting scheme. The most important
parameter here is `span`, a value between 0 and 1 (default 0.25) which
determines how large or small a window we wish to use for higher order testing.
To distinguish between either ranked samples in a trajectory or in a spatial
setting, we set `positionType = "trajectory"` and instruct to extract the
trajectory ordering from the `"pseudotime"` column from `colData(scHOT_traj)`.
```{r}
scHOT_traj@weightMatrix
scHOT_traj <- scHOT_setWeightMatrix(scHOT_traj,
positionType = "trajectory",
positionColData = c("pseudotime"),
nrow.out = NULL,
span = 0.25)
dim(scHOT_traj@weightMatrix)
class(scHOT_traj@weightMatrix)
plot(scHOT_traj@weightMatrix[50,])
```
By default the weight matrix is square, with as many rows as the number of
cells (columns), but if you have especially large data, you may want to reduce
the weight matrix rows for faster runtimes. Here we reduce the weight matrix to
roughly 50 rows using the `nrow.out` arguemnt. Note you can provide your own
pre-prepared matrix using the `weightMatrix` argument.
```{r}
scHOT_traj <- scHOT_setWeightMatrix(scHOT_traj,
positionType = "trajectory",
positionColData = c("pseudotime"),
nrow.out = 50,
span = 0.25)
dim(scHOT_traj@weightMatrix)
class(scHOT_traj@weightMatrix)
plot(scHOT_traj@weightMatrix[50,])
```
Now we calculate the global higher order function, in this case is simply the
sample variance giving equal weight to all cells. This becomes important when you
wish to test many genes and use a fast p-value estimation approach to speed up
computation.
```{r}
scHOT_traj <- scHOT_calculateGlobalHigherOrderFunction(
scHOT_traj,
higherOrderFunction = matrixStats::weightedVar,
higherOrderFunctionType = "weighted")
slot(scHOT_traj, "scHOT_output")
apply(assay(scHOT_traj, "expression")[c(gene_to_test),],1,var)
```
`scHOT` allows for a lot of customisation. In particular, you can set which
tests you wish to perform permutation testing for, and if so, what number of
permutations can be used, and if they should be stored for later use. Here, we
set the number of permutations to a mix of 20 and 50. Allowing different
permutation numbers is useful if we wish to perform a lot of permutations for
one test, and few for another.
Note if you wish to explicitly set the number of permutations for all tests,
then ensure that `numberScaffold` is set above the number of tests (default
100).
`scHOT_setPermutationScaffold` only gives the instructions for running
permutation testing, it doesn't actually perform the testing.
```{r}
scHOT_traj <- scHOT_setPermutationScaffold(scHOT_traj,
numberPermutations = c(20,50,20),
storePermutations = c(TRUE, FALSE, TRUE))
slot(scHOT_traj, "scHOT_output")
```
Now we can calculate the observed test statistic. This is calculated as a
summary function (default standard deviation) of the local higher order
sequence, which is parametrised using the higher order function (here weighted
variance) and weighting scheme (here thinned triangular weight matrix
with span 0.25).
```{r}
scHOT_traj <- scHOT_calculateHigherOrderTestStatistics(
scHOT_traj,
higherOrderSummaryFunction = sd)
slot(scHOT_traj, "scHOT_output")
```
Once the test statistic is calculated, we perform permutation testing, using
the instructions we provided earlier.
```{r}
system.time(scHOT_traj <- scHOT_performPermutationTest(
scHOT_traj,
verbose = TRUE,
parallel = FALSE))
slot(scHOT_traj, "scHOT_output")
```
To avoid P-values identically zero, we rescale zero P-values to
1/(1+numberPermutations).
We could also use the existing stored permutations to estimate P-values,
since genes with a similar global higher order function are likely to have a
similar null distribution. In this example case it does not show much gain
computationally, but this can significantly reduce the number of permutation
tests needed for large datasets and testing strategies.
Running `scHOT_estimatePvalues` results in more columns in the `scHOT_output`
slot, corresponding to the number of permutations used in estimating, the
range of the global higher order function used for estimating, as well as the
estimated P-value itself and FDR adjusted P-value. Here, we set a maximum of
10,000 permutations to be used, for genes with at most a difference of 5 in the
global higher order function (the variance).
```{r}
scHOT_traj <- scHOT_estimatePvalues(scHOT_traj,
nperm_estimate = 10000,
maxDist = 5)
slot(scHOT_traj, "scHOT_output")
```
Note that having performed the testing using the thinned weight matrix, we can
still plot, but beware that not every position is sampled.
```{r}
plotHigherOrderSequence(scHOT_traj, gene_to_test)
```
# Spatial differential correlation in Mouse Olfactory Bulb
In this example, we look at the Spatial Transcriptomics mouse olfactory bulb
data. This data is provided in the form of a `SingleCellExperiment` object,
with the spatial coordinates provided in the `colData` slot. For file size
reasons we only load up a small number of genes, corresponding to highly
variable genes (HVGs) that are not found to be significantly
differentially expressed in space.
```{r}
data(MOB_subset)
sce_MOB_subset <- MOB_subset$sce_MOB_subset
sce_MOB_subset
```
We build the `scHOT` object using `scHOT_buildFromSCE`. Note that `scHOT` only
takes in a single `assay` slot, for which testing is based on.
```{r}
scHOT_spatial <- scHOT_buildFromSCE(sce_MOB_subset,
assayName = "logcounts",
positionType = "spatial",
positionColData = c("x", "y"))
scHOT_spatial
```
## Perform scHOT step by step - skip [ahead](#perform-schot-using-schot-wrapper-function) for wrapper using `scHOT`
In this example, we want to perform spatial differential correlation testing
between distinct pairs of genes. We build up our testing scaffold by taking
all pairs of the non-differentially expressed HVGs. For practicality, we only
consider a small set of pairs for testing here.
```{r}
pairs <- t(combn(rownames(sce_MOB_subset),2))
rownames(pairs) <- apply(pairs,1,paste0,collapse = "_")
head(pairs)
set.seed(2020)
pairs <- pairs[sample(nrow(pairs), 20), ]
if (!"Arrb1_Mtor" %in% rownames(pairs)) {
pairs <- rbind(pairs, "Arrb1_Mtor" = c("Arrb1", "Mtor"))
}
if (!"Dnm1l_Fam63b" %in% rownames(pairs)) {
pairs <- rbind(pairs, "Dnm1l_Fam63b" = c("Dnm1l", "Fam63b"))
}
scHOT_spatial <- scHOT_addTestingScaffold(scHOT_spatial, pairs)
scHOT_spatial@testingScaffold
```
Note that since we are performing differential correlation testing, our
testing scaffold is a matrix with two columns. If you wish to use some higher
order function with more than two genes, you can simply add more columns to the
testing scaffold, and ensure there are more arguments in the provided
higher order function.
Now we set the weight matrix, using the positional coordinates in the `scHOT`
object. The `span` parameter here corresponds to the proportion of cells that
have nonzero values around a radius of the central cell.
This can also be thinned for faster computation by setting the `nrow.out`
argument to the number of samples to roughly thin to.
```{r}
scHOT_spatial <- scHOT_setWeightMatrix(scHOT_spatial,
positionColData = c("x","y"),
positionType = "spatial",
nrow.out = NULL,
span = 0.05)
dim(slot(scHOT_spatial, "weightMatrix"))
```
We can visualise the weighting scheme for each row of the weight matrix.
```{r}
cellID = 75
ggplot(as.data.frame(colData(scHOT_spatial)), aes(x = -x, y = y)) +
geom_point(aes(colour = slot(scHOT_spatial, "weightMatrix")[cellID,],
size = slot(scHOT_spatial, "weightMatrix")[cellID,])) +
scale_colour_gradient(low = "black", high = "purple") +
scale_size_continuous(range = c(1,5)) +
theme_classic() +
guides(colour = guide_legend(title = "Spatial Weight"),
size = guide_legend(title = "Spatial Weight")) +
ggtitle(paste0("Central cell: ", cellID))
```
Now we can calculate the global higher order function. In this case, we use
weighted Spearman correlation as our weighted higher order function, and so
this is simply equivalent to calculating Spearman correlation for the gene pairs
of interest.
```{r}
scHOT_spatial <- scHOT_calculateGlobalHigherOrderFunction(
scHOT_spatial,
higherOrderFunction = weightedSpearman,
higherOrderFunctionType = "weighted")
slot(scHOT_spatial, "scHOT_output")
head(diag(cor(t(assay(scHOT_spatial, "expression")[pairs[,1],]),
t(assay(scHOT_spatial, "expression")[pairs[,2],]),
method = "spearman")))
```
Now we can set the permutation parameters. In this case, we only perform 50
permutations for around 10 randomly selected tests.
```{r}
scHOT_spatial <- scHOT_setPermutationScaffold(scHOT_spatial,
numberPermutations = 50,
numberScaffold = 10)
slot(scHOT_spatial, "scHOT_output")
```
Now we calculate the observed higher order test statistics, which in this case
correspond to the summary of local correlation vectors.
```{r}
scHOT_spatial <- scHOT_calculateHigherOrderTestStatistics(scHOT_spatial)
slot(scHOT_spatial, "scHOT_output")
```
Now we perform permutation testing for those tests which we provided with a
nonzero value for number of permutations. This takes about a minute to run.
```{r}
system.time(scHOT_spatial <- scHOT_performPermutationTest(
scHOT_spatial,
verbose = TRUE,
parallel = FALSE))
slot(scHOT_spatial, "scHOT_output")
```
With the above, we calculated P-values for some of the tests, but we have not
performed permutation testing for all tests. Here, we employ a
permutation sharing approach to estimate significance for the other tests.
Ideally this would be performed with around a hundred tests each with around
1,000 permutations to ensure accurate P-value estimation. You can check how
good an estimate you can expect by plotting the global higher order statistic
against the permuted test statistics, there should be good representation
along the x-axis and the fitted curve should appear quite smooth. Here the
fitted curve is bumpy, so we would suggest selecting more genes and more
permutations.
We estimate P-values by borrowing 100 permutations from closest tests with a
difference in global higher order statistic of at most 0.1
```{r}
scHOT_plotPermutationDistributions(scHOT_spatial)
scHOT_spatial <- scHOT_estimatePvalues(scHOT_spatial,
nperm_estimate = 100,
maxDist = 0.1)
slot(scHOT_spatial, "scHOT_output")
ggplot(as.data.frame(slot(scHOT_spatial, "scHOT_output")),
aes(x = -log10(pvalPermutations), y = -log10(pvalEstimated))) +
geom_point() +
theme_classic() +
geom_abline(slope = 1, intercept = 0) +
xlab("Permutation -log10(p-value)") +
ylab("Estimated -log10(p-value)") +
NULL
```
In this example we can still see fairly good concordance between the estimated
and direct permutation tests, even with a very small number of permutations.
Once testing is done, we can interrogate the results with various plots. The
`plotHigherOrderSequence` function will plot the points in space, coloured by
the local correlation estimates, and `plotOrderedExpression` will plot the
points in space, coloured by expression of each gene.
```{r}
colData(scHOT_spatial)[, "-x"] <- -colData(scHOT_spatial)[, "x"]
plotHigherOrderSequence(scHOT_spatial, c("Dnm1l_Fam63b"),
positionColData = c("-x", "y"))
plotOrderedExpression(scHOT_spatial, c("Dnm1l", "Fam63b"),
positionColData = c("-x", "y"),
assayName = "expression")
```
## Perform scHOT using `scHOT` wrapper function
Strip the existing scHOT output using `scHOT_stripOutput` before rerunning
scHOT in a new context.
```{r}
scHOT_spatial <- scHOT_stripOutput(scHOT_spatial, force = TRUE)
scHOT_spatial
slot(scHOT_spatial, "scHOT_output")
```
We can perform `scHOT` in a single wrapper function, which will perform the
steps as described above, with all parameters given at once.
```{r}
scHOT_spatial <- scHOT(scHOT_spatial,
testingScaffold = pairs,
positionType = "spatial",
positionColData = c("x", "y"),
nrow.out = NULL,
higherOrderFunction = weightedSpearman,
higherOrderFunctionType = "weighted",
numberPermutations = 50,
numberScaffold = 10,
higherOrderSummaryFunction = sd,
parallel = FALSE,
verbose = FALSE,
span = 0.05)
slot(scHOT_spatial, "scHOT_output")
```
Again, we can examine the results in terms of the spatial expression and the
local higher order sequences.
```{r}
plotOrderedExpression(scHOT_spatial, c("Arrb1", "Mtor"),
positionColData = c("-x", "y"),
assayName = "expression")
plotHigherOrderSequence(scHOT_spatial, "Arrb1_Mtor",
positionColData = c("-x", "y"))
```
# Misc
```{r}
sessionInfo()
```