CiteFuse example
Last updated: 2020-11-24
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Knit directory: interaction-tools/
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# Setup document
source(here::here("code", "setup.R"))
# Function dependencies
invisible(drake::readd(download_link))Introduction
In this document we are going to run through the example analysis for the CiteFuse package and have a look at the output it produces. This package is primarily for analysing CITE-seq data but it’s cell-cell interaction function also works with regular scRNA-seq. More information about CiteFuse can be found at https://github.com/SydneyBioX/CiteFuse/.
library("CiteFuse")Chunk time: 5.71 secs
1 Input
1.1 Expression
The main input for CiteFuse is provided as a list of matrices:
data("CITEseq_example", package = "CiteFuse")Chunk time: 0.26 secs
The list contains 3 items: RNA, ADT and HTO
For this example we will just use the RNA matrix.
pander(as.matrix(CITEseq_example$RNA[1:5, 1:5]))| AAGCCGCGTTGTCTTT | GATCGCGGTTATCGGT | GGCTGGTAGAGGTTAT | |
|---|---|---|---|
| hg19_AL627309.1 | 0 | 0 | 0 |
| hg19_AL669831.5 | 0 | 0 | 0 |
| hg19_FAM87B | 0 | 0 | 0 |
| hg19_LINC00115 | 0 | 0 | 0 |
| hg19_FAM41C | 0 | 0 | 0 |
| CTACACCCAATAGCAA | ACAGCTACAGATCGGA | |
|---|---|---|
| hg19_AL627309.1 | 0 | 0 |
| hg19_AL669831.5 | 0 | 0 |
| hg19_FAM87B | 0 | 0 |
| hg19_LINC00115 | 0 | 0 |
| hg19_FAM41C | 0 | 0 |
Chunk time: 0.2 secs
The full matrix has 19521 rows (genes) and 500 columns (cells).
1.2 Ligand-receptor pairs
The other thing we need is a set of ligand-receptor pairs. For this example we use a subset provided with the package as a matrix. Because we are only use the RNA data we need to add a prefix to the receptor column.
data("lr_pair_subset", package = "CiteFuse")
# Modify the pair names because we are using just RNA
modified_lr_pairs <- lr_pair_subset
modified_lr_pairs[, 2] <- paste0("hg19_", modified_lr_pairs[, 2])
pander(as.matrix(modified_lr_pairs[1:5, 1:2]))| hg19_IL17RA | hg19_CD45 |
| hg19_FAS | hg19_CD11b |
| hg19_GZMK | hg19_CD62L |
| hg19_CD40LG | hg19_CD11b |
| hg19_FLT3LG | hg19_CD62L |
Chunk time: 0.02 secs
The matrix has 50 rows (pairs) and 2 columns.
2 Pre-processing
The first step is to create a SingleCellExperiment object to hold the expression data.
sce <- preprocessing(CITEseq_example$RNA)
sceclass: SingleCellExperiment
dim: 19521 500
metadata(0):
assays(1): counts
rownames(19521): hg19_AL627309.1 hg19_AL669831.5 ... hg19_MT-ND6
hg19_MT-CYB
rowData names(0):
colnames(500): AAGCCGCGTTGTCTTT GATCGCGGTTATCGGT ... TTGGCAACACTAGTAC
GCTGCGAGTTGTGGCC
colData names(0):
reducedDimNames(0):
altExpNames(0):Chunk time: 0.29 secs
The dataset has 19521 genes and 500 cells.
3 Normalisation
Next we normalise the expression values. This is done in two steps where the scater logNormCounts() is used to correct for sequencing depth and log-transform before a min-max transformation is applied.
sce <- scater::logNormCounts(sce)
sce <- normaliseExprs(
sce,
altExp_name = "none",
exprs_value = "logcounts",
transform = "minMax"
)
sceclass: SingleCellExperiment
dim: 19521 500
metadata(0):
assays(3): counts logcounts minMax
rownames(19521): hg19_AL627309.1 hg19_AL669831.5 ... hg19_MT-ND6
hg19_MT-CYB
rowData names(0):
colnames(500): AAGCCGCGTTGTCTTT GATCGCGGTTATCGGT ... TTGGCAACACTAGTAC
GCTGCGAGTTGTGGCC
colData names(1): sizeFactor
reducedDimNames(0):
altExpNames(0):Chunk time: 3.54 secs
4 Clustering
This example dataset doesn’t come with cluster labels. In the original dataset they perform clustering on the combined CITE-seq data, but here we will quickly cluster just the RNA.
**NOTE: This may not give ideal clusters and could be the cause of any weirdness in the following sections.
sce <- scater::runPCA(sce)
graph <- scran::buildSNNGraph(sce, k=10, use.dimred = "PCA")
clusters <- igraph::cluster_louvain(graph)$membershipChunk time: 0.57 secs
This gives us 6 clusters.
5 Testing
Once the data is normalised and clustered we can perform the test for ligand-receptor activity.
# Modify the pair names because we are using just RNA
modified_lr_pairs <- lr_pair_subset
modified_lr_pairs[, 2] <- paste0("hg19_", modified_lr_pairs[, 2])
sce <- ligandReceptorTest(
sce,
ligandReceptor_list = modified_lr_pairs,
cluster = factor(clusters),
RNA_exprs_value = "minMax",
use_alt_exp = FALSE,
num_permute = 1000
)100 ......200 ......300 ......400 ......500 ......600 ......700 ......800 ......900 ......1000 ......Chunk time: 39.59 secs
6 Visualisation
Let’s have a look at the results.
6.1 Heatmap
Heatmap of p-values for ligand-receptor pairs.
visLigandReceptor(
sce,
type = "pval_heatmap",
receptor_type = "RNA"
)
| Version | Author | Date |
|---|---|---|
| d85e512 | Luke Zappia | 2020-11-09 |
Chunk time: 0.4 secs
6.2 Dot plot
Dot plot of p-values for ligand-receptor pairs.
visLigandReceptor(
sce,
type = "pval_dotplot",
receptor_type = "RNA"
)
| Version | Author | Date |
|---|---|---|
| d85e512 | Luke Zappia | 2020-11-09 |
Chunk time: 0.73 secs
6.3 Cluster network
Network of interactions between clusters.
visLigandReceptor(
sce,
type = "group_network",
receptor_type = "RNA"
)
| Version | Author | Date |
|---|---|---|
| d85e512 | Luke Zappia | 2020-11-09 |
Chunk time: 0.46 secs
6.4 Cluster heatmap
Heatmap of interactions between clusters.
visLigandReceptor(
sce,
type = "group_heatmap",
receptor_type = "RNA"
)
| Version | Author | Date |
|---|---|---|
| d85e512 | Luke Zappia | 2020-11-09 |
Chunk time: 0.13 secs
6.5 Ligand-receptor network
Network of ligands and receptors. Here the nodes are ligands and receptors and the edges are interactions between clusters.
visLigandReceptor(
sce,
type = "lr_network",
receptor_type = "RNA"
)
| Version | Author | Date |
|---|---|---|
| d85e512 | Luke Zappia | 2020-11-09 |
Chunk time: 0.28 secs
Summary
Parameters
This table describes parameters used and set in this document.
params <- list(
)
params <- toJSON(params, pretty = TRUE)
kable(fromJSON(params))Chunk time: 0.01 secs
Output files
This table describes the output files produced by this document. Right click and Save Link As… to download the results.
kable(data.frame(
File = c(
download_link("parameters.json", OUT_DIR)
),
Description = c(
"Parameters set and used in this analysis"
)
))| File | Description |
|---|---|
| parameters.json | Parameters set and used in this analysis |
Chunk time: 0.02 secs
Session information
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.0 (2020-04-24)
os macOS Catalina 10.15.7
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Berlin
date 2020-11-24
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