scTHI example
Last updated: 2020-11-24
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Knit directory: interaction-tools/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 38ee322 | Luke Zappia | 2020-11-10 | Add scTHI to drake |
| Rmd | 89d2503 | Luke Zappia | 2020-11-10 | Add scTHI tutorial |
| Rmd | 7ff056a | Luke Zappia | 2020-11-10 | Add scTHI document |
# 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 scTHI package and have a look at the output it produces. More information about scTHI can be found at https://github.com/miccec/scTHI.
library("scTHI")Chunk time: 0.15 secs
1 Input
1.1 Expression
The scTHI tutorial contains a pre-processed dataset from a glioma sample. The sample has been filtered, transformed to log TPM and quantile normalised.
data("H3K27", package = "scTHI.data")
pander(as.matrix(H3K27[1:5, 1:5]))| BCH836-P01-A03 | BCH836-P01-A04 | BCH836-P01-A06 | |
|---|---|---|---|
| A1BG | 0.7119 | 0 | 2.915 |
| A1BG-AS1 | 1.3 | 6.564 | 0 |
| A1CF | 0 | 0 | 0 |
| A2M | 0 | 0 | 0 |
| A2M-AS1 | 0 | 0 | 0 |
| BCH836-P01-A08 | BCH836-P01-A11 | |
|---|---|---|
| A1BG | 0 | 1.291 |
| A1BG-AS1 | 0 | 6.152 |
| A1CF | 0 | 0 |
| A2M | 0 | 0 |
| A2M-AS1 | 0 | 0 |
Chunk time: 2.81 secs
The matrix has 21673 rows (pairs) and 527 columns.
1.2 Metadata
We are also provided with a data frame of metadata information.
data("H3K27.meta", package = "scTHI.data")
skim(H3K27.meta)| Name | H3K27.meta |
| Number of rows | 527 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Sample | 0 | 1.00 | 6 | 6 | 0 | 1 | 0 |
| GenesExpressed | 0 | 1.00 | 4 | 5 | 0 | 503 | 0 |
| HousekeepingGeneExpression | 0 | 1.00 | 9 | 11 | 0 | 527 | 0 |
| Type | 0 | 1.00 | 6 | 15 | 0 | 4 | 0 |
| Cellcycle | 89 | 0.83 | 10 | 12 | 0 | 438 | 0 |
| OPC.variable | 89 | 0.83 | 8 | 12 | 0 | 438 | 0 |
| OC.like | 89 | 0.83 | 9 | 12 | 0 | 438 | 0 |
| AC.like | 89 | 0.83 | 9 | 12 | 0 | 438 | 0 |
| OPC.like | 89 | 0.83 | 9 | 12 | 0 | 438 | 0 |
Chunk time: 0.16 secs
The most important column here contains the cell type labels.
table(H3K27.meta$Type)
Filter Immune cell Malignant Oligodendrocyte
2 53 438 34 Chunk time: 0 secs
2 Selection
For the tutorial we only use the immune cells and a subset of the malignant cells.
malignant <- rownames(H3K27.meta)[H3K27.meta$Type == "Malignant"][1:100]
immune <- rownames(H3K27.meta)[H3K27.meta$Type == "Immune cell"]Chunk time: 0.01 secs
3 Testing
We now pass the input to the scoring function which uses an internal database of ligand-receptor pairs. The score considers the average expression of each pair but also the percentage of cells that express each ligand/receptor. Genes are only considered to be expressed if they are in the top 10 percent of genes in a cell. A p-value for each interaction is computed using a permutation test.
output <- scTHI_score(
expMat = H3K27,
cellCusterA = malignant,
cellCusterB = immune,
cellCusterAName = "Malignant",
cellCusterBName = "Immune",
topRank = 10,
PValue = TRUE,
pvalueCutoff = 0.05,
nPermu = 10,
ncore = 1
)[1] "Computed ranked values for partner A"
[1] "Computed ranked values for partner B"
[1] "RPS19_C5AR1" "THY1_ITGAX:ITGB2" "B2M_HLA-F" "PTN_PLXNB2"
[5] "B2M_LILRB1" "PSAP_LRP1" "SERPINE2_LRP1" "KLRC2_HLA-E"
[9] "APP_CD74" "VCAN_ITGB1" "CALR_LRP1" "HLA-A_APLP2"
[13] "THY1_ITGB2:ITGAM" "VCAN_TLR2" "HLA-A_LILRB1" "CHAD_ITGB1"
[17] "APP_LRP1" "APP_NCSTN" Chunk time: 40.87 secs
The output is a list with 4 items: result, expMat, Malignant and Immune
Most of these are the inputs we provided but the scores are available in the result item.
skim(output$result)| Name | output$result |
| Number of rows | 18 |
| Number of columns | 11 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| interationPair | 0 | 1 | 8 | 16 | 0 | 18 | 0 |
| interactionType | 0 | 1 | 6 | 7 | 0 | 2 | 0 |
| partnerA | 0 | 1 | 3 | 8 | 0 | 12 | 0 |
| partnerB | 0 | 1 | 4 | 11 | 0 | 13 | 0 |
| expValueA_Malignant | 0 | 1 | 3 | 4 | 0 | 11 | 0 |
| expValueB_Immune | 0 | 1 | 3 | 11 | 0 | 13 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| rnkPartnerA_Malignant | 0 | 1 | 0.77 | 0.18 | 0.53 | 0.57 | 0.80 | 0.97 | 0.99 | ▇▂▂▃▇ |
| rnkPartnerB_Immune | 0 | 1 | 0.70 | 0.15 | 0.57 | 0.60 | 0.62 | 0.77 | 1.00 | ▇▂▁▁▂ |
| SCORE | 0 | 1 | 0.74 | 0.10 | 0.55 | 0.70 | 0.75 | 0.79 | 0.94 | ▅▁▇▅▂ |
| pValue | 0 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
| FDR | 0 | 1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | ▁▁▇▁▁ |
Chunk time: 0.09 secs
4 Visualisation
4.1 Scores
We can visualise a bar plot of the scores for the significant pairs.
scTHI_plotResult(
scTHIresult = output,
cexNames = 0.7,
plotType = "score"
)
| Version | Author | Date |
|---|---|---|
| 38ee322 | Luke Zappia | 2020-11-10 |
Chunk time: 0.15 secs
4.2 Expression
This bar plot shows the percentage of cells in each cluster that express each partner in the interaction.
scTHI_plotResult(
scTHIresult = output,
cexNames = 0.7,
plotType = "pair"
)
| Version | Author | Date |
|---|---|---|
| 38ee322 | Luke Zappia | 2020-11-10 |
Chunk time: 0.1 secs
4.3 t-SNE
We can also look at where cells lie in a t-SNE embedding.
4.3.1 Cluster
Here the cells are coloured by the selected clusters.
output <- scTHI_runTsne(scTHIresult = output)
scTHI_plotCluster(
scTHIresult = output,
cexPoint = 0.8,
legendPos = "bottomleft"
)
| Version | Author | Date |
|---|---|---|
| 38ee322 | Luke Zappia | 2020-11-10 |
Chunk time: 12.06 secs
4.3.2 Interactions
We can also plot the expression of specific pairs we are interested in.
scTHI_plotPairs(
scTHIresult = output,
cexPoint = 0.8,
interactionToplot = "THY1_ITGAX:ITGB2"
)
| Version | Author | Date |
|---|---|---|
| 38ee322 | Luke Zappia | 2020-11-10 |
Chunk time: 0.2 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: 0.01 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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