In tidytranscriptomics-workshops/iscb2022_tidytranscriptomics: Tidy Transcriptomics for Single-cell RNA Sequencing Analyses
knitr::opts_chunk$set(echo = TRUE)
Instructors
Dr. Stefano Mangiola is currently a Postdoctoral researcher in the laboratory of Prof. Tony Papenfuss at the Walter and Eliza Hall Institute in Melbourne, Australia. His background spans from biotechnology to bioinformatics and biostatistics. His research focuses on prostate and breast tumour microenvironment, the development of statistical models for the analysis of RNA sequencing data, and data analysis and visualisation interfaces.
Dr. Maria Doyle is the Application and Training Specialist for Research Computing at the Peter MacCallum Cancer Centre in Melbourne, Australia. She has a PhD in Molecular Biology and currently works in bioinformatics and data science education and training. She is passionate about supporting researchers, reproducible research, open source and tidy data.
Workshop goals and objectives
What you will learn
- Basic
tidy operations possible with tidyseurat and tidySingleCellExperiment
- The differences between
Seurat and SingleCellExperiment representation, and tidy representation
- How to interface
Seurat and SingleCellExperiment with tidy manipulation and visualisation
- A real-world case study that will showcase the power of
tidy single-cell methods compared with base/ad-hoc methods
What you will not learn
- The molecular technology of single-cell sequencing
- The fundamentals of single-cell data analysis
- The fundamentals of tidy data analysis
Slides
Getting started
Cloud
Easiest way to run this material. Only available during workshop. Many thanks to the Australian Research Data Commons (ARDC) for providing RStudio in the Australian Nectar Research Cloud and Andy Botting from ARDC for helping to set up.
- Login to one of the servers (e.g. https://rstudio5.tidytranscriptomics-workshop.cloud.edu.au/rstudio/) with one of the usernames and passwords from the spreadsheet provided at the workshop. Add your name into the spreadsheet beside the login you're using.
- Open
tidytranscriptomics_case_study.Rmd in iscb2022_tidytranscriptomcs/vignettes folder
Local
We will use the Cloud during the ISCB workshop and this method is available if you want to run the material after the workshop. If you want to install on your own computer, see instructions here.
Alternatively, you can view the material at the workshop webpage here.
library(Seurat)
library(ggplot2)
library(plotly)
library(dplyr)
library(colorspace)
library(SeuratWrappers)
library(dittoSeq)
Introduction to tidyseurat
Seurat is a very popular analysis toolkit for single cell RNA sequencing data [@butler2018integrating; @stuart2019comprehensive].
Here we load single-cell data in Seurat object format. This data is peripheral blood mononuclear cells (PBMCs) from metastatic breast cancer patients.
# load single cell RNA sequencing data
seurat_obj <- iscb2022tidytranscriptomics::seurat_obj
# take a look
seurat_obj
tidyseurat provides a bridge between the Seurat single-cell package and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Seurat object as a tidyverse tibble, and provides Seurat-compatible dplyr, tidyr, ggplot and plotly functions.
If we load the tidyseurat package and then view the single cell data, it now displays as a tibble.
library(tidyseurat)
seurat_obj
It can be interacted with using Seurat commands such as Assays.
Assays(seurat_obj)
We can also interact with our object as we do with any tidyverse tibble.
Tidyverse commands
We can use tidyverse commands, such as filter, select and mutate to explore the tidyseurat object. Some examples are shown below and more can be seen at the tidyseurat website here.
We can use filter to choose rows, for example, to see just the rows for the cells in G1 cell-cycle stage. Check if have groups or ident present.
seurat_obj |> filter(Phase == "G1")
We can use select to choose columns, for example, to see the sample, cell, total cellular RNA
seurat_obj |> select(.cell, nCount_RNA, Phase)
We also see the UMAP columns as they are not part of the cell metadata, they are read-only.
If we want to save the edited metadata, the Seurat object is modified accordingly.
# Save edited metadata
seurat_modified <- seurat_obj |> select(.cell, nCount_RNA, Phase)
# View Seurat metadata
seurat_modified[[]] |> head()
We can use mutate to create a column. For example, we could create a new Phase_l column that contains a lower-case version of Phase.
seurat_obj |>
mutate(Phase_l=tolower(Phase)) |>
# Select columns to view
select(Phase, Phase_l)
We can use tidyverse commands to polish an annotation column. We will extract the sample, and group information from the file name column into separate columns.
# First take a look at the file column
seurat_obj |> select(file)
# Create columns for sample and group
seurat_obj <- seurat_obj |>
# Extract sample and group
extract(file, "sample", "../data/.*/([a-zA-Z0-9_-]+)/outs.+", remove = FALSE)
# Take a look
seurat_obj |> select(sample)
We could use tidyverse unite to combine columns, for example to create a new column for sample id that combines the sample and patient identifier (BCB) columns.
seurat_obj <- seurat_obj |> unite("sample_id", sample, BCB, remove = FALSE)
# Take a look
seurat_obj |> select(sample_id, sample, BCB)
Case study
We will now demonstrate a real-world example of the power of using tidy transcriptomics packages in single cell analysis. For more information on single-cell analysis steps performed in a tidy way please see the ISMB2021 workshop.
Data pre-processing
The object seurat_obj we've been using was created as part of a study on breast cancer systemic immune response. Peripheral blood mononuclear cells have been sequenced for RNA at the single-cell level. The steps used to generate the object are summarised below.
-
scran, scater, and DropletsUtils packages have been used to eliminate empty droplets and dead cells. Samples were individually quality checked and cells were filtered for good gene coverage.
-
Variable features were identified using Seurat.
-
Read counts were scaled and normalised using SCTtransform from Seurat.
-
Data integration was performed using Seurat with default parameters.
-
PCA performed to reduce feature dimensionality.
-
Nearest-neighbor cell networks were calculated using 30 principal components.
-
2 UMAP dimensions were calculated using 30 principal components.
-
Cells with similar transcriptome profiles were grouped into clusters using Louvain clustering from Seurat.
Analyse custom signature
The researcher analysing this dataset wanted to to identify gamma delta T cells using a gene signature from a published paper [@Pizzolato2019].
With tidyseurat's join_features the counts for the genes could be viewed as columns.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
)
They were able to use tidyseurat's join_features to select the counts for the genes in the signature, followed by tidyverse mutate to easily create a column containing the signature score.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1 + TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
select(signature_score, everything())
The gamma delta T cells could then be visualised by the signature score using Seurat's visualisation functions.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
Seurat::FeaturePlot(features = "signature_score", min.cutoff = 0)
The cells could also be visualised using the popular and powerful ggplot package, enabling the researcher to use ggplot functions they were familiar with, and to customise the plot with great flexibility.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
mutate(signature_score = case_when(signature_score>0 ~ signature_score)) |>
ggplot(aes(UMAP_1, UMAP_2, color = signature_score)) +
geom_point(shape="." ) +
scale_color_continuous_sequential(palette = "Blues", na.value = "grey") +
theme_bw()
The gamma delta T cells (the blue cluster on the left with high signature score) could be interactively selected from the plot using the tidygate package.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
mutate(gate = tidygate::gate_int(
UMAP_1, UMAP_2,
.size = 0.1,
.color =signature_score
))
Here we draw one gate but multiple gates can be drawn.
After the selection we can reload from file the gate drawn for reproducibility.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
mutate(gate = tidygate::gate_int(
UMAP_1, UMAP_2,
.size = 0.1,
.color =signature_score,
gate_list = iscb2022tidytranscriptomics::gate_seurat_obj
))
And the dataset could be filtered for just these cells using tidyverse filter.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
mutate(gate = tidygate::gate_int(UMAP_1, UMAP_2, gate_list = iscb2022tidytranscriptomics::gate_seurat_obj)) |>
filter(gate == 1)
It was then possible to perform analyses on these gamma delta T cells by simply chaining further commands, such as below.
seurat_obj |>
join_features(
features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"),
shape = "wide",
assay = "SCT"
) |>
mutate(signature_score =
scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) -
scales::rescale(CD8A + CD8B, to=c(0,1))
) |>
mutate(gate = tidygate::gate_int(UMAP_1, UMAP_2, gate_list = iscb2022tidytranscriptomics::gate_seurat_obj) ) |>
filter(gate == 1) |>
# Reanalyse
NormalizeData(assay="RNA") |>
FindVariableFeatures(nfeatures = 100, assay="RNA") |>
SplitObject(split.by = "file") |>
RunFastMNN(assay="RNA") |>
RunUMAP(reduction = "mnn", dims = 1:20) |>
FindNeighbors(dims = 1:20, reduction = "mnn") |>
FindClusters(resolution = 0.3)
For comparison, we show the alternative using base R and Seurat
counts_positive <-
GetAssayData(seurat_obj, assay="SCT")[c("CD3D", "TRDC", "TRGC1", "TRGC2"),] |>
colSums() |>
scales::rescale(to=c(0,1))
counts_negative <-
GetAssayData(seurat_obj, assay="SCT")[c("CD8A", "CD8B"),] |>
colSums() |>
scales::rescale(to=c(0,1))
seurat_obj$signature_score <- counts_positive - counts_negative
p <- FeaturePlot(seurat_obj, features = "signature_score")
# This is not reproducible (in contrast to tidygate)
seurat_obj$within_gate <- colnames(seurat_obj) %in% CellSelector(plot = p)
seurat_obj |>
subset(within_gate == TRUE) |>
# Reanalyse
NormalizeData(assay="RNA") |>
FindVariableFeatures(nfeatures = 100, assay="RNA") |>
SplitObject(split.by = "file") |>
RunFastMNN(assay="RNA") |>
RunUMAP(reduction = "mnn", dims = 1:20) |>
FindNeighbors(dims = 1:20, reduction = "mnn") |>
FindClusters(resolution = 0.3)
As a final note, it's also possible to do complex and powerful things in a simple way, due to the integration of the tidy transcriptomics packages with the tidy universe. As one example, we can visualise the cells as a 3D plot using plotly.
The example data we've been using only contains a few genes, for the sake of time and size in this demonstration, but below is how you could generate the 3 dimensions needed for 3D plot with a full dataset.
single_cell_object |>
RunUMAP(dims = 1:30, n.components = 3L, spread = 0.5,min.dist = 0.01, n.neighbors = 10L)
We'll demonstrate creating a 3D plot using some data that has 3 UMAP dimensions.
pbmc <- iscb2022tidytranscriptomics::seurat_obj_UMAP3
pbmc |>
plot_ly(
x = ~`UMAP_1`,
y = ~`UMAP_2`,
z = ~`UMAP_3`,
color = ~curated_cell_type,
colors = dittoSeq::dittoColors()
) |>
add_markers(size = I(1))
Exercises
-
What proportion of all cells are gamma-delta T cells?
-
There is a cluster of cells characterised by a low RNA output (nCount_RNA). Use tidygate to identify the cell composition (curated_cell_type) of that cluster.
Session Information
sessionInfo()
References
tidytranscriptomics-workshops/iscb2022_tidytranscriptomics documentation built on Jan. 13, 2023, 12:52 a.m.
R Package Documentation
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knitr::opts_chunk$set(echo = TRUE)
Instructors
Dr. Stefano Mangiola is currently a Postdoctoral researcher in the laboratory of Prof. Tony Papenfuss at the Walter and Eliza Hall Institute in Melbourne, Australia. His background spans from biotechnology to bioinformatics and biostatistics. His research focuses on prostate and breast tumour microenvironment, the development of statistical models for the analysis of RNA sequencing data, and data analysis and visualisation interfaces.
Dr. Maria Doyle is the Application and Training Specialist for Research Computing at the Peter MacCallum Cancer Centre in Melbourne, Australia. She has a PhD in Molecular Biology and currently works in bioinformatics and data science education and training. She is passionate about supporting researchers, reproducible research, open source and tidy data.
Workshop goals and objectives
What you will learn
- Basic
tidyoperations possible withtidyseuratandtidySingleCellExperiment - The differences between
SeuratandSingleCellExperimentrepresentation, andtidyrepresentation - How to interface
SeuratandSingleCellExperimentwith tidy manipulation and visualisation - A real-world case study that will showcase the power of
tidysingle-cell methods compared with base/ad-hoc methods
What you will not learn
- The molecular technology of single-cell sequencing
- The fundamentals of single-cell data analysis
- The fundamentals of tidy data analysis
Slides
Getting started
Cloud
Easiest way to run this material. Only available during workshop. Many thanks to the Australian Research Data Commons (ARDC) for providing RStudio in the Australian Nectar Research Cloud and Andy Botting from ARDC for helping to set up.
- Login to one of the servers (e.g. https://rstudio5.tidytranscriptomics-workshop.cloud.edu.au/rstudio/) with one of the usernames and passwords from the spreadsheet provided at the workshop. Add your name into the spreadsheet beside the login you're using.
- Open
tidytranscriptomics_case_study.Rmdiniscb2022_tidytranscriptomcs/vignettesfolder
Local
We will use the Cloud during the ISCB workshop and this method is available if you want to run the material after the workshop. If you want to install on your own computer, see instructions here.
Alternatively, you can view the material at the workshop webpage here.
library(Seurat) library(ggplot2) library(plotly) library(dplyr) library(colorspace) library(SeuratWrappers) library(dittoSeq)
Introduction to tidyseurat
Seurat is a very popular analysis toolkit for single cell RNA sequencing data [@butler2018integrating; @stuart2019comprehensive].
Here we load single-cell data in Seurat object format. This data is peripheral blood mononuclear cells (PBMCs) from metastatic breast cancer patients.
# load single cell RNA sequencing data seurat_obj <- iscb2022tidytranscriptomics::seurat_obj # take a look seurat_obj
tidyseurat provides a bridge between the Seurat single-cell package and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Seurat object as a tidyverse tibble, and provides Seurat-compatible dplyr, tidyr, ggplot and plotly functions.
If we load the tidyseurat package and then view the single cell data, it now displays as a tibble.
library(tidyseurat)
seurat_obj
It can be interacted with using Seurat commands such as Assays.
Assays(seurat_obj)
We can also interact with our object as we do with any tidyverse tibble.
Tidyverse commands
We can use tidyverse commands, such as filter, select and mutate to explore the tidyseurat object. Some examples are shown below and more can be seen at the tidyseurat website here.
We can use filter to choose rows, for example, to see just the rows for the cells in G1 cell-cycle stage. Check if have groups or ident present.
seurat_obj |> filter(Phase == "G1")
We can use select to choose columns, for example, to see the sample, cell, total cellular RNA
seurat_obj |> select(.cell, nCount_RNA, Phase)
We also see the UMAP columns as they are not part of the cell metadata, they are read-only. If we want to save the edited metadata, the Seurat object is modified accordingly.
# Save edited metadata seurat_modified <- seurat_obj |> select(.cell, nCount_RNA, Phase) # View Seurat metadata seurat_modified[[]] |> head()
We can use mutate to create a column. For example, we could create a new Phase_l column that contains a lower-case version of Phase.
seurat_obj |> mutate(Phase_l=tolower(Phase)) |> # Select columns to view select(Phase, Phase_l)
We can use tidyverse commands to polish an annotation column. We will extract the sample, and group information from the file name column into separate columns.
# First take a look at the file column seurat_obj |> select(file)
# Create columns for sample and group seurat_obj <- seurat_obj |> # Extract sample and group extract(file, "sample", "../data/.*/([a-zA-Z0-9_-]+)/outs.+", remove = FALSE) # Take a look seurat_obj |> select(sample)
We could use tidyverse unite to combine columns, for example to create a new column for sample id that combines the sample and patient identifier (BCB) columns.
seurat_obj <- seurat_obj |> unite("sample_id", sample, BCB, remove = FALSE) # Take a look seurat_obj |> select(sample_id, sample, BCB)
Case study
We will now demonstrate a real-world example of the power of using tidy transcriptomics packages in single cell analysis. For more information on single-cell analysis steps performed in a tidy way please see the ISMB2021 workshop.
Data pre-processing
The object seurat_obj we've been using was created as part of a study on breast cancer systemic immune response. Peripheral blood mononuclear cells have been sequenced for RNA at the single-cell level. The steps used to generate the object are summarised below.
-
scran,scater, andDropletsUtilspackages have been used to eliminate empty droplets and dead cells. Samples were individually quality checked and cells were filtered for good gene coverage. -
Variable features were identified using
Seurat. -
Read counts were scaled and normalised using SCTtransform from
Seurat. -
Data integration was performed using
Seuratwith default parameters. -
PCA performed to reduce feature dimensionality.
-
Nearest-neighbor cell networks were calculated using 30 principal components.
-
2 UMAP dimensions were calculated using 30 principal components.
-
Cells with similar transcriptome profiles were grouped into clusters using Louvain clustering from
Seurat.
Analyse custom signature
The researcher analysing this dataset wanted to to identify gamma delta T cells using a gene signature from a published paper [@Pizzolato2019].
With tidyseurat's join_features the counts for the genes could be viewed as columns.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" )
They were able to use tidyseurat's join_features to select the counts for the genes in the signature, followed by tidyverse mutate to easily create a column containing the signature score.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1 + TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> select(signature_score, everything())
The gamma delta T cells could then be visualised by the signature score using Seurat's visualisation functions.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> Seurat::FeaturePlot(features = "signature_score", min.cutoff = 0)
The cells could also be visualised using the popular and powerful ggplot package, enabling the researcher to use ggplot functions they were familiar with, and to customise the plot with great flexibility.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> mutate(signature_score = case_when(signature_score>0 ~ signature_score)) |> ggplot(aes(UMAP_1, UMAP_2, color = signature_score)) + geom_point(shape="." ) + scale_color_continuous_sequential(palette = "Blues", na.value = "grey") + theme_bw()
The gamma delta T cells (the blue cluster on the left with high signature score) could be interactively selected from the plot using the tidygate package.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> mutate(gate = tidygate::gate_int( UMAP_1, UMAP_2, .size = 0.1, .color =signature_score ))
Here we draw one gate but multiple gates can be drawn.
After the selection we can reload from file the gate drawn for reproducibility.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> mutate(gate = tidygate::gate_int( UMAP_1, UMAP_2, .size = 0.1, .color =signature_score, gate_list = iscb2022tidytranscriptomics::gate_seurat_obj ))
And the dataset could be filtered for just these cells using tidyverse filter.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> mutate(gate = tidygate::gate_int(UMAP_1, UMAP_2, gate_list = iscb2022tidytranscriptomics::gate_seurat_obj)) |> filter(gate == 1)
It was then possible to perform analyses on these gamma delta T cells by simply chaining further commands, such as below.
seurat_obj |> join_features( features = c("CD3D", "TRDC", "TRGC1", "TRGC2", "CD8A", "CD8B"), shape = "wide", assay = "SCT" ) |> mutate(signature_score = scales::rescale(CD3D + TRDC + TRGC1+ TRGC2, to=c(0,1)) - scales::rescale(CD8A + CD8B, to=c(0,1)) ) |> mutate(gate = tidygate::gate_int(UMAP_1, UMAP_2, gate_list = iscb2022tidytranscriptomics::gate_seurat_obj) ) |> filter(gate == 1) |> # Reanalyse NormalizeData(assay="RNA") |> FindVariableFeatures(nfeatures = 100, assay="RNA") |> SplitObject(split.by = "file") |> RunFastMNN(assay="RNA") |> RunUMAP(reduction = "mnn", dims = 1:20) |> FindNeighbors(dims = 1:20, reduction = "mnn") |> FindClusters(resolution = 0.3)
For comparison, we show the alternative using base R and Seurat
counts_positive <- GetAssayData(seurat_obj, assay="SCT")[c("CD3D", "TRDC", "TRGC1", "TRGC2"),] |> colSums() |> scales::rescale(to=c(0,1)) counts_negative <- GetAssayData(seurat_obj, assay="SCT")[c("CD8A", "CD8B"),] |> colSums() |> scales::rescale(to=c(0,1)) seurat_obj$signature_score <- counts_positive - counts_negative p <- FeaturePlot(seurat_obj, features = "signature_score") # This is not reproducible (in contrast to tidygate) seurat_obj$within_gate <- colnames(seurat_obj) %in% CellSelector(plot = p) seurat_obj |> subset(within_gate == TRUE) |> # Reanalyse NormalizeData(assay="RNA") |> FindVariableFeatures(nfeatures = 100, assay="RNA") |> SplitObject(split.by = "file") |> RunFastMNN(assay="RNA") |> RunUMAP(reduction = "mnn", dims = 1:20) |> FindNeighbors(dims = 1:20, reduction = "mnn") |> FindClusters(resolution = 0.3)
As a final note, it's also possible to do complex and powerful things in a simple way, due to the integration of the tidy transcriptomics packages with the tidy universe. As one example, we can visualise the cells as a 3D plot using plotly.
The example data we've been using only contains a few genes, for the sake of time and size in this demonstration, but below is how you could generate the 3 dimensions needed for 3D plot with a full dataset.
single_cell_object |> RunUMAP(dims = 1:30, n.components = 3L, spread = 0.5,min.dist = 0.01, n.neighbors = 10L)
We'll demonstrate creating a 3D plot using some data that has 3 UMAP dimensions.
pbmc <- iscb2022tidytranscriptomics::seurat_obj_UMAP3 pbmc |> plot_ly( x = ~`UMAP_1`, y = ~`UMAP_2`, z = ~`UMAP_3`, color = ~curated_cell_type, colors = dittoSeq::dittoColors() ) |> add_markers(size = I(1))
Exercises
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What proportion of all cells are gamma-delta T cells?
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There is a cluster of cells characterised by a low RNA output (nCount_RNA). Use tidygate to identify the cell composition (curated_cell_type) of that cluster.
Session Information
sessionInfo()
References
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