library(tidytof)
library(dplyr)
library(stringr)
In addition to its functions for analyzing and visualizing CyTOF data at the single-cell and cluster levels, {tidytof}’s tof_extract_features() verb allows users to aggregate single-cell and cluster-level information in order to summarize whole samples (or whole patients) from which cells were collected. These features can be useful for visualizing the differences between patients and samples in different experimental conditions or for building machine learning models.
To understand how the tof_extract_features() verb works, it’s easiest to look at each of its subroutines (the members of the tof_extract_* function family) independently.
To demonstrate how to use these verbs, we’ll first download a dataset originally collected for the development of the CITRUS algorithm. These data are available in the {HDCytoData} package, which is available on Bioconductor and can be downloaded with the following command:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("HDCytoData")
To load the CITRUS data into our current R session, we can call a function from the {HDCytoData}, which will provide it to us in a format from the {flowCore} package (called a “flowSet”). To convert this into a tidy tibble, we can use {tidytof} built-in method for converting flowCore objects into tof_tbl’s .
citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet()
citrus_data <-
citrus_raw |>
as_tof_tbl(sep = "_")
Thus, we can see that citrus_data is a tof_tbl with 172791 cells (one in each row) and 39 pieces of information about each cell (one in each column).
We can also extract some metadata from the raw data and join it with our single-cell data using some functions from the tidyverse:
citrus_metadata <-
tibble(
file_name = as.character(flowCore::pData(citrus_raw)[[1]]),
sample_id = 1:length(file_name),
patient = stringr::str_extract(file_name, "patient[:digit:]"),
stimulation = stringr::str_extract(file_name, "(BCR-XL)|Reference")
) |>
mutate(
stimulation = if_else(stimulation == "Reference", "Basal", stimulation)
)
citrus_metadata |>
head()
#> # A tibble: 6 × 4
#> file_name sample_id patient stimulation
#> <chr> <int> <chr> <chr>
#> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL
#> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal
#> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL
#> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal
#> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL
#> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal
Thus, we now have sample-level information about which patient each sample was collected from and which stimulation condition (“Basal” or “BCR-XL”) each sample was exposed to before data acquisition.
Finally, we can join this metadata with our single-cell tof_tbl to obtain the cleaned dataset.
citrus_data <-
citrus_data |>
left_join(citrus_metadata, by = "sample_id")
After these data cleaning steps, we now have citrus_data, a tof_tbl containing cells collected from 8 patients. Specifically, 2 samples were taken from each patient: one in which the cells’ B-cell receptors were stimulated (BCR-XL) and one in which they were not (Basal). In citrus_data, each cell’s patient of origin is stored in the patient column, and each cell’s stimulation condition is stored in the stimulation column. In addition, the population_id column stores information about cluster labels that were applied to each cell using a combination of FlowSOM clustering and manual merging (for details, run ?HDCytoData::Bodenmiller_BCR_XL in the R console).
tof_extract_proportion()First, we have tof_extract_proportion(), which extracts the proportion of cells in each cluster within each sample (with samples defined using the group_cols argument):
# preprocess the numeric columns in the citrus dataset
citrus_data <-
citrus_data |>
mutate(cluster = str_c("cluster", population_id)) |>
tof_preprocess()
citrus_data |>
tof_extract_proportion(
cluster_col = cluster,
group_cols = c(patient, stimulation)
) |>
head()
#> # A tibble: 6 × 10
#> patient stimulation `prop@cluster1` `prop@cluster2` `prop@cluster3`
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 patient1 BCR-XL 0.0109 0.0395 0.268
#> 2 patient1 Basal 0.0190 0.0482 0.447
#> 3 patient2 BCR-XL 0.0101 0.0143 0.358
#> 4 patient2 Basal 0.0130 0.0280 0.491
#> 5 patient3 BCR-XL 0.0200 0.0412 0.323
#> 6 patient3 Basal 0.0326 0.0830 0.397
#> # ℹ 5 more variables: `prop@cluster4` <dbl>, `prop@cluster5` <dbl>,
#> # `prop@cluster6` <dbl>, `prop@cluster7` <dbl>, `prop@cluster8` <dbl>
Like all members of the tof_extract_* function family, tof_extract_proportion() returns one row for each sample (defined as a unique combination of values of the columns specified in group_cols) and one column for each extracted feature (above, one column for the proportion of each of the 8 clusters in citrus_data). These values can also be returned in “long” format by changing the format argument:
citrus_data |>
tof_extract_proportion(
cluster_col = cluster,
group_cols = c(patient, stimulation),
format = "long"
) |>
head()
#> # A tibble: 6 × 4
#> patient stimulation cluster prop
#> <chr> <chr> <chr> <dbl>
#> 1 patient1 BCR-XL cluster1 0.0109
#> 2 patient1 BCR-XL cluster2 0.0395
#> 3 patient1 BCR-XL cluster3 0.268
#> 4 patient1 BCR-XL cluster4 0.461
#> 5 patient1 BCR-XL cluster5 0.00176
#> 6 patient1 BCR-XL cluster6 0.0447
tof_extract_central_tendency()Another member of the tof_extract_*() function family, tof_extract_central_tendency(), computes the central tendency (e.g. mean or median) of user-specified markers in each cluster.
citrus_data |>
tof_extract_central_tendency(
cluster_col = cluster,
group_cols = c(patient, stimulation),
marker_cols = any_of(c("CD45_In115", "CD4_Nd145", "CD20_Sm147")),
central_tendency_function = mean
) |>
head()
#> # A tibble: 6 × 26
#> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct`
#> <chr> <chr> <dbl> <dbl>
#> 1 patient1 BCR-XL 4.80 0.0967
#> 2 patient1 Basal 4.68 0.765
#> 3 patient2 BCR-XL 5.00 -0.0579
#> 4 patient2 Basal 4.88 0.808
#> 5 patient3 BCR-XL 5.04 -0.0432
#> 6 patient3 Basal 4.98 0.745
#> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` <dbl>,
#> # `CD45_In115@cluster2_ct` <dbl>, `CD4_Nd145@cluster2_ct` <dbl>,
#> # `CD20_Sm147@cluster2_ct` <dbl>, `CD45_In115@cluster3_ct` <dbl>,
#> # `CD4_Nd145@cluster3_ct` <dbl>, `CD20_Sm147@cluster3_ct` <dbl>,
#> # `CD45_In115@cluster4_ct` <dbl>, `CD4_Nd145@cluster4_ct` <dbl>,
#> # `CD20_Sm147@cluster4_ct` <dbl>, `CD45_In115@cluster5_ct` <dbl>,
#> # `CD4_Nd145@cluster5_ct` <dbl>, `CD20_Sm147@cluster5_ct` <dbl>, …
The argument central_tendency_function can be used to compute any summary statistic. For example, the following choice for central_tendency_function will compute the 75th percentile for each marker-cluster pair in citrus_data:
citrus_data |>
tof_extract_central_tendency(
cluster_col = cluster,
group_cols = c(patient, stimulation),
marker_cols = any_of(c("CD45_In115", "CD4_Nd145", "CD20_Sm147")),
central_tendency_function = function(x) quantile(x = x, probs = 0.75)
) |>
head()
#> # A tibble: 6 × 26
#> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct`
#> <chr> <chr> <dbl> <dbl>
#> 1 patient1 BCR-XL 5.30 -0.0186
#> 2 patient1 Basal 5.18 1.32
#> 3 patient2 BCR-XL 5.41 -0.0201
#> 4 patient2 Basal 5.28 1.39
#> 5 patient3 BCR-XL 5.42 -0.0362
#> 6 patient3 Basal 5.41 1.27
#> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` <dbl>,
#> # `CD45_In115@cluster2_ct` <dbl>, `CD4_Nd145@cluster2_ct` <dbl>,
#> # `CD20_Sm147@cluster2_ct` <dbl>, `CD45_In115@cluster3_ct` <dbl>,
#> # `CD4_Nd145@cluster3_ct` <dbl>, `CD20_Sm147@cluster3_ct` <dbl>,
#> # `CD45_In115@cluster4_ct` <dbl>, `CD4_Nd145@cluster4_ct` <dbl>,
#> # `CD20_Sm147@cluster4_ct` <dbl>, `CD45_In115@cluster5_ct` <dbl>,
#> # `CD4_Nd145@cluster5_ct` <dbl>, `CD20_Sm147@cluster5_ct` <dbl>, …
tof_extract_proportion()tof_extract_threshold() is similar to tof_extract_central_tendency(), but calculates the proportion of cells above a user-specified expression value for each marker instead of a measure of central tendency:
citrus_data |>
tof_extract_threshold(
cluster_col = cluster,
group_cols = c(patient, stimulation),
marker_cols = any_of(c("CD45_In115", "CD4_Nd145", "CD20_Sm147")),
threshold = 5
) |>
head()
#> # A tibble: 6 × 26
#> patient stimulation `CD45_In115@cluster1_threshold` CD4_Nd145@cluster1_thre…¹
#> <chr> <chr> <dbl> <dbl>
#> 1 patient1 BCR-XL 0.516 0
#> 2 patient1 Basal 0.365 0
#> 3 patient2 BCR-XL 0.554 0
#> 4 patient2 Basal 0.452 0
#> 5 patient3 BCR-XL 0.547 0
#> 6 patient3 Basal 0.549 0
#> # ℹ abbreviated name: ¹`CD4_Nd145@cluster1_threshold`
#> # ℹ 22 more variables: `CD20_Sm147@cluster1_threshold` <dbl>,
#> # `CD45_In115@cluster2_threshold` <dbl>,
#> # `CD4_Nd145@cluster2_threshold` <dbl>,
#> # `CD20_Sm147@cluster2_threshold` <dbl>,
#> # `CD45_In115@cluster3_threshold` <dbl>,
#> # `CD4_Nd145@cluster3_threshold` <dbl>, …
tof_extract_emd() and tof_extract_jsd()The two final members of the tof_extract_* function family – tof_extract_emd and tof_extract_jsd – are designed specifically for comparing distributions of marker expression between stimulation conditions. As such, they must be given a stimulation column (using the emd_col or jsd_col argument) that identifies the stimulation condition each cell is in, and a reference_level that specifies the reference (i.e. unstimulated) condition within the emd_col or jsd_col.
With these additional arguments, tof_extract_emd computes the Earth-mover’s distance between each marker’s distribution in the stimulation conditions (within each cluster) and the basal condition; similarly, tof_extract_jsd computes the Jensen-Shannon divergence index between the same distributions. Both of these values are ways to compare how different 2 distributions are to one another and are more computationally expensive (but also higher-resolution) than simply comparing measures of central tendency.
# Earth-mover's distance
citrus_data |>
tof_extract_emd(
cluster_col = cluster,
group_cols = patient,
marker_cols = any_of(c("CD45_In115", "CD4_Nd145", "CD20_Sm147")),
emd_col = stimulation,
reference_level = "Basal"
) |>
head()
#> # A tibble: 6 × 25
#> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³
#> <chr> <dbl> <dbl> <dbl>
#> 1 patient1 0.864 2.47 13.0
#> 2 patient2 1.11 7.05 10.8
#> 3 patient3 0.670 6.23 10.5
#> 4 patient4 2.64 5.86 9.90
#> 5 patient5 0.594 7.56 8.13
#> 6 patient6 0.661 4.77 7.97
#> # ℹ abbreviated names: ¹`BCR-XL_CD45_In115@cluster3_emd`,
#> # ²`BCR-XL_CD4_Nd145@cluster3_emd`, ³`BCR-XL_CD20_Sm147@cluster3_emd`
#> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_emd` <dbl>,
#> # `BCR-XL_CD4_Nd145@cluster7_emd` <dbl>,
#> # `BCR-XL_CD20_Sm147@cluster7_emd` <dbl>,
#> # `BCR-XL_CD45_In115@cluster4_emd` <dbl>,
#> # `BCR-XL_CD4_Nd145@cluster4_emd` <dbl>, …
# Jensen-Shannon Divergence
citrus_data |>
tof_extract_jsd(
cluster_col = cluster,
group_cols = patient,
marker_cols = any_of(c("CD45_In115", "CD4_Nd145", "CD20_Sm147")),
jsd_col = stimulation,
reference_level = "Basal"
) |>
head()
#> # A tibble: 6 × 25
#> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³
#> <chr> <dbl> <dbl> <dbl>
#> 1 patient1 0.0367 0.0513 0.347
#> 2 patient2 0.00831 0.168 0.401
#> 3 patient3 0.0104 0.115 0.357
#> 4 patient4 0.0301 0.135 0.205
#> 5 patient5 0.00911 0.0789 0.274
#> 6 patient6 0.00972 0.0346 0.214
#> # ℹ abbreviated names: ¹`BCR-XL_CD45_In115@cluster3_jsd`,
#> # ²`BCR-XL_CD4_Nd145@cluster3_jsd`, ³`BCR-XL_CD20_Sm147@cluster3_jsd`
#> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_jsd` <dbl>,
#> # `BCR-XL_CD4_Nd145@cluster7_jsd` <dbl>,
#> # `BCR-XL_CD20_Sm147@cluster7_jsd` <dbl>,
#> # `BCR-XL_CD45_In115@cluster4_jsd` <dbl>,
#> # `BCR-XL_CD4_Nd145@cluster4_jsd` <dbl>, …
tof_extract_features()Finally, the tof_extract_features() verb provides a wrapper to each of the members of its function family, allowing users to extract multiple features types at once. For example, the following code extracts the proportion of each cluster, median of several markers in each cluster, and EMD between the basal condition and stimulated condition in each cluster for all patients in citrus_data.
signaling_markers <-
c(
"pNFkB_Nd142", "pStat5_Nd150", "pAkt_Sm152", "pStat1_Eu153", "pStat3_Gd158",
"pSlp76_Dy164", "pBtk_Er166", "pErk_Er168", "pS6_Yb172", "pZap70_Gd156"
)
citrus_data |>
tof_extract_features(
cluster_col = cluster,
group_cols = patient,
stimulation_col = stimulation,
lineage_cols = any_of(c("CD45_In115", "CD20_Sm147", "CD33_Nd148")),
signaling_cols = any_of(signaling_markers),
signaling_method = "emd",
basal_level = "Basal"
) |>
head()
#> # A tibble: 6 × 193
#> patient `prop@cluster1` `prop@cluster2` `prop@cluster3` `prop@cluster4`
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 patient1 0.0149 0.0438 0.356 0.351
#> 2 patient2 0.0115 0.0212 0.425 0.323
#> 3 patient3 0.0255 0.0594 0.355 0.217
#> 4 patient4 0.0127 0.0418 0.320 0.223
#> 5 patient5 0.0207 0.0423 0.377 0.269
#> 6 patient6 0.0183 0.0493 0.459 0.250
#> # ℹ 188 more variables: `prop@cluster5` <dbl>, `prop@cluster6` <dbl>,
#> # `prop@cluster7` <dbl>, `prop@cluster8` <dbl>,
#> # `CD45_In115@cluster1_ct` <dbl>, `CD20_Sm147@cluster1_ct` <dbl>,
#> # `CD33_Nd148@cluster1_ct` <dbl>, `CD45_In115@cluster2_ct` <dbl>,
#> # `CD20_Sm147@cluster2_ct` <dbl>, `CD33_Nd148@cluster2_ct` <dbl>,
#> # `CD45_In115@cluster3_ct` <dbl>, `CD20_Sm147@cluster3_ct` <dbl>,
#> # `CD33_Nd148@cluster3_ct` <dbl>, `CD45_In115@cluster4_ct` <dbl>, …
sessionInfo()
#> R version 4.4.1 (2024-06-14)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
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#> other attached packages:
#> [1] tidyr_1.3.1 stringr_1.5.1
#> [3] HDCytoData_1.25.0 flowCore_2.18.0
#> [5] SummarizedExperiment_1.36.0 Biobase_2.66.0
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