# Topology-based Clustering in Seurat

# Introduction

This tutorial shows how to seamlessly integrate PIUMA into the Seurat workflow for scRNA-seq, using datasets from SeuratData, finding clusters cells with PIUMA and, then, performing downstream analyses again with Seurat. The update has been inspired by this book (Colleen M. Farrelly 2023).

# Installation

PIUMA can be installed by:

    if (!require("BiocManager", quietly = TRUE)) {
        install.packages("BiocManager")
    }

    BiocManager::install("PIUMA")

# Scope of this Vignette

In this vignette, we demonstrate how to integrate PIUMA’s TDA–based geometry–informed clustering into a Seurat workflow for single‐cell RNA‐seq data. Due to compilation time constrains of this vignette, we did not execute the chunks of this vignette, thus the user will not see the generated plots.

## Seurat pbmc3k testing data

As a practical demo of PIUMA-Seurat integration, we’ll use the pbmc3k dataset. The idea is simple: run your usual Seurat preprocessing, then hand the data to PIUMA for TDA and process again with Seurat. Below we fetch the data and perform the standard scRNA-seq steps (QC, normalization, feature selection, scaling, and dimensional reduction) before moving on to Mapper.

    set.seed(42)
    library(Seurat)
    library(SingleCellExperiment)
    library(SummarizedExperiment)
    library(PIUMA)
    #devtools::install_github('satijalab/seurat-data')
    library(SeuratData)

    SeuratData::InstallData("pbmc3k")
    pbmc3k <- SeuratData::LoadData("pbmc3k")

    pbmc3k <- UpdateSeuratObject(pbmc3k)
    pbmc3k <- NormalizeData(pbmc3k)
    pbmc3k <- ScaleData(pbmc3k)
    pbmc3k <- FindVariableFeatures(pbmc3k)
    pbmc3k <- RunPCA(pbmc3k)
    pbmc3k <- RunUMAP(pbmc3k,dims = 1:20)
    DimPlot(pbmc3k,group.by = 'seurat_annotations')

The cells are colored according to cell types. Let’s now convert the S4 Seurat file into a SummarizedExperiment format, prior to converting it to TDAObj.

    set.seed(42)
    counts_mat <- pbmc3k@assays$RNA@layers$counts
    logcounts_mat <- pbmc3k@assays$RNA@layers$data

    colnames(counts_mat) <- rownames(pbmc3k@meta.data)
    colnames(logcounts_mat) <- rownames(pbmc3k@meta.data)

    rownames(counts_mat) <- rownames(pbmc3k@assays$RNA@features@.Data)
    rownames(logcounts_mat) <- rownames(pbmc3k@assays$RNA@features@.Data)

    counts_dense    <- as.matrix(counts_mat)
    logcounts_dense <- as.matrix(logcounts_mat)
    cell_meta     <- pbmc3k@meta.data

    gene_meta     <- DataFrame(
      gene_id = rownames(counts_mat),
      row.names = rownames(counts_mat)
    )

    # assemble the SummarizedExperiment
    se <- SummarizedExperiment(
      assays   = list(
        counts    = counts_dense,
        logcounts = logcounts_dense
      ),
      colData = cell_meta,
      rowData = gene_meta
    )

    #refine Seurat annotation
    colData(se)$seurat_annotations <- factor(
      ifelse(
        is.na(colData(se)$seurat_annotations),
        "Unknown",
        as.character(colData(se)$seurat_annotations)
      )
    )

    # Create a PIUMA TDAobj from the SummarizedExperiment
    tda_obj <- makeTDAobjFromSE(se,'seurat_annotations')

Now we can proceed to run the PIUMA TDA pipeline, using preprocessed dimensional reductions of Seurat.

    set.seed(42)
    library(umap)
    tda_obj <- dfToDistance(tda_obj, distMethod = "euclidean")
    umap <- pbmc3k@reductions$umap@cell.embeddings
    colnames(umap) <- c('comp1','comp2')

    tda_obj@comp <- as.data.frame(umap)

Perfect! Now we have everything set up for our mapperCore() TDA call.

## PIUMA TDA clustering

With our hyperparameters (nBins = 33, overlap = 0.15), we can now proceed with the standard PIUMA workflow.

    set.seed(42)
    tda_obj <- mapperCore(x = tda_obj,
                          nBins = 33,
                          overlap = 0.15)
    tda_obj <- jaccardMatrix(tda_obj)
    tda_obj <- setGraph(tda_obj)
    tda_obj <- predict_mapper_class(tda_obj)
    tda_obj <- autoClusterMapper(tda_obj,method = 'walktrap')

Now we can go back to the Seurat object:

    set.seed(42)
    clusters_piuma <- tda_obj@clustering$obs_cluster
    all_cells     <- rownames(pbmc3k@meta.data)
    piuma_clusters <- setNames(
      rep(NA_integer_, length(all_cells)),
      all_cells
    )
    piuma_clusters[ clusters_piuma$obs ] <- clusters_piuma$cluster
    pbmc3k@meta.data$PIUMA_clusters <- as.factor(piuma_clusters)

    DimPlot(pbmc3k,group.by = 'PIUMA_clusters',label = TRUE,label.box = TRUE,repel = TRUE)+
      DimPlot(pbmc3k,group.by = 'seurat_annotations',label = TRUE,label.box = TRUE,repel = TRUE)

Here, we are showing the identified clusters from TDA compared to the original annotations. Some clusters mostly overlap with Seurat clusters, such as the subdivision between Naive CD4 T cells and Memory CD4 T cells, while other clusters, such as cluster 11 in PIUMA, do not have a clear corresponding sub-cluster.

## Biological Validation: GZMK+ CD8+ T Subset

Let’s investigate what PIUMA has uncovered in this dataset:

    set.seed(42)
    library(dplyr)
    library(ggplot2)

    Idents(pbmc3k) <- 'PIUMA_clusters'
    markers_12 <- FindMarkers(pbmc3k,
                             ident.1    = 12,
                             group.by   = "PIUMA_clusters",
                             test.use   = "wilcox",
                             logfc.threshold = 0.25,
                             only.pos=TRUE)
    markers_12_top <- markers_12 %>% filter(pct.1 - pct.2 > 0.7)

    FeaturePlot(pbmc3k,features=rownames(markers_12_top),alpha = 1)+ggplot2::ggtitle('Expression of Granzime K')
    pbmc3k$GZMK_pos <- FetchData(pbmc3k, vars = "GZMK")[,1] > 2
    pbmc3k$GZMK_pos <- factor(pbmc3k$GZMK_pos,
                              levels = c(FALSE, TRUE),
                              labels = c("GZMK–", "GZMK+"))
    pbmc3k$Cluster_12 <- pbmc3k$PIUMA_clusters==12
    pbmc3k_sub <- subset(pbmc3k, subset = !is.na(seurat_annotations))

    VlnPlot(pbmc3k_sub,
            features = "GZMK",
            group.by = "seurat_annotations",
            split.by = "Cluster_12",
            pt.size  = 0,
            assay    = "RNA"
    ) +
      ggplot2::scale_fill_manual(
        name   = "PIUMA Cluster 12",
        values = c("FALSE" = "grey80", "TRUE" = "steelblue"),
        labels = c("Other cells", "Cluster 12")
      ) +
      ggplot2::guides(fill = ggplot2::guide_legend(override.aes = list(alpha = 1))) +
      ggplot2::theme(
        axis.text.x     = ggplot2::element_text(angle = 45, hjust = 1),
        legend.position = "right"
      ) +
      ggplot2::labs(
        title = "TDA Cluster 12 identifies a GZMK+ CD8+ T subset",
        x     = "Seurat annotation",
        y     = "GZMK expression"
      )

PIUMA cluster 12 selectively isolates a GZMK+ T-cell sub-population. While standard annotation already captures GZMK+ CD4+ T cells, PIUMA uniquely uncovers a discrete GZMK+ CD8+ T-cell subset that the original clustering missed.

## Quantitative Comparison

Having demonstrated PIUMA’s ability to pinpoint biologically meaningful cellular subtypes, we now turn to a quantitative comparison with the standard annotation:

    set.seed(42)
    library(mclust)
    library(aricode)
    library(viridis)
    valid   <- which(!is.na(pbmc3k$seurat_annotations) & !is.na(pbmc3k$PIUMA_clusters))
    ari     <- mclust::adjustedRandIndex(pbmc3k$seurat_annotations[valid],
                                 pbmc3k$PIUMA_clusters[valid])
    nmi     <- aricode::NMI(pbmc3k$seurat_annotations[valid],
                   pbmc3k$PIUMA_clusters[valid])
    df <- data.frame(
      Metric = c("ARI","NMI"),
      Value  = c(ari, nmi)
    )

    ggplot2::ggplot(df, ggplot2::aes(x = Metric, y = 1, fill = Value)) +
      ggplot2::geom_tile(color = "white", width = 0.9, height = 0.9) +
      ggplot2::geom_text(aes(label = sprintf("%.3f", Value)),
                color = "white", size = 6) +
      viridis::scale_fill_viridis(option = "magma", limits = c(0, 1)) +
      coord_fixed(ratio = 1) +      # force squares
      ggplot2::theme_minimal() +
      ggplot2::theme(
        axis.title    = element_blank(),
        axis.text     = element_blank(),
        axis.ticks    = element_blank(),
        panel.grid    = element_blank(),
        plot.title    = element_text(hjust = 0.5)
      ) +
      ggplot2::labs(
        fill  = "Score",
        title = "Clustering Concordance (ARI & NMI)"
      )

We observe an ARI of 0.571 and an NMI of 0.68 between the original Seurat labels and the PIUMA TDA clusters. If PIUMA was merely recapitulating the standard annotation, both metrics would approach 1.0. Their lower values here instead reflect PIUMA’s reorganization of cells, precisely the flexibility needed to reveal previously unrecognized cellular subpopulations.

## Conclusion

This vignette demonstrates that PIUMA achieves strong concordance with Seurat’s standard annotations (ARI = 0.571, NMI = 0.68) while offering the flexibility to discover novel cell‐state subpopulations, such as a GZMK+ CD8+ T‐cell cluster unseen by conventional methods.

# Session Info

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] dplyr_1.1.4      ggrepel_0.9.6    ggplot2_4.0.1    PIUMA_1.7.2
    ## [5] BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
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    ##  [3] farver_2.1.2                S7_0.2.1
    ##  [5] fastmap_1.2.0               digest_0.6.39
    ##  [7] rpart_4.1.24                lifecycle_1.0.5
    ##  [9] cluster_2.1.8.1             magrittr_2.0.4
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    ## [13] compiler_4.5.2              rlang_1.1.7
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    ## [29] abind_1.4-8                 withr_3.0.2
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    ## [33] sys_3.4.3                   nnet_7.3-20
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    ## [37] colorspace_2.1-2            scales_1.4.0
    ## [39] MASS_7.3-65                 SummarizedExperiment_1.41.0
    ## [41] cli_3.6.5                   rmarkdown_2.30
    ## [43] vegan_2.7-2                 generics_0.1.4
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    ## [93] buildtools_1.0.0            pkgconfig_2.0.3

# References

Colleen M. Farrelly, Yaé Ulrich Gaba. 2023. The Shape of Data: Geometry-Based Machine Learning and Data Analysis in r. No Starch Press.