Spatially Variable Genes

Spatially variable genes (SVGs) are genes whose expression levels vary significantly across different spatial regions within a tissue or across cells in a spatially structured context.

50 datasets · 14 methods · 2 control methods · 1 metrics

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MIT

Task info Method info Metric info Dataset info Results

Recent years have witnessed significant progress in spatially-resolved transcriptome profiling techniques that simultaneously characterize cellular gene expression and their physical position, generating spatial transcriptomic (ST) data. The application of these techniques has dramatically advanced our understanding of disease and developmental biology. One common task for all ST profiles, regardless of the employed protocols, is to identify genes that exhibit spatial patterns. These genes, defined as spatially variable genes (SVGs), contain additional information about the spatial structure of the tissues of interest, compared to highly variable genes (HVGs).

Identification of spatially variable genes is crucial to for studying spatial domains within tissue microenvironmnets, developmental gradients and cell signaling pathways. In this task we attempt to evaluate various methods for detecting SVGs using a number of realistic simulated datasets with diverse patterns derived from real-world spatial transcriptomics data using scDesign3. Synthetic data is generated by mixing a Gaussian Process (GP) model and a non-spatial model (obtained by shuffling mean parameters of the GP model to remove spatial correlation between spots) to generate gene expressions with various spatial variability. For more details, please refer to our manuscript and Github.

Summary

function aggregate_scores(obj) {
  return d3.mean(obj.map(val => {
    if (val.score === undefined || isNaN(val.score)) return 0;
    return Math.min(1, Math.max(0, val.score))
  }));
}

function transpose_list_of_objects(list) {
  return Object.fromEntries(Object.keys(list[0]).map(key => [key, list.map(d => d[key])]))
}

function label_time(time) {
  if (time < 1e-5) return "0s";
  if (time < 1) return "<1s";
  if (time < 60) return `${Math.floor(time)}s`;
  if (time < 3600) return `${Math.floor(time / 60)}m`;
  if (time < 3600 * 24) return `${Math.floor(time / 3600)}h`;
  if (time < 3600 * 24 * 7) return `${Math.floor(time / 3600 / 24)}d`;
  return ">7d"; // Assuming missing values are encoded as NaN
}

function label_memory(x_mb, include_mb = true) {
  if (!include_mb && x_mb < 1e3) return "<1G";
  if (x_mb < 1) return "<1M";
  if (x_mb < 1e3) return `${Math.round(x_mb)}M`;
  if (x_mb < 1e6) return `${Math.round(x_mb / 1e3)}G`;
  if (x_mb < 1e9) return `${Math.round(x_mb / 1e6)}T`;
  return ">1P";
}

function mean_na_rm(x) {
  return d3.mean(x.filter(d => !isNaN(d)));
}

poss_dataset_ids = dataset_info
  .map(d => d.dataset_id)
  .filter(d => results.map(r => r.dataset_id).includes(d))
poss_method_ids = method_info
  .map(d => d.method_id)
  .filter(d => results.map(r => r.method_id).includes(d))
poss_metric_ids = metric_info
  .map(d => d.metric_id)
  .filter(d => results.map(r => Object.keys(r.scaled_scores)).flat().includes(d))

has_resources = results[0].hasOwnProperty("resources")
has_exit_codes = results[0].hasOwnProperty("exit_codes")

results_long = results.flatMap(d => {
  return Object.entries(d.scaled_scores).map(([metric_id, value]) =>
    ({
      method_id: d.method_id,
      dataset_id: d.dataset_id,
      metric_id: metric_id,
      score: value
    })
  )
}).filter(d => method_ids.includes(d.method_id) && metric_ids.includes(d.metric_id) && dataset_ids.includes(d.dataset_id))

overall = d3.groups(results_long, d => d.method_id)
  .map(([method_id, values]) => ({method_id, mean_score: aggregate_scores(values)}))

per_dataset = d3.groups(results_long, d => d.method_id)
  .map(([method_id, values]) => {
    const datasets = d3.groups(values, d => d.dataset_id)
      .map(([dataset_id, values]) => ({["dataset_" + dataset_id]: aggregate_scores(values)}))
      .reduce((a, b) => ({...a, ...b}), {})
    return {method_id, ...datasets}
  })

per_metric = d3.groups(results_long, d => d.method_id)
  .map(([method_id, values]) => {
    const metrics = d3.groups(values, d => d.metric_id)
      .map(([metric_id, values]) => ({["metric_" + metric_id]: aggregate_scores(values)}))
      .reduce((a, b) => ({...a, ...b}), {})
    return {method_id, ...metrics}
  })

results_resources = {
  let results_resources = null

  if (has_resources) {
    results_resources = results.flatMap(d => {
      return ({
        method_id: d.method_id,
        dataset_id: d.dataset_id,
        ...d.resources
      })
    }).filter(d => method_ids.includes(d.method_id) && dataset_ids.includes(d.dataset_id))
  }

  return results_resources
}

resources = {
  let resources = null

  if (has_resources) {
    resources = d3.groups(results_resources, d => d.method_id)
      .map(([method_id, values]) => {
        const mean_peak_memory_mb = mean_na_rm(values.map(d => d.peak_memory_mb))
        const mean_disk_read_mb = mean_na_rm(values.map(d => d.disk_read_mb))
        const mean_disk_write_mb = mean_na_rm(values.map(d => d.disk_write_mb))
        const mean_duration_sec = mean_na_rm(values.map(d => d.duration_sec))

        return ({
          method_id,
          mean_cpu_pct: mean_na_rm(values.map(d => d.cpu_pct)),
          mean_peak_memory_mb,
          mean_peak_memory_log: -Math.log10(mean_peak_memory_mb),
          mean_peak_memory_str: " " + label_memory(mean_peak_memory_mb) + " ",
          mean_disk_read_mb: mean_na_rm(values.map(d => d.disk_read_mb)),
          mean_disk_read_log: -Math.log10(mean_disk_read_mb),
          mean_disk_read_str: " " + label_memory(mean_disk_read_mb) + " ",
          mean_disk_write_mb: mean_na_rm(values.map(d => d.disk_write_mb)),
          mean_disk_write_log: -Math.log10(mean_disk_write_mb),
          mean_disk_write_str: " " + label_memory(mean_disk_write_mb) + " ",
          mean_duration_sec,
          mean_duration_log: -Math.log10(mean_duration_sec),
          mean_duration_str: " " + label_time(mean_duration_sec) + " "
        })
      })
  }

  return resources
}

exit_codes = {
  let exit_codes = null

  if (has_exit_codes) {
    exit_codes = results.flatMap(d => {
      return ({
        method_id: d.method_id,
        dataset_id: d.dataset_id,
        exit_codes: Object.values(d.exit_codes)
      })
    }).filter(d => method_ids.includes(d.method_id) && dataset_ids.includes(d.dataset_id))
  } else {
    exit_codes = results_resources.flatMap(d => {
      let exit_code = d.exit_code
      if (exit_code === undefined) {
        // If there is not exit code, assume the method ran successfully
        exit_code = 0
      }

      return ({
        method_id: d.method_id,
        dataset_id: d.dataset_id,
        exit_codes: [exit_code]
      })
    }).filter(d => method_ids.includes(d.method_id) && dataset_ids.includes(d.dataset_id))
  }

  return exit_codes
}

error_reasons = d3.groups(exit_codes, d => d.method_id)
  .map(([method_id, values]) => {
    const all_codes = values.flatMap(d => d.exit_codes)

    if (all_codes.length === 0) {
      return {method_id, error_reason: []}
    }

    const error_pct_oom = d3.mean(all_codes, d => d === 137)
    const error_pct_timeout = d3.mean(all_codes, d => d === 143)
    const error_pct_na = d3.mean(all_codes, d => d === 99)
    const error_pct_error = d3.mean(all_codes, d => d > 0) - error_pct_oom - error_pct_timeout - error_pct_na
    const error_pct_unknown = d3.mean(all_codes, d => d < 0)
    const error_pct_ok = d3.mean(all_codes, d => d === 0)
    return ({
      method_id,
      error_reason: [
        error_pct_oom,
        error_pct_timeout,
        error_pct_error,
        error_pct_unknown,
        error_pct_na,
        error_pct_ok
      ],
    })
  })

summary_all = method_info
  .filter(d => show_con || !d.is_baseline)
  .filter(d => method_ids.includes(d.method_id))
  .map(method => {
    const method_id = method.method_id
    const method_name = method.method_name
    const mean_score = overall.find(d => d.method_id === method_id).mean_score
    const datasets = per_dataset.find(d => d.method_id === method_id)
    const metrics = per_metric.find(d => d.method_id === method_id)
    const error_reasons_ = error_reasons.find(d => d.method_id === method_id)

    let summary = {
      method_id,
      method_name,
      mean_score,
      ...datasets,
      ...metrics,
      ...error_reasons_
    }

    if (has_resources) {
      const resources_ = resources.find(d => d.method_id === method_id)
      summary = {...summary, ...resources_}
    }
    return summary
  })
  .sort((a, b) => b.mean_score - a.mean_score)

// make sure the first entry contains all columns
column_info = {
  let column_info = [
    {
      id: "method_name",
      name: "Name",
      label: null,
      group: "method",
      geom: "text",
      palette: null
    },
    {
      id: "mean_score",
      name: "Score",
      group: "overall",
      geom: "bar",
      palette: "overall"
    },
    {
      id: "error_reason",
      name: "Error reason",
      group: "overall",
      geom: "pie",
      palette: "error_reason"
    },
    ...dataset_info
      .filter(d => dataset_ids.includes(d.dataset_id))
      .map(
        d => ({
          id: "dataset_" + d.dataset_id,
          name: d.dataset_name,
          group: "dataset",
          geom: "funkyrect",
          palette: "dataset"
        })
      )
      .sort((a, b) => a.name.localeCompare(b.name)),
    ...metric_info
      .filter(d => metric_ids.includes(d.metric_id))
      .map(
        d => ({
          id: "metric_" + d.metric_id,
          name: d.metric_name,
          group: "metric",
          geom: "funkyrect",
          palette: "metric"
        })
      )
      .sort((a, b) => a.name.localeCompare(b.name)),
  ]

  if (has_resources) {
    column_info.push(
      {
        id: "mean_cpu_pct",
        name: "%CPU",
        group: "resources",
        geom: "funkyrect",
        palette: "resources"
      },
      {
        id: "mean_peak_memory_log",
        name: "Peak memory",
        label: "mean_peak_memory_str",
        group: "resources",
        geom: "rect",
        palette: "resources"
      },
      {
        id: "mean_disk_read_log",
        name: "Disk read",
        label: "mean_disk_read_str",
        group: "resources",
        geom: "rect",
        palette: "resources"
      },
      {
        id: "mean_disk_write_log",
        name: "Disk write",
        label: "mean_disk_write_str",
        group: "resources",
        geom: "rect",
        palette: "resources"
      },
      {
        id: "mean_duration_log",
        name: "Duration",
        label: "mean_duration_str",
        group: "resources",
        geom: "rect",
        palette: "resources"
      }
    )
  }

  column_info = column_info.map(d => {
    if (d.id === "method_name") {
      return {...d, options: {width: 15, hjust: 0}}
    } else if (d.id === "is_baseline") {
      return {...d, options: {width: 1}}
    } else if (d.geom === "bar") {
      return {...d, options: {width: 4}}
    } else {
      return d
    }
  })

  return column_info
}

column_groups = {
  let column_groups = [
    {
      group: "method",
      palette: null,
      level1: ""
    },
    {
      group: "overall",
      palette: "overall",
      level1: "Overall"
    },
    {
      group: "error_reason",
      palette: "error_reason",
      level1: "Error reason"
    },
    {
      group: "dataset",
      palette: "dataset",
      level1: dataset_info.length >= 3 ? "Datasets" : ""
    },
    {
      group: "metric",
      palette: "metric",
      level1: metric_info.length >= 3 ? "Metrics" : ""
    }
  ]

  if (has_resources) {
    column_groups.push(
      {group: "resources", palette: "resources", level1: "Resources"}
    )
  }

  return column_groups
}

palettes = [
  {
    overall: "Greys",
    dataset: "Blues",
    metric: "Reds",
    resources: "YlOrBr",
    error_reason: {
      colors: ["#8DD3C7", "#FFFFB3", "#BEBADA", "#fdb462", "#999999", "#FFFFFF"],
      names: [
        "Memory limit exceeded",
        "Time limit exceeded",
        "Execution error",
        "Unknown error",
        "Not applicable",
        "No error"
      ]
    }
  }
][0]

funkyheatmap(
    transpose_list_of_objects(summary_all),
    transpose_list_of_objects(column_info),
    [],
    transpose_list_of_objects(column_groups),
    [],
    palettes,
    {
        fontSize: 14,
        rowHeight: 26,
        rootStyle: 'max-width: none',
        colorByRank: color_by_rank,
        theme: {
            oddRowBackground: 'var(--bs-body-bg)',
            evenRowBackground: 'var(--bs-button-hover)',
            textColor: 'var(--bs-body-color)',
            strokeColor: 'var(--bs-body-color)',
            headerColor: 'var(--bs-body-color)',
            hoverColor: 'var(--bs-body-color)'
        }
    },
    scale_column
);

Figure 1: Overview of the results per method. This figures shows the mean of the scaled scores (group Overall), the mean scores per dataset (group Dataset) and the mean scores per metric (group Metric).

Display settings

viewof color_by_rank = Inputs.toggle({label: "Color by rank:", value: true})
viewof scale_column = Inputs.toggle({label: "Minmax column:", value: false})
viewof show_con = Inputs.toggle({label: "Show control methods:", value: true})

Filter datasets

viewof dataset_ids = Inputs.checkbox(
  dataset_info.filter(d => poss_dataset_ids.includes(d.dataset_id)),
  {
    keyof: d => d.dataset_name,
    valueof: d => d.dataset_id,
    value: dataset_info.map(d => d.dataset_id),
    label: "Datasets:"
  }
)

Filter methods

viewof method_ids = Inputs.checkbox(
  method_info.filter(d => poss_method_ids.includes(d.method_id)),
  {
    keyof: d => d.method_name,
    valueof: d => d.method_id,
    value: method_info.map(d => d.method_id),
    label: "Methods:"
  }
)

Filter metrics

viewof metric_ids = Inputs.checkbox(
  metric_info.filter(d => poss_metric_ids.includes(d.metric_id)),
  {
    keyof: d => d.metric_name,
    valueof: d => d.metric_id,
    value: metric_info.map(d => d.metric_id),
    label: "Metrics:"
  }
)

funkyheatmap = (await require('d3@7').then(d3 => {
  window.d3 = d3;
  window._ = _;
  return import('https://unpkg.com/funkyheatmapjs@0.2.5');
})).default;

Results

Results table of the scores per method, dataset and metric (after scaling). Use the filters to make a custom subselection of methods and datasets. The “Overall mean” dataset is the mean value across all datasets.

Dataset info

Show

Slide-seqV2 - Mouse Cortex

Source dataset · Data source · 26-09-2024 · 1.14 MiB

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Stickels et al. 2020).

Gene expression library of Mouse cortex profiled using Slide-seq V2.

10X Visium - Human Breast Cancer 1

Source dataset · Data source · 26-09-2024 · 494.65 KiB

Whole transcriptome analysis, Adult Human Breast Cancer (Visium) (10x Genomics 2020a).

10X Genomics obtained fresh frozen human Invasive Lobular Carcinoma breast tissue from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols Tissue Preparation Guide Demonstrated Protocol (CG000240). Tissue sections of 10µm were placed on Visium Gene Expression slides and fixed and stained following Methanol Fixation, H&E Staining & Imaging for Visium Spatial Protocols (CG000160).

Stereo-seq - Drosophila embryo E5_6

Source dataset · Data source · 26-09-2024 · 280.9 KiB

Stereo-seq faithfully captures Drosophila spatial transcriptomes with high resolution (M. Wang et al. 2022).

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. (Data from an embryo collected 14-16 h after egg laying)

10X Visium - Human Skin Melanoma

Source dataset · Data source · 26-09-2024 · 449.4 KiB

Gene expression library of Human Skin Melanoma (CytAssist FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2022d).

10x Genomics obtained FFPE Human Melanoma tissue blocks from Avaden Biosciences. The tissue was sectioned as described in Visium CytAssist Spatial Gene Expression for FFPE Tissue Preparation Guide Demonstrated Protocol (CG000518). Tissue sections of 5 µm was placed on a standard glass slide, deparaffinized followed by immunofluorescence (IF) staining. Sections were coverslipped with 85% glycerol, imaged, decoverslipped, followed by dehydration & decrosslinking Demonstrated Protocol (CG000519). The glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression slide. The probe extension and library construction steps follow the standard Visium for FFPE workflow outside of the instrument.

Stereo-seq - Drosophila embryo E6_3

Source dataset · Data source · 26-09-2024 · 280.43 KiB

Stereo-seq faithfully captures Drosophila spatial transcriptomes with high resolution (M. Wang et al. 2022).

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. (Data from an embryo collected 14-16 h after egg laying)

Stereo-seq - Drosophila embryo E9_1

Source dataset · Data source · 26-09-2024 · 291.12 KiB

Stereo-seq faithfully captures Drosophila spatial transcriptomes with high resolution (M. Wang et al. 2022).

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. (Data from an embryo collected 14-16 h after egg laying)

Slide-seqV2 - Mouse Hippocampus Puck

Source dataset · Data source · 26-09-2024 · 1.84 MiB

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Stickels et al. 2020).

Gene expression library of mouse hippocampus puck profiled using Slide-seq V2.

10X Visium - Human Heart

Source dataset · Data source · 26-09-2024 · 493.46 KiB

V1_Human_Heart (10x Genomics 2019a).

10x Genomics obtained fresh frozen human heart tissue from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols - Tissue Preparation Guide Demonstrated Protocol (CG000240). Tissue sections of 10 µm thickness were placed on Visium Gene Expression Slides.

Slide-seqV2 - Mouse Olfactory Bulb Puck

Source dataset · Data source · 26-09-2024 · 1.38 MiB

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Stickels et al. 2020).

Gene expression library of mouse olfactory bulk puck profiled using Slide-seq V2.

10X Visium - Human Lymph Node

Source dataset · Data source · 26-09-2024 · 479.59 KiB

Whole transcriptome analysis, Human Lymph Node (10x Genomics 2019b).

10x Genomics obtained fresh frozen human lymph node from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols - Tissue Preparation Guide Demonstrated Protocol (CG000240). Tissue sections of 10 µm thickness were placed on Visium Gene Expression Slides.

10X Post Xenium - Mouse Brain

Source dataset · Data source · 26-09-2024 · 567.12 KiB

Gene expression library of Post Xenium Mouse Brain (CytAssist Fresh Frozen) using the Mouse Whole Transcriptome Probe Set - Replicate 1 (10x Genomics 2023f).

This dataset is provided as part of the Technical Note: Post-Xenium In Situ Applications: Immunofluorescence, H&E, and Visium CytAssist Spatial Gene Expression (CG000709). Post-Xenium samples were compared to controls (samples not processed through the Xenium workflow) using 10 µm fresh-frozen (FF) serial sections.

10X Visium - Human Heart MI 1

Source dataset · Data source · 26-09-2024 · 494.84 KiB

Gene expression library of human heart using 10x Visium (Kuppe et al. 2022).

Frozen heart samples were embedded in OCT (Tissue-Tek) and cryosectioned (Thermo Cryostar). The 10-µm section was placed on the pre-chilled Optimization slides (Visium, 10X Genomics, PN-1000193) and the optimal lysis time was determined. The tissues were treated as recommended by 10X Genomics and the optimization procedure showed an optimal permeabilization time of 12 or 18 min of digestion and release of RNA from the tissue slide. Spatial gene expression slides (Visium, 10X Genomics, PN-1000187) were used for spatial transcriptomics following the Visium User Guides

10X Visium - Human Heart MI 2

Source dataset · Data source · 26-09-2024 · 510.62 KiB

Gene expression library of human heart using 10x Visium (Kuppe et al. 2022).

Frozen heart samples were embedded in OCT (Tissue-Tek) and cryosectioned (Thermo Cryostar). The 10-µm section was placed on the pre-chilled Optimization slides (Visium, 10X Genomics, PN-1000193) and the optimal lysis time was determined. The tissues were treated as recommended by 10X Genomics and the optimization procedure showed an optimal permeabilization time of 12 or 18 min of digestion and release of RNA from the tissue slide. Spatial gene expression slides (Visium, 10X Genomics, PN-1000187) were used for spatial transcriptomics following the Visium User Guides

MERFISH - Human Cortex 2

Source dataset · Data source · 26-09-2024 · 388.59 KiB

Spatially resolved profiling of human cerebral cortex using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (Fang et al. 2022).

Spatially resolved profiling of human cerebral cortex (middle temopral gyrus) replicate 1 using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (4000 gene panel).

DBiT-seq - Mouse Whole Body (E10)

Source dataset · Data source · 26-09-2024 · 222.68 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E10 whole mouse embryo tissue profiled using DBiT-seq.

10X Visium - Human Brain Cancer

Source dataset · Data source · 26-09-2024 · 957.06 KiB

Gene expression library of Human Glioblastoma (CytAssist FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2023a).

10x Genomics obtained FFPE human brain cancer tissue from Avaden Biosciences. The tissue was sectioned as described in the Visium CytAssist Spatial Gene Expression for FFPE - Tissue Preparation Guide (CG000518). Tissue section of 5 µm was placed on a standard glass slide, then stained following the Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000520). The glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression Slide v2, with 11 mm capture areas following the Visium CytAssist Spatial Gene Expression Reagent Kits User Guide (CG000495).

10X Visium - Human Lung Cancer

Source dataset · Data source · 26-09-2024 · 631.03 KiB

Gene expression library of Human Lung Cancer (CytAssist FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2023d).

10x Genomics obtained FFPE human lung cancer tissue from Avaden Biosciences. The tissue was sectioned as described in the Visium CytAssist Spatial Gene Expression for FFPE Tissue Preparation Guide (CG000518). Tissue section of 5 µm was placed on a standard glass slide, then stained following the Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000520). The glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression Slide v2, with 11 mm capture areas following the Visium CytAssist Spatial Gene Expression Reagent Kits User Guide (CG000495).

DBiT-seq - Mouse Lower Body (E11)

Source dataset · Data source · 26-09-2024 · 265.37 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E11 whole mouse embryo tissue (lower body in early-stage organogenesis) profiled using DBiT-seq.

10X Visium - Adult Human Cerebellum

Source dataset · Data source · 26-09-2024 · 544.49 KiB

Human Cerebellum Whole Transcriptome Analysis (10x Genomics 2020b).

10X Genomics obtained fresh frozen human cerebellum tissue from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols Tissue Preparation Guide (Demonstrated Protocol CG000240). Tissue sections of 10µm were placed on Visium Gene Expression slides and fixed and stained following Methanol Fixation, H&E Staining & Imaging for Visium Spatial Protocols (CG000160).

10X Visium - Human Kidney

Source dataset · Data source · 26-09-2024 · 610.9 KiB

Gene expression library of Human Kidney (CytAssist FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2023c).

10x Genomics obtained FFPE human kidney tissue from Avaden Biosciences. The tissue was sectioned as described in the Visium CytAssist Spatial Gene Expression for FFPE – Tissue Preparation Guide (CG000518). Tissue section of 5 µm was placed on a standard glass slide, then stained following the Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000520). The glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression Slide v2, with 11 mm capture areas following the Visium CytAssist Spatial Gene Expression Reagent Kits User Guide (CG000495).

10X Post Xenium - Human Colon

Source dataset · Data source · 26-09-2024 · 587.21 KiB

Gene expression library of Post Xenium Human Colon Cancer (CytAssist FFPE) using the Human Whole Transcriptome Probe Set - Replicate 1 (10x Genomics 2023e).

This dataset is provided as part of the Technical Note: Post-Xenium In Situ Applications: Immunofluorescence, H&E, and Visium CytAssist Spatial Gene Expression (CG000709). Post-Xenium samples were compared to controls (samples not processed through the Xenium workflow) using 5 µm (FFPE) serial sections.

10X Visium - Human Cervical Cancer

Source dataset · Data source · 26-09-2024 · 392.34 KiB

Gene expression library of Human Cervical Cancer (Visium FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2022b).

5 µm section from squamous cell carcinoma of human cervical cancer. FFPE tissue purchased from Discovery Life Sciences.

DBiT-seq - Mouse Brain (E10)

Source dataset · Data source · 26-09-2024 · 273.34 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E10 whole mouse embryo tissue (brain in early-stage organogenesis) profiled using DBiT-seq.

MERFISH - Human Cortex 4

Source dataset · Data source · 26-09-2024 · 461.49 KiB

Spatially resolved profiling of human cerebral cortex using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (Fang et al. 2022).

Spatially resolved profiling of human cerebral cortex (middle temopral gyrus) replicate 3 using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (4000 gene panel).

Slide-tags - Human Skin Melanoma

Source dataset · Data source · 26-09-2024 · 535.56 KiB

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics (Russell et al. 2023).

A metastatic melanoma sample was profiled by Slide-tags.

Stereo-seq - Drosophila embryo E7

Source dataset · Data source · 26-09-2024 · 257.9 KiB

Stereo-seq faithfully captures Drosophila spatial transcriptomes with high resolution (M. Wang et al. 2022).

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. (Data from an embryo collected 14-16 h after egg laying)

10X Visium - Human Breast Cancer 2

Source dataset · Data source · 26-09-2024 · 372.12 KiB

Gene expression library of Human Breast Cancer (Visium FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2021a).

10x Genomics obtained FFPE human breast tissue from BioIVT Asterand Human Tissue Specimens. The tissue was annotated with Ductal Carcinoma In Situ, Invasive Carcinoma. The tissue was sectioned as described in Visium Spatial Gene Expression for FFPE - Tissue Preparation Guide Demonstrated Protocol (CG000408). Tissue sections of 5 µm were placed on Visium Gene Expression slides, then stained following Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000409).

DBiT-seq - Mouse Whole Body 2 (E11)

Source dataset · Data source · 26-09-2024 · 291.34 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E11 whole mouse embryo tissue profiled using DBiT-seq.

DBiT-seq - Mouse Whole Body 1 (E11)

Source dataset · Data source · 26-09-2024 · 274.93 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E11 whole mouse embryo tissue profiled using DBiT-seq.

10X Visium - Mouse Olfactory Bulb

Source dataset · Data source · 26-09-2024 · 278.43 KiB

10X Genomics obtained fresh frozen mouse olfactory bulb tissue from BioIVT (10x Genomics 2022a).

The tissue was embedded and cryosectioned as described in Visium Spatial Protocols Tissue Preparation Guide (Demonstrated Protocol CG000240). Tissue sections of 10µm were placed on Visium Gene Expression slides, then fixed and stained following Methanol Fixation, H&E Staining & Imaging for Visium Spatial Protocols (CG000160).

Slide-tags - Mouse Embryo

Source dataset · Data source · 26-09-2024 · 521.92 KiB

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics (Russell et al. 2023).

Mouse embryo tonsil was profiled by Slide-tags.

Slide-tags - Human Tonsil

Source dataset · Data source · 26-09-2024 · 601.99 KiB

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics (Russell et al. 2023).

A human tonsil was profiled by Slide-tags.

10X Visium - Human Intestine Cancer

Source dataset · Data source · 26-09-2024 · 380.56 KiB

Gene expression library of Human Intestinal Cancer (Visium FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2022c).

5 µm section from Human Intestinal Cancer. FFPE tissue purchased from BioIVT Asterand Human Tissue Specimens. Libraries were prepared following the Visium Spatial Gene Expression Reagent Kits for FFPE User Guide (CG000407 Rev A).

Slide-tags - Human Cortex

Source dataset · Data source · 26-09-2024 · 480.46 KiB

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics (Russell et al. 2023).

A 100 mm2 region of the human prefrontal cortex from a neurotypical donor aged 78 years was profiled by Slide-tags.

DBiT-seq - Mouse Eye (E10)

Source dataset · Data source · 26-09-2024 · 319.56 KiB

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue (Liu et al. 2020).

Gene expression library of an E10 whole mouse embryo tissue (eye in early-stage organogenesis) profiled using DBiT-seq.

10X Visium - Mouse Brain Coronal

Source dataset · Data source · 26-09-2024 · 357.62 KiB

Gene expression library of Mouse Brain (CytAssist FFPE) using the Mouse Whole Transcriptome Probe Set (10x Genomics 2022f).

FFPE Mouse Brain tissue blocks sectioned as described in Visium CytAssist Spatial Gene Expression for FFPE - Tissue Preparation Guide Demonstrated Protocol. The H&E stained glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression slide. The probe extension and library construction steps follow the standard Visium for FFPE workflow outside of the instrument. The H&E image was acquired using Olympus VS200 Slide Scanning Microscope. Sequencing depth was 53,497 reads per spot. Sequencing configuration: 28bp read 1 (16bp Visium spatial barcode, 12bp UMI), 90bp read 2 (transcript), 10bp i7 sample barcode and 10bp i5 sample barcode. Key metrics include: 2,310 spots detected under tissue; 6,736 median genes per spot; 24,862 median UMI counts per spot.

MERFISH - Mouse Cortex

Source dataset · Data source · 26-09-2024 · 1 MiB

Spatially resolved profiling of mouse cerebral cortex using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (Fang et al. 2022).

Spatially resolved profiling of mouse cerebral cortex (visual cortex (VIS), auditory cortex (AUD) and temporal association area (TEa) unexpanded sections) using multiplexed error-robust fluorescence in situ hybridization (MERFISH).

10X Visium - Human Prostate Cancer

Source dataset · Data source · 26-09-2024 · 437.56 KiB

Gene expression library of Human Prostate Cancer (Visium FFPE) with an IF image using the Human Whole Transcriptome Probe Set (10x Genomics 2022e).

10x Genomics obtained FFPE human prostate tissue from Indivumed Human Tissue Specimens. Original diagnosis with adenocarcinoma. The tissue was sectioned as described in Visium Spatial Gene Expression for FFPE Tissue Preparation Guide Demonstrated Protocol (CG000408). Tissue sections of 10 µm were placed on Visium Gene Expression slides, then stained following Deparaffinization, Decrosslinking, Immunofluorescence Staining & Imaging Demonstrated Protocol (CG000410).

Slide-seqV2 - Mouse Somatosensory Cortex Puck

Source dataset · Data source · 26-09-2024 · 1.39 MiB

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Stickels et al. 2020).

Gene expression library of mouse somatosensory cortex puck profiled using Slide-seq V2.

Stereo-seq - Drosophila embryo E10

Source dataset · Data source · 26-09-2024 · 276.12 KiB

Stereo-seq faithfully captures Drosophila spatial transcriptomes with high resolution (M. Wang et al. 2022).

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. (Data from an embryo collected 14-16 h after egg laying)

Slide-seqV2 - Mouse Cerebellum

Source dataset · Data source · 26-09-2024 · 1.59 MiB

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2 (Stickels et al. 2020).

Gene expression library of mouse cerebellum profiled using Slide-seq V2.

10X Visium - Mouse Kidney 1

Source dataset · Data source · 26-09-2024 · 298.31 KiB

Mouse Kidney Whole Transcriptome Analysis (10x Genomics 2020c).

10x Genomics obtained fresh frozen mouse kidney tissue from BioIVT Asterand. The tissue was embedded and cryosectioned as described in Visium Spatial Protocols - Tissue Preparation Guide Demonstrated Protocol (CG000240). Tissue sections of 10 µm thickness from a slice of the coronal plane were placed on Visium Gene Expression slides, then stained following the Methanol Fixation, H&E Staining & Imaging Demonstrated Protocol (CG000160).

10X Visium - Mouse Embryo

Source dataset · Data source · 26-09-2024 · 650.49 KiB

Gene expression library of Mouse Embryo (CytAssist FFPE) using the Mouse Whole Transcriptome Probe Set (10x Genomics 2023g).

The tissue was sectioned as described in Visium CytAssist Spatial Gene Expression for FFPE Tissue Preparation Guide Demonstrated Protocol CG000518. Tissue sections of 5 µm was placed on a standard glass slide, and H&E-stained following deparaffinization. Sections were coverslipped with 85% glycerol, imaged, decoverslipped, followed by dehydration & decrosslinking (Demonstrated Protocol CG000520). The glass slide with the tissue section was processed with the Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression slide (11 mm Capture Area). The probe extension and library construction steps follow the standard Visium for FFPE workflow outside of the instrument.

10X Visium - Human Colorectal Cancer

Source dataset · Data source · 26-09-2024 · 833.15 KiB

Gene expression library of Human Colorectal Cancer (CytAssist FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2023b).

The tissue was sectioned as described in the Visium CytAssist Spatial Gene Expression for FFPE Tissue Preparation Guide (CG000518). Tissue section of 5 µm was placed on a standard glass slide, then stained following the Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000520). The glass slide with tissue section was processed via Visium CytAssist instrument to transfer analytes to a Visium CytAssist Spatial Gene Expression Slide v2, with 11 mm capture areas following the Visium CytAssist Spatial Gene Expression Reagent Kits User Guide (CG000495).

MERFISH - Human Cortex 1

Source dataset · Data source · 26-09-2024 · 745.19 KiB

Spatially resolved profiling of human cerebral cortex using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (Fang et al. 2022).

Spatially resolved profiling of human cerebral cortex (middle temopral gyrus) replicate 1 using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (250 gene panel).

MERFISH - Human Cortex 3

Source dataset · Data source · 26-09-2024 · 463.84 KiB

Spatially resolved profiling of human cerebral cortex using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (Fang et al. 2022).

Spatially resolved profiling of human cerebral cortex (middle temopral gyrus) replicate 2 using multiplexed error-robust fluorescence in situ hybridization (MERFISH) (4000 gene panel).

Seqfish - Mouse Organogenesis

Data source · 26-09-2024 · 1.43 MiB

Single-cell spatial expression of mouse organogenesis (Lohoff et al. 2021).

Sagittal sections from mouse embryo at the 8-12 ss was profiled by seqFISH.

STARmap - Mouse Brain 1

Source dataset · Data source · 26-09-2024 · 262.22 KiB

Three-dimensional intact-tissue sequencing of single-cell transcriptional states (X. Wang et al. 2018).

3D architecture of cell types in visual cortex volumes.

STARmap - Mouse Brain 2

Source dataset · Data source · 26-09-2024 · 381.63 KiB

Three-dimensional intact-tissue sequencing of single-cell transcriptional states (X. Wang et al. 2018).

3D architecture of cell types in visual cortex volumes.

10X Visium - Human Normal Prostate

Source dataset · Data source · 26-09-2024 · 372.9 KiB

Gene expression library of Human Normal Prostate (Visium FFPE) using the Human Whole Transcriptome Probe Set (10x Genomics 2021b).

10x Genomics obtained FFPE human prostate tissue from Indivumed Human Tissue Specimens. The tissue was sectioned as described in Visium Spatial Gene Expression for FFPE – Tissue Preparation Guide Demonstrated Protocol (CG000408). Tissue sections of 5 µm were placed on Visium Gene Expression slides, then stained following Deparaffinization, H&E Staining, Imaging & Decrosslinking Demonstrated Protocol (CG000409).

Method info

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BOOST-GP

Documentation · Repository · Source Code · Container · build_main

Bayesian modeling of spatial molecular profiling data via Gaussian process (Li et al. 2021)

BOOST-GP a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications.

GPcounts

Documentation · Repository · Source Code · Container · build_main

GPcounts is non-parametric modelling of temporal and spatial counts data from RNA-seq experiments (BinTayyash et al. 2021)

The GPcounts package implements GP regression methods for modelling counts data using a negative binomial likelihood function. Computational efficiency is achieved through the use of variational Bayesian inference. The GP function models changes in the mean of the negative binomial likelihood through a logarithmic link function and the dispersion parameter is fitted by maximum likelihood. We validate the method on simulated time course data, showing better performance to identify changes in over-dispersed counts data than methods based on Gaussian or Poisson likelihoods.

Moran’s I

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Moran’s I is a measurement of spatial autocorrelation (Palla et al. 2022)

The MoranI global spatial auto-correlation statistics evaluates whether features (i.e. genes) shows a pattern that is clustered, dispersed or random in the tissue are under consideration.

nnSVG

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nnSVG is based on nearest-neighbor Gaussian process (NNGP) models to estimate parameters in GPs (Weber et al. 2023)

nnSVG identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains. It uses gene-specific estimates of length scale parameters within the Gaussian process models, and scales linearly with the number of spatial locations.

scGCO

Documentation · Repository · Source Code · Container · build_main

Identification of spatially variable genes with graph cuts (Zhang, Feng, and Wang 2022)

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond..

Sepal

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Sepal simulates diffusion of individual transcripts to extract genes with spatial patterns (Andersson and Lundeberg 2021)

This method assesses the degree of randomness exhibited by each transcript profile and rank them accordingly.

SOMDE

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SOMDE is a scalable method for identifying spatially variable genes with self-organizing map (Hao, Hua, and Zhang 2021)

SOMDE uses self-organizing map to cluster neighboring cells into nodes, and then uses a Gaussian process to fit the node-level spatial gene expression to identify SVgenes. Experiments show that SOMDE is about 5 to 50 times faster than existing methods with comparable results. The adjustable resolution of SOMDE makes it the only method that can give results in about 5 min in large datasets of more than 20 000 sequencing sites.

SpaGCN

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Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (Hu et al. 2021)

To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.

SpaGFT

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SpaGFT is a graph Fourier transform for tissue module identification from spatially resolved transcriptomics (Chang et al. 2022)

The tissue module (TM) was defined as an architectural area containing recurrent cellular communities executing specific biological functions at different tissue sites. However, the computational identification of TMs poses challenges owing to their various length scales, convoluted biological processes, not well-defined molecular features, and irregular spatial patterns. Here, we present a hypothesis-free graph Fourier transform model, SpaGFT, to characterize TMs. For the first time, SpaGFT transforms complex gene expression patterns into simple, but informative signals, leading to the accurate identification of spatially variable genes (SVGs) at a fast computational speed. Based on clustering the transformed signals of the SVGs, SpaGFT provides a novel computational framework for TM characterization. Three case studies were used to illustrate TM identities, the biological processes of convoluted TMs in the lymph node, and conserved TMs across multiple samples constituting the complex organ. The superior accuracy, scalability, and interpretability of SpaGFT indicate that it is a novel and powerful tool for the investigation of TMs to gain new insights into a variety of biological questions.

Spanve

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Spanve is a non-parametric statistical approach based on modeling space dependence as a distance of two distributions for detecting SV genes (Cai et al. 2023)

The depiction of in situ gene expression through spatial transcriptomics facilitates the inference of cell function mechanisms. To build spatial maps of transcriptomes, the first and crucial step is to identify spatially variable (SV) genes. However, current methods fall short in dealing with large-scale spatial transcriptomics data and may result in a high false positive rate due to the modeling of gene expression into parametric distributions. This paper introduces Spanve (https://github.com/zjupgx/Spanve), a non-parametric statistical approach based on modeling space dependence as a distance of two distributions for detecting SV genes. The high computing efficiency and accuracy of Spanve is demonstrated through comprehensive benchmarking. Additionally, Spanve can detect clustering-friendly SV genes and spatially variable co-expression, facilitating the identification of spatial tissue domains by an imputation.

SPARK

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Spatial PAttern Recognition via Kernels (Sun, Zhu, and Zhou 2020)

SPARK builds upon a generalized linear spatial model (GLSM) with a variety of spatial kernels to accommodate count data. With a newly developed penalized quasi-likelihood (PQL) algorithm, SPARK is scalable to analyzing tens of thousands of genes across tens of thousands spatial locations.

SPARK-X

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SPARK-X is a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies (Zhu, Sun, and Zhou 2021)

Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.

SpatialDE

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SpatialDE is a method for identify spatially variable genes based on Gaussian Process model (Svensson, Teichmann, and Stegle 2018)

SpatialDE decomposes expression variability into spatial and nonspatial components using two random effect terms: a spatial variance term that parametrizes gene expression covariance by pairwise distances of samples, and a noise term that models nonspatial variability.

SpatialDE2

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SpatialDE2: Fast and localized variance component analysis of spatial transcriptomics (Kats, Vento-Tormo, and Stegle 2021)

Spatial transcriptomics is now a mature technology, allowing to assay gene expression changes in the histological context of complex tissues. A canonical analysis workflow starts with the identification of tissue zones that share similar expression profiles, followed by the detection of highly variable or spatially variable genes. Rapid increases in the scale and complexity of spatial transcriptomic datasets demand that these analysis steps are conducted in a consistent and integrated manner, a requirement that is not met by current methods. To address this, we here present SpatialDE2, which unifies the mapping of tissue zones and spatial variable gene detection as integrated software framework, while at the same time advancing current algorithms for both of these steps. Formulated in a Bayesian framework, the model accounts for the Poisson count noise, while simultaneously offering superior computational speed compared to previous methods. We validate SpatialDE2 using simulated data and illustrate its utility in the context of two real-world applications to the spatial transcriptomics profiles of the mouse brain and human endometrium.

Control method info

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Random Ranking

Documentation · Repository · Source Code · Container · build_main

Negative control method that randomly rank genes

A negative control method with random ranking of genes.

True Ranking

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Positive control method that correctly rank genes

A positive control method with correct ranking of genes.

Metric info

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correlation

Source code · Container

Correlation represents the agreement of true and predicted spatial variability (KENDALL 1938).

Kendall rank correlation coefficient measures the ordinal association between two measured quantities. The best score and upper bound is 1 (observations have an identical rank), while the lower bound is -1 (observations have a completely different rank).

Quality control results

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Category	Name	Value	Condition	Severity
Raw results	Method 'boostgp' %missing	0.7800	pct_missing <= .1	✗✗✗
Dataset info	Pct 'task_id' missing	1.0000	percent_missing(dataset_info, field)	✗✗
Method info	Pct 'paper_reference' missing	0.8750	percent_missing(method_info, field)	✗✗
Metric info	Pct 'paper_reference' missing	1.0000	percent_missing(metric_info, field)	✗✗
Raw results	Dataset 'zenodo_spatial/merfish/mouse_cortex' %missing	0.2500	pct_missing <= .1	✗✗
Raw results	Dataset 'zenodo_spatial/seqfish/mouse_organogenesis_seqfish' %missing	0.1875	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/slideseqv2/mouse_cortex' %missing	0.1875	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/stereoseq/drosophila_embryo_e6_3' %missing	0.1875	pct_missing <= .1	✗
Raw results	Method 'spark' %missing	0.1600	pct_missing <= .1	✗
Raw results	Dataset 'tenx_visium/visium/human_brain_cancer' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'tenx_visium/visium/human_normal_prostate' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/slideseqv2/mouse_cerebellum' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/slideseqv2/mouse_hippocampus_puck' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/slideseqv2/mouse_olfactory_bulb_puck' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/slideseqv2/mouse_somatosensory_cortex_puck' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/starmap/mouse_brain_2d_zstep10_0' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/starmap/mouse_brain_2d_zstep15_0' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/stereoseq/drosophila_embryo_e10' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/stereoseq/drosophila_embryo_e5_6' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/stereoseq/drosophila_embryo_e7' %missing	0.1250	pct_missing <= .1	✗
Raw results	Dataset 'zenodo_spatial/stereoseq/drosophila_embryo_e9_1' %missing	0.1250	pct_missing <= .1	✗
Raw results	Method 'somde' %missing	0.1200	pct_missing <= .1	✗

Normalisation visualisation

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Authors

• Zhijian Li (author, maintainer) ,

• Zain M. Patel (author)

• Dongyuan Song (author)

• Guanao Yan (author)

• Jingyi Jessica Li (author)

• Luca Pinello (author)

• Robrecht Cannoodt (contributor) ,

• Sai Nirmayi Yasa (contributor) ,

References

10x Genomics. 2019a. “Human Heart.” https://www.10xgenomics.com/datasets/human-heart-1-standard-1-0-0.

———. 2019b. “Human Lymph Node.” https://www.10xgenomics.com/datasets/human-lymph-node-1-standard-1-0-0.

———. 2020a. “Human Breast Cancer: Whole Transcriptome Analysis.” https://www.10xgenomics.com/datasets/human-breast-cancer-whole-transcriptome-analysis-1-standard-1-2-0.

———. 2020b. “Human Cerebellum: Whole Transcriptome Analysis.” https://www.10xgenomics.com/datasets/human-cerebellum-whole-transcriptome-analysis-1-standard-1-2-0.

———. 2020c. “Mouse Kidney Section (Coronal).” https://www.10xgenomics.com/datasets/mouse-kidney-section-coronal-1-standard-1-1-0.

———. 2021a. “Human Breast Cancer: Ductal Carcinoma in Situ, Invasive Carcinoma (FFPE).” https://www.10xgenomics.com/datasets/human-breast-cancer-ductal-carcinoma-in-situ-invasive-carcinoma-ffpe-1-standard-1-3-0.

———. 2021b. “Normal Human Prostate (FFPE).” https://www.10xgenomics.com/datasets/normal-human-prostate-ffpe-1-standard-1-3-0.

———. 2022a. “Adult Mouse Olfactory Bulb.” https://www.10xgenomics.com/datasets/adult-mouse-olfactory-bulb-1-standard-1.

———. 2022b. “Human Cervical Cancer (FFPE).” https://www.10xgenomics.com/datasets/human-cervical-cancer-1-standard.

———. 2022c. “Human Intestine Cancer (FPPE).” https://www.10xgenomics.com/datasets/human-intestine-cancer-1-standard.

———. 2022d. “Human Melanoma, IF Stained (FFPE).” https://www.10xgenomics.com/datasets/human-melanoma-if-stained-ffpe-2-standard.

———. 2022e. “Human Prostate Cancer, Adjacent Normal Section with IF Staining (FFPE).” https://www.10xgenomics.com/datasets/human-prostate-cancer-adjacent-normal-section-with-if-staining-ffpe-1-standard.

———. 2022f. “Mouse Brain Coronal Section 1 (FFPE).” https://www.10xgenomics.com/datasets/mouse-brain-coronal-section-1-ffpe-2-standard.

———. 2023a. “Human Brain Cancer, 11 Mm Capture Area (FFPE).” https://www.10xgenomics.com/datasets/human-brain-cancer-11-mm-capture-area-ffpe-2-standard.

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———. 2023c. “Human Kidney, 11 Mm Capture Area (FFPE).” https://www.10xgenomics.com/datasets/human-kidney-11-mm-capture-area-ffpe-2-standard.

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