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Intermediates Folder

Analysis Output

When the analysis run completes, the DRAGEN StrataMap analysis software generates an intermediate analysis output folder in a specified location. This folder contains outputs from each of the modules within the analysis pipeline, created as the pipeline runs. To view analysis output, navigate to the analysis output folder and select the files that you want to view.

Analysis Output Folder Structure

The intermediate_results directory contains the following top-level folders and files:

  • 00_bclconvert/ - BCL conversion outputs

  • 01_alignments/ - Alignment results and statistics

  • 02_sequencing_saturation/ - Sequencing saturation curves

  • 03_matrix_metrics/ - Matrix metrics reports (10 µm bin, cell bin, and SBC reports)

  • 04_heatmap/ - Heatmap visualizations

  • 05_clustering/ - Clustering results (cell-binned and grid-binned)

  • 06_cell_segmentation/ - Cell segmentation masks and contours

  • fastqs/ - BCL Convert output files

  • logs/ - Pipeline logs

  • params_inputs/ - Input parameters

  • stats/ - Consolidated statistics per sample

  • summary/ - Aggregated run-level summaries

  • samplesheet.csv - Sample sheet for the run

In folders with sample-specific results, all files are organized by sample ID. For example:

  • 01_alignments/Sample_ID1/

  • 01_alignments/Sample_ID2/

Note: Some samples may have multiple sub-samples with suffixes like _x001, _x002, _x003 in the alignments folder.

00 BCLconvert

Contains the outputs from BCLconvert, including Reports and Logs folders.

Directory structure:

The FlowCell ID (Substrate ID) of the sequencing read is appended to the Logs and Reports folder names to differentiate runs with multiple flow cell runs of sequencing data.

DRAGEN StrataMap runs an updated BCLconvert that demultiplexes by SBC sequence.

01 Alignments

Contains alignment results for each sample, organized by Sample_ID.

Files per Sample

Alignment Statistics Files:

  • {Sample_ID}_alignment_table_user_friendly.csv - User-friendly alignment summary

  • Alignment_stats.csv - Detailed alignment statistics

  • Fastqc_stats.csv - FastQC quality metrics

  • Quant_stats.csv - Quantification statistics

  • Trimming_stats.csv - Trimming statistics

Multimapping Files (produced when multimappers are included, which is the default):

  • {Sample_ID}.spatial.multimap_ties_summary.csv - Summary of multimapping ties

  • {Sample_ID}.spatial.multimap_ties.tsv - Detailed multimapping information

These files are not produced when include_only_uniquely_aligned is set to true.

BAM file (if enabled by user):

  • {Sample_ID}.spatial.bam and {Sample_ID}.spatial.bam.bai - Output only when the output-bam flag is enabled. Not produced by default.

Alignment Stats.csv

Metric
Description

n_dragen_input_reads

Number of reads given to DRAGEN for alignment

n_reads_mapped_to_genome / percent_reads_mapped_to_genome

Number/percent of reads that map to the genome

n_reads_unmapped / percent_reads_unmapped

Number/percent of reads that do not map to the genome

n_reads_multimapped_to_genome / percent_reads_multimapped_to_genome

Number/percent of reads that map to multiple places in the genome

n_reads_uniquely_mapped_to_genome / percent_reads_uniquely_mapped_to_genome

Number/percent of unique, unambiguous alignments

n_unique_SBC

Number of unique spatial barcodes (SBCs) detected

n_reads_w_error_free_barcodes

Number of reads with error-free barcodes

n_reads_w_corrected_barcodes

Number of reads with corrected barcodes

n_deduplicated_reads

Number of reads remaining after de-duplication (i.e., the number of unique molecules)

sequencing_saturation

The fraction of reads that were the second or greater occurrence of a molecular identity. Calculated as 1 - (# deduplicated reads / # total reads)

n_reads_mapped_to_gene / percent_reads_mapped_to_gene

Number/percent of alignments assigned to genes

n_reads_unique_exon_matches

Number of unique alignments that were assigned to exons

n_reads_unique_intron_matches

Number of unique alignments that were assigned to introns

n_multimapped_reads_filtered

Number of multimapped reads that were filtered out

n_multimapped_reads_with_gene_matches

Number of multimapped reads that matched to genes

Fastqc stats.csv

Metric
Description

after_trimming_mean_payload_quality

Average Q-score after trimming

Quant Stats

Metric
Description

Exonic_Forward_strand

Number of reads that match transcripts on the forward strand

Exonic_Reverse_stand

Number of reads that match transcripts on the reverse strand (note: "stand" is misspelled in actual output)

Exonic_Mismatch_Strand

In the case of stranded library orientation, number of reads that do not match the expected strand of the transcript

Exonic_Ambiguous_strand

Reads that match transcripts in both forward and reverse orientation

Exonic_Unknown_transcript

Reads that overlap with an exon of a gene, but do not match any transcript (mismatched splice sites)

exonic_alignments / exonic_alignments_percent

Number/percent of alignments assigned to an exon

intronic_alignments / intronic_alignments_percent

Number/percent of alignments assigned to an intron

intergenic_alignments / intergenic_alignments_percent

Number/percent of alignments to regions between genes

5_prime_bias

Average 5' bias in transcripts with assigned alignments. Calculated as (Average coverage in 5' region) / (average coverage across entire transcript)

3_prime_bias

Average 3' bias in transcripts with assigned alignments. Calculated as (Average coverage in 3' region) / (average coverage across entire transcript)

Trimming stats

DRAGEN StrataMap trims in one step: it trims adapters, low-quality bases, polyA, and polyG, and filters any reads that are too short (<22 nt).

Metric
Description

n_reads_mapped_to_SBCs

Number of reads successfully mapped to spatial barcodes (SBCs)

polyg_trimmed_percentage

Percentage of payload reads trimmed due to polyG nucleotides

quality_trimmed_percentage

Percentage of payload reads trimmed due to low-quality sequences (Q-score < 20)

adapter_trimmed_percentage

Percentage of payload reads trimmed by adapter trimming

polya_trimmed_percentage

Percentage of payload reads trimmed by polyA trimming

n_reads_removed_by_trimming / n_reads_removed_by_trimming_percentage

Number/percent of reads that were removed by trimming due to being too short

n_reads_passing_trimming / n_reads_passing_trimming_percentage

Number/percent of reads that were kept by trimming and passed to the alignment module

02 Sequencing Saturation

Contains sequencing saturation curves and metrics for each sample.

Directory structure:

Files per Sample

Saturation Curve Data:

  • {Sample_ID}_saturation_curve_cell.csv - Cell-binned saturation curve data

  • {Sample_ID}_saturation_curve_grid.csv - Grid-binned saturation curve data

Saturation at Fixed Depths:

  • {Sample_ID}_saturation_at_fixed_depths_cell.csv - Saturation metrics at specific sequencing depths (cell-binned)

  • {Sample_ID}_saturation_at_fixed_depths_grid.csv - Saturation metrics at specific sequencing depths (grid-binned)

Saturation Plots (PNG):

Multiple saturation-curve plot variants are produced per sample (molecules and genes, per cell and per 10 µm bin, plotted against SBC-filtered reads or total aligned reads). File names follow the pattern {Sample_ID}_{mol|genes}_saturation_{percell|perbin10}_{SBC_filtered_reads|reads_aligned}.png.

03 Matrix Metrics

Contains detailed matrix metrics for each sample with three types of reports:

  1. 10 µm bin report - Grid-binned at 10 µm resolution

  2. Cell bin report - Cell-based binning

  3. SBC report - Spatial-barcode-level metrics

    • May be downsampled depending on sample size.

Directory structure:

10 µm Bin Report Files

Files in {Sample_ID}_10umbin_report/:

  • {Sample_ID}_10umbin_summary_report.csv - Summary statistics

  • {Sample_ID}_10umbin_total_gene_counts.csv - Total UMI counts per gene

  • {Sample_ID}_10umbin_top100_genes.csv - Top 100 genes with the highest UMI counts

  • {Sample_ID}_10umbin_all_hist.png - Combined histogram of UMI/gene/rRNA/mtRNA counts per 10 µm bin

  • {Sample_ID}_10umbin_all_vln.png - Combined violin plot of UMI/gene/rRNA/mtRNA counts per 10 µm bin

  • {Sample_ID}_10umbin_individual_hist.png - Individual histograms of UMI/gene/rRNA/mtRNA counts per 10 µm bin

  • {Sample_ID}_10umbin_individual_vln.png - Individual violin plots of UMI/gene/rRNA/mtRNA counts per 10 µm bin

Summary Report Metrics ({Sample_ID}_10umbin_summary_report.csv):

Metric
Description

num_10umbins_wUMI

Number of 10 µm bins with at least one UMI

umi_total

Total UMI counts in the whole sample

rRNA_total

Total rRNA counts in the whole sample

rRNA_per

Total rRNA percentage in the whole sample

mtRNA_total

Total mtRNA counts in the whole sample

mtRNA_per

Total mtRNA percentage in the whole sample

umi_mean, umi_median, umi_min, umi_max, umi_std

Statistics of UMIs detected per 10 µm bin

gene_mean, gene_median, gene_min, gene_max, gene_std

Statistics of genes detected per 10 µm bin

rRNA_mean, rRNA_median, rRNA_min, rRNA_max, rRNA_std

Statistics of rRNA detected per 10 µm bin

mtRNA_mean, mtRNA_median, mtRNA_min, mtRNA_max, mtRNA_std

Statistics of mtRNA detected per 10 µm bin

Cell Bin Report Files

Files in {Sample_ID}_cell_report/:

  • {Sample_ID}_cell_summary_report.csv - Summary statistics

  • {Sample_ID}_cell_total_gene_counts.csv - Total UMI counts per gene

  • {Sample_ID}_cell_top100_genes.csv - Top 100 genes with the highest UMI counts

  • {Sample_ID}_cell_all_hist.png - Combined histogram of UMI/gene/rRNA/mtRNA counts per cell

  • {Sample_ID}_cell_all_vln.png - Combined violin plot of UMI/gene/rRNA/mtRNA counts per cell

  • {Sample_ID}_cell_individual_hist.png - Individual histograms of UMI/gene/rRNA/mtRNA counts per cell

  • {Sample_ID}_cell_individual_vln.png - Individual violin plots of UMI/gene/rRNA/mtRNA counts per cell

Summary Report Metrics ({Sample_ID}_cell_summary_report.csv): Same structure as the 10 µm bin report, but with cell-level statistics instead of grid bins.

SBC Report Files

Files in {Sample_ID}_SBC_report/:

  • {Sample_ID}_SBC_summary_report.csv - Summary statistics

  • {Sample_ID}_SBC_total_gene_counts.csv - Total UMI counts per gene

  • {Sample_ID}_SBC_top100_genes.csv - Top 100 genes with the highest UMI counts

  • {Sample_ID}_SBC_top100_SBC.csv - Top 100 spatial barcodes by UMI count

  • {Sample_ID}_SBC_all_hist.png - Combined histogram of UMI/gene/rRNA/mtRNA counts per SBC

  • {Sample_ID}_SBC_all_vln.png - Combined violin plot of UMI/gene/rRNA/mtRNA counts per SBC

  • {Sample_ID}_SBC_individual_hist.png - Individual histograms of UMI/gene/rRNA/mtRNA counts per SBC

  • {Sample_ID}_SBC_individual_vln.png - Individual violin plots of UMI/gene/rRNA/mtRNA counts per SBC

  • {Sample_ID}_SBC_umi_distribution.png - UMI distribution plot per SBC

  • {Sample_ID}_SBC_umi_cumulative_distribution.png - Cumulative UMI distribution per SBC

Summary Report Metrics ({Sample_ID}_SBC_summary_report.csv): Same structure as the 10 µm bin report, but with SBC-level statistics instead of grid bins.

Notes:

  • rRNA is defined as genes from reference GTF files with gene_type or gene_biotype labeled as rRNA, mt-rRNA, or rRNA_pseudogene.

  • mtRNA is defined as any gene on the mitochondrial chromosome. The mitochondrial chromosome is either user-defined (using the mitochondrial_chr parameter) or is detected by the pipeline, which looks for a chromosome named chrm, m, or mt.

04 Heatmap

The following outputs are produced for every sample:

  • All transcript counts per 10 µm bin

  • All transcript counts per cell bin

05 Clustering

Contains Leiden clustering results for both cell-binned and grid-binned data.

Directory structure:

Cell-Binned Clustering Files

Files in {Sample_ID}_cell_binned/:

  • {Sample_ID}_cell_raw.h5ad - Raw cell-binned AnnData object

  • {Sample_ID}_cell_filtered.h5ad - Filtered cell-binned AnnData object

  • {Sample_ID}_cell_clustered.h5ad - Clustered cell-binned AnnData object

  • {Sample_ID}_cell_umap.png - UMAP visualization

  • {Sample_ID}_cell_sc_spatial.png - Spatial clustering visualization

Grid-Binned (10 µm) Clustering Files

Files in {Sample_ID}_grid_binned_10um/:

  • {Sample_ID}_10um_raw.h5ad - Raw 10 µm grid-binned AnnData object

  • {Sample_ID}_10um_filtered.h5ad - Filtered 10 µm grid-binned AnnData object

  • {Sample_ID}_10um_clustered.h5ad - Clustered 10 µm grid-binned AnnData object

  • {Sample_ID}_10um_umap.png - UMAP visualization

  • {Sample_ID}_10um_sc_spatial.png - Spatial clustering visualization

Note: The AnnData (.h5ad) files are in the standard single-cell analysis format compatible with Scanpy and other Python-based analysis tools.

06 Cell Segmentation

  • This directory contains segmentation results for the registered images.

  • The cell mask file (or nuclei mask file) is in TIFF format, where pixel intensities correspond to cell IDs (pixel intensity 0 means no-cell or no-nuclei region).

  • The contour coordinates CSV files include three columns: cell-ID, x-coordinate (µm), and y-coordinate (µm). There is one CSV file for nuclei contours (*nuclei_contour_coords.csv) and one for cell contours (*Expanded_5um_cell_contour_coords.csv). Contour coordinates are in substrate coordinate space, so they can be overlaid with spatial data (raw, 10 µm binned, and cell binned) without any further adjustment.

  • To overlay cell or nuclei masks with spatial data (raw, 10 µm binned, and cell binned), you must translate the coordinates in barcodes.tsv.gz from substrate coordinate space to the local coordinate space of the sample region. This can be done by subtracting the global shifts (described above) from the substrate coordinates reported in barcodes.tsv.gz for each sample.

  • Cell regions are obtained by expanding detected nuclei regions.

  • More detailed information about the segmentation output structure can be found here: Illumina StrataMap Cell Segmentation Tool

Contains cell segmentation results, including masks and contour coordinates.

Directory structure:

Nuclei Segmentation Files

Files in nuclei_segmentation/:

  • {Sample_ID}_nuclei_segmentation_mask.tif - Nuclei mask in TIFF format, where pixel intensities correspond to cell IDs (pixel intensity 0 means no-nuclei region)

  • {Sample_ID}_nuclei_contour_coords.csv - Contour coordinates CSV with three columns: cell-ID, x-coordinate (µm), y-coordinate (µm)

  • {Sample_ID}_nuclei_information.csv - Detailed information about each detected nucleus

  • {Sample_ID}_cell_counts.txt - Number of detected nuclei

Cell Border Expansion (5 µm) Files

Files in cell_border_expansion5um/:

  • {Sample_ID}_Expanded_5um_cell_segmentation_mask.tif - Cell mask in TIFF format, where pixel intensities correspond to cell IDs (pixel intensity 0 means no-cell region)

  • {Sample_ID}_Expanded_5um_cell_contour_coords.csv - Cell contour coordinates CSV with three columns: cell-ID, x-coordinate (µm), y-coordinate (µm)

  • {Sample_ID}_Expanded_5um_cell_information.csv - Detailed information about each cell region

  • {Sample_ID}_Expanded_5um_cell_counts.txt - Number of detected cell regions

Nuclei Segmentation Preview Files

The nuclei_segmentation_preview/ folder provides a quick visual QC of cell segmentation performance on your tissue. A diverse set of ROIs is automatically selected from across the tissue so you can easily assess how well the cell segmentation model performs on your sample without opening the full-resolution mask.

Files in nuclei_segmentation_preview/:

  • {Sample_ID}_tile_locations.png - Whole-tissue overview image with green boxes indicating the locations of the automatically selected ROI tiles.

  • {Sample_ID}_segmentation_preview.png - Grid figure showing each ROI image tile side-by-side with its nuclei contour overlay for quick visual QC.

  • preview_tiles/ - Subfolder containing the individual ROI tile images with nuclei contour overlays:

    • {Sample_ID}_tile_{N}_img_with_nuclei_contours.png - Individual ROI tile image with nuclei contour overlay (one file per selected ROI).

Important Notes:

  1. Coordinate Space: Contour coordinates are in substrate coordinate space, so they can be overlaid with spatial data (raw, 10 µm binned, and cell binned) without any further adjustment.

  2. Cell Region Definition: Cell regions are obtained by expanding detected nuclei regions by 5 µm.

  3. Overlaying Masks with Spatial Data: To overlay cell or nuclei masks with spatial data (raw, 10 µm binned, and cell binned), you must translate the coordinates in barcodes.tsv.gz from substrate coordinate space to the local coordinate space of the sample region. This can be done by subtracting the global shifts (found in the registered image metadata) from the substrate coordinates reported in barcodes.tsv.gz for each sample. See this page for more details about global and local coordinates

  4. Mask File Format: The TIFF mask files use pixel intensity values to encode cell IDs. Each unique non-zero intensity value represents a different cell or nucleus, with 0 representing the background.

Stats

Contains consolidated statistics for each sample, aggregating metrics from all pipeline modules.

Logs

Various logs output by the pipeline.

Summary

Aggregated tables and plots of the outputs of the pipeline.

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