Expected Output from NovaScope¶

Output Directory Structure¶

The directory passed through output paramter in the config_job.yaml will be organized as follows,

├── align
├── histology
├── match
├── seq1st
└── seq2nd

seq1st¶

The seq1st directory is structured for organizing 1st sequencing FASTQ files and spatial barcode maps. It includes:

A fastqs subdirectory for all input 1st sequencing FASTQ files via symlink.
Two subdirectories for spatial barcode maps:
- sbcds for maps of individual tiles from the 1st sequencing,
- nbcds for a map organized on a per-chip basis, used in later processing.

└── seq1st
    └── <flowcell_id>
        ├── fastqs
        |   └── <seq1st_id>.fastq.gz
        ├── nbcds
        |   └── <chip_id>
        |       ├── 1_1.sbcds.sorted.tsv.gz
        |       ├── 1_1.sbcds.sorted.png
        |       ├── dupstats.tsv.gz
        |       └── manifest.tsv
        └── sbcds
           └── <chip_id>
                └── ...    # spatial maps of individual tile, and a manifest file 

seq2nd¶

The seq2nd directory is dedicated to managing all input 2nd sequencing FASTQ files via symlinks. Each pair will be organized in one folder named by the 2nd sequencing ID provided via the job configuration file.

The following example demonstrates the directory structure using two pairs of input 2nd sequencing FASTQ files:

└── seq2nd
    ├── <seq2nd_id1>
    |   ├── <seq2nd_id1>.R1.fastq.gz
    |   └── <seq2nd_id1>.R2.fastq.gz
    └── <seq2nd_id2>
        ├── <seq2nd_id2>.R1.fastq.gz
        └── <seq2nd_id2>.R2.fastq.gz

match¶

The match directory houses the outcomes of aligning second sequencing reads with spatial barcodes for the corresponding chip section.

└── match
    └── <flowcell_id>
        └── <chip_id>
            └── <seq2nd_id1>
                ├── <seq2nd_id1>.R1.counts.tsv
                ├── <seq2nd_id1>.R1.match.png
                ├── <seq2nd_id1>.match.sorted.uniq.tsv.gz
                └── <seq2nd_id1>.summary.tsv

histology¶

The histology directory is designated for holding both the input histology file and the histology images aligned with the spatial coordinates of the SGE.

└── histology
    └── <flowcell_id>
        └── <chip_id>
            ├── raw
            |   └── ...     # a raw histology file
            └── aligned
                └── ...     # aligned histology files

align¶

The align directory encompasses several subdirectories, including:

bam for alignment outcomes such as the BAM file, summary metrics, and visualizations;
sge for a spatial gene expression matrix (SGE) and visualizations;

└── align
    └── <flowcell_id>
        └── <chip_id>
           └── <run_id>
                ├── bam
                |   └── ...     
               └── sge
                    └── ...     

analysis¶

The analysis directory includes three subdirectory mainly for the reformatting SGE:

sgeAR for the SGE before reformatting, where the "AR" stands for analysis-ready,
preprocess for the SGE in the FICTURE format,
segment for the hexagon-based SGE in the 10x genomics format.

└── analysis
    └── <run_id>
        └── <unit_id>
            ├── preprocess
            |   └── ...  
            ├── segment
            |   └── ...  
            └── sgeAR
                └── ...  

The sgeAR Subfolder and Manual Preprocess

The sgeAR subfolder is specifically designed to host input SGEs that require reformatting. This subfolder is particularly useful when users wish to manually preprocess SGEs, such as applying boundary filtering, before they undergo reformatting.

To manually preprocess an SGE:

Preprocess the SGE: Users must manually preprocess the SGE according to their specific requirements.
Name the dataset: After preprocessing, the dataset should be named and referred to as unit_id.
Save the preprocessed SGE: Place the manually preprocessed SGE in the sgeAR subfolder.
Preprare a coordinate meta file Prepare a barcodes.minmax.tsv with the minimum and maximum of X and Y coordinates in the sgeAR subfolder.
Update the job configuration file: Provide the unit_id in the job configuration file to ensure it is recognized in subsequent processing steps.

Automatic Handling: If reformatting features are requested without manually preparing the SGE in the sgeAR as outlined, NovaScope will automatically generate a unit_id. It will then link the original SGE from the sge subdirectory to the sgeAR, facilitating seamless processing.

Downstream Analysis¶

The aligned sequenced reads can be directly used for tasks that require read-level information, such as allele-specific expression or somatic variant analysis. The SGE can also be analyzed with many software tools, such as Latent Dirichlet Allocation (LDA) and Seurat.

An exemplary downstream analysis is provided at NovaScope-exemplary-downstream-analysis.