Transcript-Based Classification Pipeline

• Transcript-based classification pipelines are computational frameworks that transform raw transcript data into structured, unbiased labels for downstream analysis.
• They integrate spatial transcriptomics, high-dimensional clustering, and guided patch extraction to map transcript signals to morphological categories.
• These pipelines use CNN architectures with data augmentation and early stopping to efficiently bridge genotype-phenotype mapping and ensure model robustness.

A transcript-based classification pipeline is a computational system designed to perform supervised or unsupervised learning tasks in which input features are derived directly from transcript data. This class of pipelines has gained traction in biomedical imaging, natural language processing, and computational genomics, providing a method for mapping raw transcript information—whether spatially, temporally, or semantically resolved—into discrete, actionable categories. Recent developments exploit transcriptome-derived labels, spatial transcriptomic contexts, and learned representations to minimize manual annotation and support unbiased, scalable genotype-phenotype mapping.

1. Integration of Transcript Information as Supervisory Signal

Transcript-based classification pipelines often repurpose molecular or semantic transcript data as supervisory signals for the training of classifiers, rather than relying on human-curated labels. A prominent approach uses spatially resolved gene expression maps to define ground truth: spatial transcriptomics data, such as in situ sequencing (ISS), yields spatially localized expression patterns of selected marker genes. These patterns are subjected to high-dimensional clustering and dimensionality reduction to yield region or compartment masks, each representing areas of the tissue with similar transcriptomic signatures.

This integration replaces expert annotations with transcriptome-derived ground truths, reducing manual bias and enabling fully automated, data-driven region identification. The process involves extracting transcriptomic coordinates from fluorescence microscopy images (e.g., mouse brain sections with ISS for 82 gene markers), clustering gene expression data to define compartments, and label classes (e.g., Neurod6-dominated, Enpp2-dominated, foreground, and background) for the subsequent machine learning workflow.

2. Patch Extraction and Class Balancing

Spatially guided patch extraction is a key step in linking transcriptomic labels to image-based classifiers. Image patches are systematically cropped around transcriptomic marker locations associated with defined regions. To explore the influence of local context, patch sizes typically span $64 \times 64$ to $1024 \times 1024$ pixels, but all are resized via bilinear interpolation to a fixed input size (e.g., $256 \times 256$) for computational uniformity.

Class definition in such pipelines is dictated by transcriptomic clustering outcomes. For instance, patches are labeled as:

1. Morphology A—localized to Neurod6-dominated regions,
2. Morphology B—localized to Enpp2-dominated regions,
3. Foreground—other tissue sections, and
4. Background—regions outside tissue with no detected nuclei.

Selection and balancing protocols may involve random sampling for less abundant regions (foreground/background) to correct class imbalance.

3. Model Architecture and Data Augmentation

The convolutional neural network (CNN) is the typical backbone of such pipelines, leveraging spatial hierarchies in morphological or image data. A standard architecture includes three convolutional layers (each followed by max-pooling), two fully connected layers, and a softmax output layer for multi-class prediction. Rectified Linear Unit (ReLU) activations are used between layers. The pipeline is implemented in frameworks such as MATLAB Deep Learning Toolbox, but the architecture is compatible with any modern deep learning environment.

To increase generalization and counteract limited data, data augmentation strategies are essential. Transformations include random horizontal/vertical flips and arbitrary rotations within $[0, 360^\circ]$ applied to patches prior to input.

4. Training Protocol and Evaluation

Model training is performed using the ADAM optimizer with early stopping: the process is halted once the loss does not decrease for two consecutive epochs on a separate test set. Sliding window inference is used to generate dense class predictions across the entire test image. A stride (e.g., 32 pixels) is selected for computational efficiency, and resultant prediction maps are upscaled to match the original image resolution using nearest-neighbor interpolation.

Performance assessment utilizes metrics appropriate for multi-class segmentation, most notably the Sørensen-Dice Similarity Coefficient (SDC):

$$SDC^{(k)} = \frac{2|I^{(k)} \cap \tilde{I}^{(k)}|}{|I^{(k)}| + |\tilde{I}^{(k)}|}$$

where $I^{(k)}$ and $\tilde{I}^{(k)}$ are the ground-truth and predicted binary masks for class $k$, respectively.

For models trained using spatial transcriptomic ground truths, reported performance often reflects moderate Dice scores (e.g., 0.51), with improvements observed for larger context-rich patches.

5. Unbiased Genotype-Phenotype Mapping and Scalability

Pipelines leveraging transcriptomics for supervision facilitate objective genotype-phenotype mapping that is less susceptible to manual biases. The use of spatially resolved transcripts allows for automated, reproducible region annotation, expanding the possible scale of downstream studies. Larger contextual patches improve discriminatory power, highlighting the contribution of the cellular environment and microarchitecture to morphological classification.

A plausible implication is that further partitioning of transcriptomic regions can allow more granular morphological classification without the need for additional manual intervention. As transcriptomics technologies scale and spatial resolution improves, pipelines of this type become proportionally more powerful and generalizable.

6. Generalization to Broader Domains and Modularity

Although transcript-based classification pipelines are exemplified by spatial transcriptomics in tissue imaging, the foundational methodology—substituting manual annotations with direct transcriptomic or semantic context—extends to natural language applications, functional genomics, and unsupervised information extraction tasks. Pipelines may incorporate transcriptomic labels in entirely data-driven ways regardless of the input modality (e.g., images, time series, audio). The modular design, consisting of (1) transcript-based region discovery, (2) context-appropriate feature or patch extraction, and (3) supervised classification, underlies broad generalizability.

Pipeline performance and scalability are functions of the quality and resolution of transcriptomic data, the discriminatory power of the selected features or image patches, and the depth and regularization of the learning model. The methodology is robust to the addition of new data types, the introduction of higher hierarchical label structures, and advances in both transcriptomic and machine learning technologies.

7. Summary Table: Core Elements of a Transcript-Based Classification Pipeline

Stage	Key Methods	Function
Transcript region annotation	ISS, dimensionality reduction, clustering	Define unbiased, data-driven region labels
Patch/feature extraction	Spatially guided cropping, context windowing	Map transcriptomic signal to classifier input
Model training	CNNs (3 conv + 3 pool + 2 FC + softmax), ADAM	Learn morphological or semantic classes
Augmentation	Rotation, flipping, sampling	Increase robustness and balance
Evaluation	Sliding window inference, Dice/SDC coefficient	Class prediction, segmentation accuracy

This pipeline structure is directly supported by recent advances utilizing spatial transcriptomics to automate supervision for image-based morphology classification, obviating the need for severely labor-intensive expert annotation and enabling more scalable, objective, and fine-grained genotype-to-phenotype relationship discovery (Andersson et al., 2023).