This repository contains the data used in "Spatial transcriptomics reveals tumor microenvironment–driven subtypes of invasive lobular carcinoma", Serra M. et al., providing spatial transcriptomics (ST) results from multiple samples, along with morphological annotations and high-resolution histology images. The dataset includes processed R objects, raw and normalized expression matrices, spatial metadata, and high-quality annotations to facilitate further analysis and reproducibility. Repository Contents 1. STutility_object.R This file is an R object containing: Filtered expression matrices (both raw and normalized) for all spatial transcriptomics (ST) samples. Hematoxylin & Eosin (H&E) images corresponding to the analyzed ST samples. Metadata, including: Morphological annotation composition. CARD single-cell deconvolution data, providing insights into cellular composition at the spot level. The orig.ident column serves as the identifier for each ST sample (e.g., 1 corresponds to ST1, 3 corresponds to ST3, etc.). 2. spaceRanger_output.zip This ZIP archive contains part of the SpaceRanger output for each individual sample, including: filtered_feature_bc_matrix.h5: The count matrix used as input for downstream analysis. spatial/ folder, which includes: Low- and high-resolution H&E images for spatial reference. Scalefactor files, necessary for spatial mapping. Files containing spatial coordinates of the spots within the tissue section. Morphological annotation data, defined at the spot level. 3. MORPHOLOGICAL_ANNOTATIONS.zip This folder contains high-quality PNG files exported from QuPath, representing morphological annotationscorresponding to the ST samples' H&E images. These annotations provide detailed spatial insights into tissue structure. 4. NDPI_H&E_IMAGES.zip This ZIP archive includes high-resolution whole-slide H&E images for the ST samples, allowing detailed visualization of tissue morphology and histopathological features. 4. DEG_ILC_subtypes.zip This ZIP archive includes differentially expressed genes for the each ILC4TME subtypes (with relative fc, log2FC and statistics) in our ST cohort. Usage and Reproducibility This dataset provides essential resources for spatial transcriptomics analysis, integrating gene expression, histology, and morphological annotations. It is intended for computational and experimental researchers interested in spatial gene expression patterns, tumor microenvironment studies, and histopathology-driven analyses. Original Scripts The original scripts used in this publication are available on GitHub: https://github.com/BCTL-Bordet/ILC-Spatial-Transcriptomics For further details on the methodology and analysis, please refer to our publication: M. Serra, M. Rediti, L. Collet, F. Lifrange, D. Venet, N. Occelli, A. Papagiannis, D. Vincent, G. Rouas, D. Larsimont, M. Vikkula, F.P. Duhoux, F. Rothé, & C. Sotiriou, Spatial transcriptomics reveals tumor microenvironment–driven subtypes of invasive lobular carcinoma, Proc. Natl. Acad. Sci. U.S.A. 123 (6) e2517567123, https://doi.org/10.1073/pnas.2517567123 (2026).