DOI: 10.5281/zenodo.7760264
title: Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets

description: You can find here the datasets used in the publication: 

Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4 

This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: 

https://github.com/alberto-valdeolivas/ST_CRC_CMS

In particular, you will find here several zip compressed files with the following content: 

- Intermediary_FileObjects.zip: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. 

-  IntermediaryFiles_ST_CRC_LiverMetastasis.zip: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. 

- Pathology_SpotAnnotations.zip: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) 

-SN048_A121573_Rep1.zip, SN048_A121573_Rep2.zip, SN048_A416371_Rep1.zip, SN048_A416371_Rep2.zip, SN123_A551763_Rep1.zip, SN123_A595688_Rep1.zip, SN123_A798015_Rep1.zip, SN123_A938797_Rep1_X.zip, SN124_A551763_Rep2.zip, SN124_A595688_Rep2.zip, SN124_A798015_Rep2.zip, SN124_A938797_Rep2.zip, SN84_A120838_Rep1.zip, SN84_A120838_Rep2.zip: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study

- DeconvolutionResults_ST_CRC_BelgianCohort.zip, DeconvolutionResults_ST_CRC_KoreanCohort.zip, DeconvolutionResults_ST_CRC_LiverMetastasis.zip: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)

 

- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: 

Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ... (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), Visium Frozen_SN V19S23-084.ndpi (samples A120838_Rep1 and A120838_Rep2), Visium Frozen_SN V19S23-123.ndpi (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and Visium Frozen_SN V19S23-124.ndpi (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)

- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: 

IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: 



Sample S1_Cec (A551763)

Sample S2_Col_R (A595688)

Sample S3_Col_R (A416371) 

Sample S4_Col_Sig (A120838)

Sample S5_Rec (A121573)

Sample S6_Rec (A938797)

Sample S7_Rec/Sig (A798015)