DOI: 10.5061/dryad.4j0zpc8mm
title: Data from: Biomarker detection and validation for corneal involvement in patients with acute infectious conjunctivitis: A multi-country study

description: This dataset accompanies the study “Biomarker Detection and Validation for
 Corneal Involvement in Patients With Acute Infectious Conjunctivitis,”
 published in JAMA Ophthalmology (doi:10.1001/jamaophthalmol.2024.2891).
 The study utilized transcriptomic data and machine learning approaches to
 identify biomarkers associated with corneal involvement in conjunctivitis
 patients, with apolipoprotein E (APOE) emerging as a key biomarker. The
 dataset includes raw transcriptomic counts, sample metadata, and gene
 mapping files, enabling replication and further exploration of the
 findings. Ethical considerations have been addressed, with all patient
 data anonymized and deidentified to protect privacy.
description: # Data from: Biomarker detection and validation for corneal involvement in
 patients with acute infectious conjunctivitis: A multi-country study
 [https://doi.org/10.5061/dryad.4j0zpc8mm](https://doi.org/10.5061/dryad.4j0zpc8mm) This dataset contains the gene count data used for finding biomarkers predicting corneal involvement in patients with acute infectious conjuntivitis, as described in the paper: Seitzman GD, Prajna L, Prajna NV, et al. Biomarker Detection and Validation for Corneal Involvement in Patients With Acute Infectious Conjunctivitis. *JAMA Ophthalmol.* 2024;142(9):865–871. doi:10.1001/jamaophthalmol.2024.2891 ## List of files ``` biomarker_detection_count_data_md5-9b4b21f9.csv biomarker_detection_sample_table_md5-7bd7fef2.csv ENSG_ID2Name.txt.zip ``` ## Description of the data and file structure Below is a brief description of each data file. ### `biomarker_detection_count_data_DESeq2norm_md5-9b4b21f9.csv` The CSV file `biomarker_detection_count_data_DESeq2norm_md5-9b4b21f9.csv` contains counts for human genes found in 58 conjunctival samples used in the study. RNA-Seq data was generated on an Illumina NovaSeq 6000 sequencing machine at the UCSF sequencing center. Sequencing reads were quality filtered using PriceSeqFilter, and aligned to the GRCh38 human genome assembly using HISAT2 (version 2.1.0). Abundance of genes was calculated using the default parameters in stringtie2 (version 1.3.4d). Annotation of transcripts was based on ENSEMBL GRCh38.87. The attached gene count matrix was then generated using the "prepDE.py" script according to the protocol found in the *stringtie2* documentation. * **Rows:** counts for 58,302 genes (identified by ENSG gene_id) * **Columns:** one for each of 58 samples ### `biomarker_detection_sample_table_md5-7bd7fef2.csv` The CSV file `biomarker_detection_sample_table_md5-7bd7fef2.csv` contains metadata about the samples used, including the number of input reads for normalzing the counts to reads/million. It contains the following columns: * Sample: unique, anonymized ID for each sample * N_reads: number of sequencing read pairs * Country: country of origin for each sample * DESEq: part of training set used for DESeq2 dimensionality reduction * Machine Learning: included in ML (all true, redundant column) * RT-qPCR Validation: Real-time quantitative PCR was performed on this sample (Yes / No) * Corneal Involvement: corneal involvement clinically detected (1 yes, 0 no) * Sex: sex of patient * Age: age of patient in years ## ENSG_ID2Name.txt.zip `ENSG_ID2Name.txt.zip` is a zip-compressed text file containing the mapping of ENSG IDs to gene names as they were used in the study. ## Code/Software Software used for determining gene counts: * PRICE Sequence Filter (version 1.2) * HISAT2 (version 2.1.0) * stringtie2 (version 1.3.4d), and stringtie2's `prepDE.py` Python script