DOI: 10.5281/zenodo.7410194
title: Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence

description: RNA sequencing, SomaLogic proteomics and flow cytometry data were generated for two cohorts of end-stage kidney disease patients with COVID-19. The Wave 1 cohort consists of samples collected from patients during the first wave of COVID-19 in early 2020, while samples were collected for the Wave 2 cohort in the following year. This data deposition includes the RNA-seq counts, SomaScan proteomics, flow cytometry and clinical metadata associated with the study. For further information about the study and data, see the associated GitHub repository (https://github.com/jackgisby/covid-longitudinal-multi-omics) or our pre-print (https://doi.org/10.1101/2022.04.29.22274267). The repository also contains code to replicate our analysis of the data. The raw RNA-seq reads were processed using the nf-core RNA-seq v3.2 pipeline before htseq-count was used to generate a raw counts matrix, which is included in this deposition (<code>htseq_counts.csv</code>). Three files make up the proteomics data: <code>sample_technical_meta.csv</code>, <code>feature_meta.csv</code> and <code>soma_abundance.csv</code>. The first two files contain metadata columns for the samples and protein features, respectively. The final file includes the unprocessed protein abundance data. The files <code>general_panel.csv</code> and <code>t_cell_panel.csv</code> contain the flow cytometry data, split into the general and T-cell panels, respectively. Finally, clinical metadata is available for the two cohorts described in this study (<code>w1_metadata.csv</code>, <code>w2_metadata.csv</code>). The features in the clinical metadata include: Column Name Data Type Description sample_id Character Unique identifier for samples individual_id Character Unique identifier for individuals ethnicity Character The individual's ethnicity (asian, white, black or other) sex Character The individual's sex (M or F) calc_age Integer Age in years ihd Character Information on coronary heart disease previous_vte Character Whether individuals have had venous thromboembolism copd Character Whether individuals have chronic obstructive pulmonary disease diabetes Character Whether individuals have diabetes, and, if so, the type of diabetes smoking Character Smoking status cause_eskd Character Cause of ESKD WHO_severity Character The peak (WHO) severity for the patient over the disease course WHO_temp_severity Character The (WHO) severity at time of sampling fatal_disease Logical Whether the disease was fatal case_control Character Whether the individual was COVID-19 <code>POSITIVE</code> or <code>NEGATIVE</code> at time of sampling. Convalescent patients are denoted by the label <code>RECOVERY</code> radiology_evidence_covid Character Evidence of COVID-19 from radiology time_from_first_symptoms Integer The number of days since the individual first experienced COVID symptoms at time of sampling time_from_first_positive_swab Integer The number of days since the individual's first positive swab was taken at time of sampling
description: This research was partly funded by Community Jameel and the Imperial President's Excellence Fund and by a UKRI-DHSC COVID-19 Rapid Response Rolling Call (MR/V027638/1) (to JEP), and by funding from UKRI/NIHR through the UK Coronavirus Immunology Consortium (UK-CIC) (to MB).