Multi-omics identify LRRC15 as a COVID-19 severity predictor and persistent pro-thrombotic signals in convalescence

<p>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.</p> <p>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.</p> <p>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>).</p> <p>The features in the clinical metadata include:</p> <table> <thead> <tr> <th>Column Name</th> <th>Data Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>sample_id</td> <td>Character</td> <td>Unique identifier for samples</td> </tr> <tr> <td>individual_id</td> <td>Character</td> <td>Unique identifier for individuals</td> </tr> <tr> <td>ethnicity</td> <td>Character</td> <td>The individual&#39;s ethnicity (asian, white, black or other)</td> </tr> <tr> <td>sex</td> <td>Character</td> <td>The individual&#39;s sex (M or F)</td> </tr> <tr> <td>calc_age</td> <td>Integer</td> <td>Age in years</td> </tr> <tr> <td>ihd</td> <td>Character</td> <td>Information on coronary heart disease</td> </tr> <tr> <td>previous_vte</td> <td>Character</td> <td>Whether individuals have had venous thromboembolism</td> </tr> <tr> <td>copd</td> <td>Character</td> <td>Whether individuals have chronic obstructive pulmonary disease</td> </tr> <tr> <td>diabetes</td> <td>Character</td> <td>Whether individuals have diabetes, and, if so, the type of diabetes</td> </tr> <tr> <td>smoking</td> <td>Character</td> <td>Smoking status</td> </tr> <tr> <td>cause_eskd</td> <td>Character</td> <td>Cause of ESKD</td> </tr> <tr> <td>WHO_severity</td> <td>Character</td> <td>The peak (WHO) severity for the patient over the disease course</td> </tr> <tr> <td>WHO_temp_severity</td> <td>Character</td> <td>The (WHO) severity at time of sampling</td> </tr> <tr> <td>fatal_disease</td> <td>Logical</td> <td>Whether the disease was fatal</td> </tr> <tr> <td>case_control</td> <td>Character</td> <td>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></td> </tr> <tr> <td>radiology_evidence_covid</td> <td>Character</td> <td>Evidence of COVID-19 from radiology</td> </tr> <tr> <td>time_from_first_symptoms</td> <td>Integer</td> <td>The number of days since the individual first experienced COVID symptoms at time of sampling</td> </tr> <tr> <td>time_from_first_positive_swab</td> <td>Integer</td> <td>The number of days since the individual&#39;s first positive swab was taken at time of sampling</td> </tr> </tbody> </table>