Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses

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1. Version published to 10.1016/j.immuni.2021.03.002

   Apr 1, 2021
2. 

   ScreenIT

   Mar 1, 2021

   SciScore for 10.1101/2020.10.02.20205880: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Dataset collection and preprocessing: We downloaded 26 gene expression datasets from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), ArrayExpress, and European Nucleotide Archive (ENA), consisting of 4,780 samples across 34 independent cohorts derived from whole blood or peripheral blood mononuclear cells (PBMCs) (Table S1).	Gene Expression Omnibus suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012) ArrayExpress suggested: (ArrayExpress, RRID:SCR_002964)
   We mapped microarray probes in each dataset to Entrez Gene …

   SciScore for 10.1101/2020.10.02.20205880: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Dataset collection and preprocessing: We downloaded 26 gene expression datasets from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), Sequence Read Archive (SRA), ArrayExpress, and European Nucleotide Archive (ENA), consisting of 4,780 samples across 34 independent cohorts derived from whole blood or peripheral blood mononuclear cells (PBMCs) (Table S1).	Gene Expression Omnibus suggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012) ArrayExpress suggested: (ArrayExpress, RRID:SCR_002964)
   We mapped microarray probes in each dataset to Entrez Gene identifiers (IDs) to facilitate integrated analysis.	Entrez Gene suggested: (Entrez Gene, RRID:SCR_002473)
   Briefly, healthy controls from each cohort undergo ComBat co-normalization without covariates, and the ComBat estimated parameters are computed for healthy samples in each dataset.	ComBat suggested: (ComBat, RRID:SCR_010974)
   We assessed the quality of the raw reads with Trim Galore (v0.6.5), trimmed Illumina adaptors, and removed reads that were too short after adaptor trimming (less than 20 nt).	Trim Galore suggested: (Trim Galore, RRID:SCR_011847)
   We then mapped the cleaned reads to human genome sequences (hg38) using STAR (v2.7.3) (Dobin et al., 2013).	STAR suggested: (STAR, RRID:SCR_015899)
   We performed more quality control by checking the quality of the mapped reads in BAM format with Qualimap (v.2.2.2) (García-Alcalde et al., 2012).	Qualimap suggested: (QualiMap, RRID:SCR_001209)
   Finally, we applied the variance stabilizing transformation from DESeq2 (v1.26.0) (Love et al., 2014) to normalize gene expression for downstream analysis and visualization.	DESeq2 suggested: (DESeq, RRID:SCR_000154)
   We concatenated the list of viral sequences with the list of human transcriptome sequences and then built a decoy-aware index using Salmon.	Salmon suggested: (Salmon, RRID:SCR_017036)
   We also checked the reads with NCBI Nucleotide BLAST to ensure viral origin.	BLAST suggested: (BLASTX, RRID:SCR_001653)
   To investigate changes in the immune cell proportions between patients with different severity of viral infection, we conducted three multi-cohort analyses using MetaIntegrator R package (Haynes et al., 2017) between samples from the following categories: 1) subjects with non-severe viral infection (severity categories ‘mild’ and ‘moderate’) vs healthy controls, 2) subjects with severe viral infection (severity categories ‘serious’, ‘critical’, and ‘fatal’) vs healthy controls, and 3) subjects with severe viral infection vs subjects with non-severe viral infection (Table S3).	MetaIntegrator suggested: None
   Hierarchical clustering was calculated using hclust and Dist R functions with “euclidean” and “complete” parameters.	hclust suggested: (HCLUST, RRID:SCR_009154)

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   Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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   Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: We did not find any issues relating to colormaps.

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   Results from rtransparent:

   • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
   • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
   • No protocol registration statement was detected.

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3. 

   Version published to 10.1101/2020.10.02.20205880v1 on medRxiv

   Oct 5, 2020