key: cord-0427504-2t4j7bvg
authors: Schimke, Lena F.; Marques, Alexandre H.C.; Baiocchi, Gabriela Crispim; de Souza Prado, Caroline Aliane; Fonseca, Dennyson Leandro M.; Freire, Paula Paccielli; Plaça, Desirée Rodrigues; Filgueiras, Igor Salerno; Salgado, Ranieri Coelho; Jansen-Marques, Gabriel; Oliveira, Antonio Edson Rocha; Peron, Jean Pierre Schatzmann; Barbuto, José Alexandre Marzagão; Camara, Niels Olsen Saraiva; Calich, Vera Lúcia Garcia; Ochs, Hans D.; Condino-Neto, Antonio; Overmyer, Katherine A.; Coon, Joshua J.; Balnis, Joseph; Jaitovich, Ariel; Schulte-Schrepping, Jonas; Ulas, Thomas; Schultze, Joachim L.; Nakaya, Helder I.; Jurisica, Igor; Cabral-Marques, Otavio
title: Multi-layered transcriptomic analyses reveal an immunological overlap between COVID-19 and hemophagocytic lymphohistiocytosis associated with disease severity
date: 2021-08-01
journal: bioRxiv
DOI: 10.1101/2021.07.30.454529
sha: c1c7be67f8da9ee787a8c9d2bd9ff4339e5c55f4
doc_id: 427504
cord_uid: 2t4j7bvg

Clinical and hyperinflammatory overlap between COVID-19 and hemophagocytic lymphohistiocytosis (HLH) has been reported. However, the underlying mechanisms are unclear. Here we show that COVID-19 and HLH have an overlap of signaling pathways and gene signatures commonly dysregulated, which were defined by investigating the transcriptomes of 1253 subjects (controls, COVID-19, and HLH patients) using microarray, bulk RNA-sequencing (RNAseq), and single-cell RNAseq (scRNAseq). COVID-19 and HLH share pathways involved in cytokine and chemokine signaling as well as neutrophil-mediated immune responses that associate with COVID-19 severity. These genes are dysregulated at protein level across several COVID-19 studies and form an interconnected network with differentially expressed plasma proteins which converge to neutrophil hyperactivation in COVID-19 patients admitted to the intensive care unit. scRNAseq analysis indicated that these genes are specifically upregulated across different leukocyte populations, including lymphocyte subsets and immature neutrophils. Artificial intelligence modeling confirmed the strong association of these genes with COVID-19 severity. Thus, our work indicates putative therapeutic pathways for intervention.

More than one year of Coronavirus disease 2019 pandemic caused by the severe acute respiratory syndrome Coronavirus (SARS-CoV)-2, more than 197 million cases and 4,2 million deaths have been reported worldwide (July 30 th 2021, WHO COVID-19 Dashboard). The clinical presentation ranges from asymptomatic to severe disease manifesting as pneumonia, acute respiratory distress syndrome (ARDS), and a life-threatening hyperinflammatory syndrome associated with excessive cytokine release (hypercytokinaemia) [1] [2] [3] . Risk factors for severe manifestation and higher mortality include old age as well as hypertension, obesity, and diabetes 4 . Currently, COVID-19 continues to spread, new variants of SARS-CoV-2 have been reported and the number of infections resulting in death of young individuals with no comorbidities has increased the mortality rates among the young population 5, 6 . In addition, some novel SARS-CoV-2 variants of concern appear to escape neutralization by vaccine-induced humoral immunity 7 . Thus, the need for a better understanding of the immunopathologic mechanisms associated with severe SARS-CoV-2 infection.

Patients with severe COVID-19 have systemically dysregulated innate and adaptive immune responses, which are reflected in elevated plasma levels of numerous cytokines and chemokines including granulocyte colony-stimulating factor (GM-CSF), tumor necrosis factor (TNF), interleukin (IL)-6, IL-6R, IL18, CC chemokine ligand 2 (CCL2) and CXC chemokine ligand 10 (CXCL10) [8] [9] [10] , and hyperactivation of lymphoid and myeloid cells 11 . Notably, the hyperinflammation in COVID-19 shares similarities with cytokine storm syndromes such as those triggered by sepsis, autoinflammatory disorders, metabolic conditions and malignancies 12-14 , often resembling a hematopathologic condition called hemophagocytic lymphohistiocytosis (HLH) 15 . HLH is a life-threatening progressive systemic hyperinflammatory disorder characterized by multi-organ involvement, fever flares, hepatosplenomegaly, and cytopenia due to hemophagocytic activity in the bone marrow [15] [16] [17] or within peripheral lymphoid organs such as pulmonary lymph nodes and spleen. HLH is marked by aberrant activation of B and T lymphocytes and monocytes/macrophages, coagulopathy, hypotension, and ARDS. Recently, neutrophil hyperactivation has been shown to also play a critical role in HLH development 18, 19 . This is in agreement with the observation that the HLH-like phenotype observed in severe COVID-19 patients is due to an innate neutrophilic hyperinflammatory response associated with virus-induced hypercytokinaemia which is dominant in patients with an unfavorable clinical course 17 . Thus, HLH has been proposed as an underlying etiologic factor of severe COVID- 19 1, 3, 20 . HLH usually develops during the acute phase of COVID-19 1, [20] [21] [22] [23] [24] [25] [26] [27] . However, a case of HLH that occurred two weeks after recovery from COVID-19 has recently been reported as the cause of death during post-acute COVID-19 syndrome 28 .

The familial form of HLH (fHLH) is caused by inborn errors of immunity (IEI) in different genes encoding proteins involved in granule-dependent cytotoxic activity of leukocytes such as AP3B1, LYST, PRF1, RAB27A, STXBP2, STX11, UNC13D [29] [30] [31] . In contrast, the secondary form (sHLH) usually manifests in adults following a viral infection (e.g., adenovirus, EBV, enterovirus, hepatitis viruses, parvovirus B19, and HIV) 32, 33 , or in association with autoimmune/rheumatologic, malignant, or metabolic conditions that lead to defects in T/NK cell functions and excessive inflammation 16, 31 . fHLH and sHLH affect both children and adults, however, the clinical and genetic distinction of HLH forms is not clear since immunocompetent children can develop sHLH 34, 35 , while adult patients with sHLH may also have germline mutations in HLH genes 36 . Of note, germline variants in UNC13D and AP3B1 have also been identified in some COVID-19 patients with HLH phenotype 37 , thus, indicating that both HLH forms may be associated with COVID-19.

Here, we characterized the signaling pathways and gene signatures commonly dysregulated in both COVID-19 and HLH patients by investigating the transcriptomes of 1253 subjects (controls, COVID-19, and HLH patients) assessed by microarray, bulk RNA-sequencing (RNAseq), and single-cell RNAseq (scRNAseq) ( Table 1) . We found shared gene signatures and cellular signaling pathways involved in cytokine and chemokine signaling as well as neutrophilmediated immune responses that associate with COVID-19 severity.

Considering the fact that COVID-19 and HLH 14, 21 share clinical and biological features ( Figure 1A) we suspected that these diseases have a transcriptional overlap underlying their phenotypic convergence. First, we obtained differentially expressed genes (DEGs) for each dataset from different high-throughput transcriptome technologies present in peripheral blood lymphocytes (PBLs), peripheral blood mononuclear cells (PBMCs), and nasopharyngeal swabs from COVID-19

patients, HLH patients and controls (Figure 1B and Supp. Figure 1A and 1B, and Supp. Table S2 and S3). We found a total of 239 unique common DEGs between HLH and all COVID-19 datasets, most of them (237 DEGs) up-regulated ( Figure 1D) . Hereafter, we focused on the implications of the up-regulated genes, since the 2 common down-regulated genes (granulysin or GNLY; myomesin 2 or MYOM2) alone did not enrich any significant pathway.

However, this might also indicate a defect in cytotoxicity activity, typical of HLH 31 , that will require future investigation. The 237 common up-regulated DEGs encode proteins mainly involved in immune system, metabolic and signaling processes, forming a highly connected biological network based on physical protein-protein interactions (PPI, Figure 1E ). Among them are important genes encoding molecules involved in activation of inflammatory immune responses (e.g., PGLYRP1, OLR1, FFAR2), cytokine and chemokine mediated immune pathways (e.g., IL1R2, CXCR2, CXCR8, CCL4, CCL2), and neutrophil activation (e.g., CD177, MPO, ELANE). Of note, the transcriptional overlap between HLH and COVID-19 contains several molecules interacting with 7 genes causing fHLH due to IEI which itself were not among our DEGs ( Figure   1E ).

We next dissected the biological functions enriched by the 237 common up-regulated DEGs between COVID-19 and HLH patients by performing enrichment analysis of biological processes (BPs) and cellular components (CCs) by these 237 DEGs. The top 20 most enriched BPs are demonstrated in Figure 2A , which encompass cytokine/chemotaxis and neutrophil-mediated innate immune responses, ranging from response to IL-1 to neutrophil activation, degranulation, and migration (for all BPs see Supp. Table S4 ). The CCs enriched ( Figure 2B) include several compartments such as secretory granule lumen and membrane, azurophil tertiary and specific granules, as well as collagen-containing extracellular matrix, phagocytic vesicle, and primary lysosome (Supp. [50] [51] [52] . However, these genes also enrich several CCs (secretory vesicles, azurophilic granules or specific granules) and BPs (neutrophil degranulation) involved in the neutrophil immune responses (Supp. Figure 2 ). This result is in agreement with the role of these genes in a variety of neutrophil functions such as degranulation and formation of neutrophil extracellular traps (NET) [53] [54] [55] [56] [57] . Altogether, these data indicate gene signatures and signaling pathways commonly dysregulated in COVID-19 and HLH patients that involve a network of regulatory (production of chemokines/cytokines by T cells and macrophages) and effector (neutrophil hyperactivation, NK and T cell cytotoxicity) immune functions.

We next analyzed the relationship pattern and degree between the transcriptional signatures related to cytokine signaling and chemotaxis with those involved in neutrophil-mediated immune responses. We chose the COVID-19_PBL dataset from Overmyer et al. 58 , which contains transcripts from 100 individuals with COVID-19 and 26 individuals with respiratory symptoms but negative for COVID-19 serving as control group (further explored in the next session). We performed canonical-correlation analysis (CCA), which is a multivariate statistical method to determine the linear relationship between two groups of variables 59 Figure 3A and 3B) . Bivariate correlation analysis showed a similar phenomenon (Supp. Figure 3) . However, these correlation patterns partially changed when comparing COVID-19 with the control group. For instance, while reducing the correlation between molecules including IL-10, CXCL8, NFKB1, ARG1, and SOD2, new strong associations appeared between ELANE, DEFA4, AZU1, CTSG, and LCN2, with an overall tendency to higher relationships amid neutrophil-mediated immunity related genes in COVID-19 patients. Figure 3C illustrates this observation by scatter plots for some of these variables.

Transcripts stratifying severe COVID-19 from other respiratory diseases are also highly dysregulated in HLH Next, we sought to determine which genes of chemotaxis/cytokine signaling and neutrophilmediated immune responses discriminate COVID-19 patients according to disease severity. We . Table S9 ). These 25 genes were also present and upregulated in peripheral blood leukocytes of COVID-19 patients from two recently published works, which were unpublished when we started our investigation (GSE163151 61 and GSE152641 62 , Supp Table S10 ). Of note, most of these 25 genes have also been identified at protein level as dysregulated in COVID-19 patients across different studies (published during the development of our study; Supp. Table S11 ). In addition, these 25 genes might belong to a systemic immune network of molecules induced by SARS-CoV-2 since they are also highly interconnected with 158 proteins (Supp . Table 12 ) significantly dysregulated in the plasma of COVID-19_ICU when compared to COVID-19_nonICU patients. Thus, they show several interactions and functional overlap ( Figure 4B ) with plasma proteins involved in neutrophil degranulation and neutrophil-mediated immunity (Supp. Figure 4) .

To investigate the stratification power of these 25 DEGs, we performed principal component analysis (PCA) using a spectral decomposition approach 63, 64 , which examines the covariances/correlations between variables. This approach revealed that these DEGs clearly divide COVID-19_ICU, COVID-19_nonICU, Control_ICU and Control_nonICU (due to other respiratory illness but negative for SARS-CoV-2) groups ( Figure 4C and Supp. Figure 5A and 5C).

Likewise, these 25 genes stratified HLH patients from healthy controls (Figure 4D and Supp. Figure 4E and Figure 4F ).

Combined, these results suggest that these genes are associated with COVID-19 severity and highlight the importance of neutrophil-mediated immunity related molecules in severe COVID-19 on both transcriptomic and proteomic level.

Since scRNAseq allows comparison of the transcriptomes of individual cells, we next sought to investigate the distribution patterns of these 25 genes associated with COVID19 severity. We analyzed the scRNAseq dataset (EGAS00001004571) reported by Schulte-Schrepping et al. 65 (schematic overview of study group Figure 5A ) and found that 21 of the 25 genes associated with COVID-19 severity and HLH development are DEGs among the top 2,000 variable genes in the COVID-19 cohort compared to controls ( Figure 5B and Supp. Fig 6A and B) . These 21 genes exhibited cell-type-specific expression patterns. For instance, CCL4 (a chemoattractant and stimulator of T-cell immune responses 66, 67 ) was mainly produced by CD8+ T and NK cells, CD83

(B, T and dendritic cell activation marker 68, 69 ) by B cells and monocytes. CXCL8 was mostly present in monocytes and low-density neutrophils/granulocytes (LDGs; also frequently reported as immature neutrophils [70] [71] [72] , which are neutrophils remaining in the PBMC fraction after density gradient separation. Among these 21 genes, 11 genes (among them also the 8 genes described above) were differentially expressed when comparing patients with mild and severe COVID-19 ( Figure 5C) . Thus, indicating a network of cell-type-specific expression patterns that may contribute to the clinical similarities between COVID-19 and HLH.

Of note, these 11 genes encode proteins that are crucial for several pathways involved in neutrophil-mediated immunity, and are associated with diseases that increase the risk of severe COVID-19 73, 74 such as chronic obstructive pulmonary disease (COPD) 75, 76 and ulcerative colitis 77,78 (Supp. Figure 6E and Supp. Table S14 ). Similarly, these 11 genes are also significantly different between COVID-19_ICU and COVID-19_nonICU ( Figure 5E ) in the bulk RNAseq dataset (GSE157103, Overmyer et al. 2020 58 ) . Indicating that these genes are consistently associated with COVID-19 severity across different patient cohorts. Moreover, these 11 genes were differentially expressed in HLH patients compared to healthy controls ( Figure 5D ), reinforcing the transcriptomic overlap of severe COVID-19 and HLH.

We used random forest method 79 to rank the importance of these 11 genes based on their ability to discriminate between COVID-19_ICU and COVID-19_nonICU, hence evaluating the association of these genes with the COVID-19 severity. This approach showed error rate (out of bag or OOB) of 27,03% and an area under the ROC curve of 82,4% for both groups (Figure 6A and 6B). Follow-up analysis indicated that ARG1 was the most significant predictor for ICU admission followed by CD177, MCEMP1, LCN2, AZU1, OLFM4, MMP8, ELANE, CTSG, DEFA4, CEACAM8 based on the number of the nodes, gini-decrease, and average depth criteria for measuring gene importance ( Figure 6C and 6D) . ARG1 exhibited the most relevant interactions with the other genes according to the mean minimal depth criterion, mostly interacting with CD177, AZU1, MCEMP1, and LCN2 ( Figure 6D) . Altogether, these multi-layered transcriptomic results associate COVID-19 and HLH common genes with disease severity.

The results of our study provide a COVID-19/HLH immune landscape using a multi-layered transcriptomic approach of microarray, bulk and scRNAseq. Our meta-analysis integrates and unravels the consistency of several important individual studies and datasets that also validated the transcriptome data at the protein level in COVID-19 patients 58, 65, 80 . To the best of our knowledge, it represents the first attempt to systems characterize the common signaling pathways and molecular networks shared by COVID-19 and HLH. In agreement with the recent observation that neutrophil hyperactivation plays a key role in the severity of COVID-19 [81] [82] [83] [84] and HLH 18, 19 , our approach indicates that COVID-19 and HLH have a common transcriptional profile formed predominantly by a group of regulatory molecules related to cytokine/chemotaxis and by a group of effector molecules that are linked to neutrophil hyperactivation and disease severity. These data highlight the dual role of neutrophils in providing essential antimicrobial functions, but also initiating tissue injury caused by immune dysregulation 85, 86 . Ackermann et al. 84 . As we were able to demonstrate by the multi-omics association between leukocyte and plasma molecules, flow cytometry and proteomic data indicate a systemic and integrated network of molecules associated with neutrophil growth, activation, and mobilization leading to neutrophil dysregulation in severe COVID-19 82, 83 . This supports the concept that the pathophysiology of HLH does not only involve T cell, NK cell and macrophage dysregulation but also the hyperactivation of neutrophils. In accordance with our findings, it has recently been demonstrated that neutrophils accumulate in inflamed tissues of HLH and COVID-does not return to a homeostatic level due to an ineffective T cytotoxic response 18, 19 . Thus, maintaining the immunogenic stimulus for more cytokine/chemokine secretion, which promotes the sustained neutrophil recruitment and consequent tissue damage.

The use of high-throughput techniques to identify biomarkers, molecular pathways and pathophysiological information derived from genetic and transcriptomic data has contributed to the understanding of the immunopathology of diseases 94 It is important to mention that while our work has the strengths of a robust cross-study and cross-tissue network analysis, it has limited value to mechanistically explore the role of specific molecules in the outcome of COVID-19. However, several of these dysregulated molecules shared by COVID-19 and HLH have been successfully investigated for the treatment of SARS-CoV-2 infection, supporting our findings. For instance, inhibition of the CCR5-CCL4 axis by Leronlima (anti-CCR5 monoclonal antibody) 98 , or blockade of cytokine signaling by Tocilizumab (anti-IL-6R) 99 , Adalimumab (anti-TNF) 100 , or Anakinra (anti-IL1R) 101 

The authors declare no competing financial and/or non-financial interests concerning the work described. individuals with non-infectious respiratory diseases (NIRD; n=100) and with other respiratory infectious diseases (OIRD; n=41) b individuals negative for SARS-CoV-2 but admitted to ICU (n=16) and nonICU (n=10) units due to respiratory symptoms nph=nasopharyngeal

This paper analyzes existing, publicly available data. The accession numbers for the datasets are listed in the key resources table.

All original codes used for data analysis have been deposited at github (https://github.com/lschimke/COVID19-and-HLH-paper) and are publicly available as of the date of publication. R packages are listed in the key resources table.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

We searched in public functional genomics data repositories (Gene Expression Omnibus 111 and Array Express 112 for human transcriptome data from patients with HLH and COVID-19 published until February 2021. Inclusion criteria for datasets were transcriptome data from minimum n=10 patients without any treatment, availability of gene counts, blood or nasopharyngeal swab samples, and availability of control groups for comparison. This resulted in 7 studies, 6 from COVID-19 patients and one from HLH patients (containing 4 patients with germline mutations and 7 without identified mutation) with transcriptome data generated from different platforms ( Table 1) . Furthermore, we included the scRNAseq study obtained from the European Genomephenome Archive 113 (EGA; EGAS00001004571) of blood leukocytes from two independent cohorts of patients with COVID-19 and healthy controls, respectively 65 . Combined, we investigated a total of 1253 transcriptome samples.

Read counts were transformed (log2 count per million or CPM) and differentially expressed genes (DEGs) between groups were identified through the webtool NetworkAnalyst 3.0 114 using limma-voom pipeline 115 . Statistical cut-offs to define DEGs are described below in the section statistical analysis. Shared DEGs among all datasets were displayed using Venn diagram 116 and Circos Plot 117 online tools.

The Seurat Object containing the scRNAseq published by Schulte-Schrepping et al. 65 and deposited at the EGA (EGAS00001004571) were used for single cell analysis. We followed the Seurat pipeline 118 as previously described by Stuart et al. 119 to perform differential expression analysis and data visualization, i.e., UMAP, dotplot, and heatmap construction. Regression for the number of UMIs and scaling were performed as previously described 65 .

For more comprehensive Protein-Protein Interaction (PPI) analyses, we used NAViGaTOR 3.0.14 120 to visualize genes commonly dysregulated in COVID-19 and HLH datasets, highlighting the biological processes enriched by each gene. Prior to visualization, DEGs were used as input into Integrated Interactions Database (IID version 2021-05; http://ophid.utoronto.ca/iid) 106, 121 to identify direct physical protein interactions. Then, the resultant network was annotated, analysed, and visualized using NAViGaTOR 3.0.14 120 .

We used ClusterProfiler 122 R package to obtain dot plots of enriched signaling pathways. Elsevier Pathway Collection analysis for selected gene lists (7 genes underlying fHLH/IEI and 11 genes associated with severe COVID-19) was carried out using Enrichr webtool [123] [124] [125] . Set of genes associated with cytokine/chemotaxis and neutrophil-mediated immunity from each dataset were visualized in bubble-based heat maps applying one minus cosine similarity using Morpheus 126 . Circular heatmaps were generated using R version 4.0.5 (The R Project for Statistical Computing. https://www.r-project.org/) and R studio Version 1.4.1106 (RStudio.

https://www.rstudio.com/) using the circlize R package. Box plots were generated using the R packages ggpubr, lemon, and ggplot2.

Principal Component Analysis (PCA) of genes associated with COVID-19 severity (25 transcripts) was performed using the R functions prcomp and princomp, through factoextra package 127 .

Canonical Correlation Analysis (CCA) 128 of genes associated with cytokines/chemokines and neutrophil-mediated immune responses was performed using the packages CCA and whitening 128 . In addition, we used the corrgram, psych, and inlmisc R packages to build correlograms. In addition, multilinear regression analysis for combinations of different variables (genes) was performed using the R package ggpubr, ggplot2 and ggExtra.

We also evaluated the proteomics data obtained from plasma samples of COVID-19 patients previously reported by Overmyer et al. 58 . Briefly, raw LFQ abundance values were quantified, normalized and log2 transformed as previously described 58 . Differences in protein expression between COVID-19_ICU and COVID-19_nonICU were calculated as described below in the section statistical analysis. Enrichment of proteins significant for COVID-19 ICU was performed using Enrichr webtool [123] [124] [125] and most significant enriched pathways were displayed by dot plot created with R using tidyverse, viridis and ggplot2 packages while Circos Plot of gene-pathway association was built using Circos online tool 117 .

We employed random forest model to construct a classifier able to discriminate between COVID-19_nonICU and COVID-19_ICU highlighting the most significant predictors for ICU admission. We trained a Random Forest model using the functionalities of the R package randomForest (version 4.6.14) 79 . Five thousand trees were used, and the number of variables resampled were equal to three. Follow-up analysis used the Gini decrease, number of nodes, and mean minimum depth as criteria to determine variable importance. Interaction between pair of variables was assessed by minimum depth as criterion. The adequacy of the Random Forest model as a classifier was assessed through out of bags error rate and ROC curve. For cross-validation, we split the dataset in training and testing samples, using 75% of the observations for training and 25% for testing

To determine DEGs of each dataset we applied the statistical cut-offs of log2 fold-change > 1 (up-regulated), log2 fold-change < -1 (down-regulated), and adjusted p-value < 0.05. We used the Fisher´s method to combine p values from multiple studies for information integration 114 .

Differences in protein expression between COVID-19_ICU and COVID-19_nonICU was calculated using the nonparametric MANOVA (multivariate analysis of variance) test 129 followed by analysis of nonparametric Inference for Multivariate Data 130 using the R packages npmv, nparcomp, and ggplot2.

All supplemental figures, titles, and legends are provided in separate document file.

Supplemental Tables S1-S14 are provided in a separate document file: Table S1 : Up-and Down-regulated DEGs of all datasets 

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NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis

Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts

Jvenn: An interactive Venn diagram viewer

Circos: An information aesthetic for comparative genomics

Integrated analysis of multimodal single-cell data

Comprehensive Integration of Single-Cell Data

NAViGaTOR: Network Analysis, Visualization and Graphing Toronto

Informed use of protein-protein interaction data: A focus on the integrated interactions database (IID)

ClusterProfiler: An R package for comparing biological themes among gene clusters

Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology

Multivariate Analysis II, Practical Guide to Principal Component Methods in R. (STHDA)

A whitening approach to probabilistic canonical correlation analysis for omics data integration

nparcomp: An R Software Package for Nonparametric Multiple Comparisons and Simultaneous Confidence Intervals

Nonparametric Inference for Multivariate Data: The R Package npmv