netOmics
1 November 2022
Package
netOmics 1.4.0
Contents
The emergence of multi-omics data enabled the development of integration methods.
With netOmics, we go beyond integration by introducing an interpretation tool. netOmics is a package for the creation and exploration of multi-omics networks.
Depending on the provided dataset, it allows to create inference networks from expression data but also interaction networks from knowledge databases. After merging the sub-networks to obtain a global multi-omics network, we propose network exploration methods using propoagation techniques to perform functional prediction or identification of molecular mechanisms.
Furthermore, the package has been developed for longitudinal multi-omics data and can be used in conjunction with our previously published package timeOmics.
Overview
For more informnation about the method, please check (Bodein et al. 2020)
In this vignette, we introduced a case study which depict the main steps to create and explore multi-omics networks from multi-omics time-course data.
1 Requirements
# install the package via BioConductor
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("netOmics")# install the package via github
library(devtools)
install_github("abodein/netOmics")# load the package
library(netOmics)# usefull packages to build this vignette
library(timeOmics)
library(tidyverse)
library(igraph)2 Case Study: Human Microbiome Project T2D
The package will be illustrated on longitudinal MO dataset to study the seasonality of MO expression in patients with diabetes (Sailani et al. 2020).
The data used in this vignette is a subset of the data available at: https://github.com/aametwally/ipop_seasonal
We focused on a single individual with 7 timepoints. 6 different omics were sampled (RNA, proteins, cytokines, gut microbiome, metabolites and clinical variables).
# load data
data("hmp_T2D")3 (optional: timeOmics analysis)
The first step of the analysis is the preprocessing and longitudinal clustering. This step is carried out with timeOmics and should be reserved for longitudinal data.
It ensures that the time profiles are classified into groups of similar profiles so each MO molecule is labbeled with its cluster.
In addition, timeOmics can identify a multi-omics signature of the clusters. These molecules can be, for example, the starting points of the propogation analysis.
For more informations about timeOmics, please check http://www.bioconductor.org/packages/release/bioc/html/timeOmics.html
As illustrated in the R chunk below the timeOmics step includes:
- omic-specific preprocessing and longitudinal fold-change filtering
- modelling of expression profiles
- clustering of MO expression profiles
- signature identification by cluster
# not evaluated in this vignette
#1 filter fold-change
remove.low.cv <- function(X, cutoff = 0.5){
# var.coef
cv <- unlist(lapply(as.data.frame(X),
function(x) abs(sd(x, na.rm = TRUE)/mean(x, na.rm= TRUE))))
return(X[,cv > cutoff])
}
fc.threshold <- list("RNA"= 1.5, "CLINICAL"=0.2, "GUT"=1.5, "METAB"=1.5,
"PROT" = 1.5, "CYTO" = 1)
# --> hmp_T2D$raw
data.filter <- imap(raw, ~{remove.low.cv(.x, cutoff = fc.threshold[[.y]])})
#2 scale
data <- lapply(data.filter, function(x) log(x+1))
# --> hmp_T2D$data
#3 modelling
lmms.func <- function(X){
time <- rownames(X) %>% str_split("_") %>%
map_chr(~.x[[2]]) %>% as.numeric()
lmms.output <- lmms::lmmSpline(data = X, time = time,
sampleID = rownames(X), deri = FALSE,
basis = "p-spline", numCores = 4,
keepModels = TRUE)
return(lmms.output)
}
data.modelled <- lapply(data, function(x) lmms.func(x))
# 4 clustering
block.res <- block.pls(data.modelled, indY = 1, ncomp = 1)
getCluster.res <- getCluster(block.res)
# --> hmp_T2D$getCluster.res
# 5 signature
list.keepX <- list("CLINICAL" = 4, "CYTO" = 3, "GUT" = 10, "METAB" = 3,
"PROT" = 2,"RNA" = 34)
sparse.block.res <- block.spls(data.modelled, indY = 1, ncomp = 1, scale =TRUE,
keepX =list.keepX)
getCluster.sparse.res <- getCluster(sparse.block.res)
# --> hmp_T2D$getCluster.sparse.restimeOmics resulted in 2 clusters, labelled 1 and -1
# clustering results
cluster.info <- hmp_T2D$getCluster.res4 Network building
Each layer of the network is built sequentially and then assembled in a second section.
All the functions in the package can be used on one element or a list of
elements.
In the longitudinal context of the data, kinetic cluster sub-networks are built
plus a global network
(1, -1 and All).
4.1 Inference Network
Multi-omics network building starts with a first layer of gene. Currently, the ARACNe algorithm handles the inference but we will include more algorithms in the future.
The function get_grn return a Gene Regulatory Network from gene expression
data.
Optionally, the user can provide a timeOmics clustering result (?getCluster)
to get cluster specific sub-networks. In this case study, this will
automatically build the networks (1, -1 and All), as indicated previously.
The get_graph_stats function provides basic graph statistics such as the
number of vertices and edges.
If the vertices have different attributes, it also includes a summary of these.
cluster.info.RNA <- timeOmics::getCluster(cluster.info, user.block = "RNA")
graph.rna <- get_grn(X = hmp_T2D$data$RNA, cluster = cluster.info.RNA)
# to get info about the network
get_graph_stats(graph.rna)## node.c node.i edge edge.density type:gene mode:core cluster:-1 cluster:1
## All 759 16 70539 0.2351888 775 775 610 165
## 1 155 10 2922 0.2159645 165 165 NA 165
## -1 595 15 61000 0.3284072 610 610 610 NA4.2 Interaction from databases
As for the genes, the second layer is a protein layer (Protein-Protein Interaction). This time, no inference is performed. Instead, known interactions are extracted from a database of interaction (BIOGRID).
The function get_interaction_from_database will fetch the interactions from a
database provided as a data.frame (with columns from and to) or a graph
(igraph object).
In addition to the interactions between the indicated molecules, the first
degree neighbors can also be collected (option user.ego = TRUE)
# Utility function to get the molecules by cluster
get_list_mol_cluster <- function(cluster.info, user.block){
require(timeOmics)
tmp <- timeOmics::getCluster(cluster.info, user.block)
res <- tmp %>% split(.$cluster) %>%
lapply(function(x) x$molecule)
res[["All"]] <- tmp$molecule
return(res)
}
cluster.info.prot <- get_list_mol_cluster(cluster.info, user.block = 'PROT')
graph.prot <- get_interaction_from_database(X = cluster.info.prot,
db = hmp_T2D$interaction.biogrid,
type = "PROT", user.ego = TRUE)
# get_graph_stats(graph.prot)In this example, only a subset of the Biogrid database is used (matching elements).
4.3 Other inference methods
Another way to compute networks from expression data is to use other inference methods. In the following chunk, we intend to illustrate the use of the SparCC algorithm (Friedman and Alm 2012) on the gut data and how it can be integrate into the pipeline. (sparcc is not included in this package)
# not evaluated in this vignette
library(SpiecEasi)
get_sparcc_graph <- function(X, threshold = 0.3){
res.sparcc <- sparcc(data = X)
sparcc.graph <- abs(res.sparcc$Cor) >= threshold
colnames(sparcc.graph) <- colnames(X)
rownames(sparcc.graph) <- colnames(X)
res.graph <- graph_from_adjacency_matrix(sparcc.graph,
mode = "undirected") %>% simplify
return(res.graph)
}
gut_list <- get_list_mol_cluster(cluster.info, user.block = 'GUT')
graph.gut <- list()
graph.gut[["All"]] <- get_sparcc_graph(hmp_T2D$raw$GUT, threshold = 0.3)
graph.gut[["1"]] <- get_sparcc_graph(hmp_T2D$raw$GUT %>%
dplyr::select(gut_list[["1"]]),
threshold = 0.3)
graph.gut[["-1"]] <- get_sparcc_graph(hmp_T2D$raw$GUT %>%
dplyr::select(gut_list[["-1"]]),
threshold = 0.3)
class(graph.gut) <- "list.igraph"graph.gut <- hmp_T2D$graph.gut
# get_graph_stats(graph.gut)4.4 Other examples
For this case study, we complete this first step of network building with the missing layers.
# CYTO -> from database (biogrid)
cyto_list = get_list_mol_cluster(cluster.info = cluster.info,
user.block = "CYTO")
graph.cyto <- get_interaction_from_database(X = cyto_list,
db = hmp_T2D$interaction.biogrid,
type = "CYTO", user.ego = TRUE)
# get_graph_stats(graph.cyto)
# METAB -> inference
cluster.info.metab <- timeOmics::getCluster(X = cluster.info,
user.block = "METAB")
graph.metab <- get_grn(X = hmp_T2D$data$METAB,
cluster = cluster.info.metab)
# get_graph_stats(graph.metab)
# CLINICAL -> inference
cluster.info.clinical <- timeOmics::getCluster(X = cluster.info,
user.block = 'CLINICAL')
graph.clinical <- get_grn(X = hmp_T2D$data$CLINICAL,
cluster = cluster.info.clinical)
# get_graph_stats(graph.clinical)5 Layer merging
We included 2 types of layer merging:
- merging with interactions uses the shared elements between 2 graphs to build a larger network.
- merging with correlations uses the spearman correlation from expression profiles between 2 layers when any interaction is known.
5.1 Merging with interactions
The function combine_layers enables the fusion of different network layers.
It combines the network (or list of network) in graph1 with the network
(or list of network) in graph2, based on the shared vertices between
the networks.
Additionally, the user can provide an interaction table interaction.df
(in the form of a data.frame or igraph object).
In the following chunk, we sequentially merge RNA, PROT and CYTO layers and uses the TFome information (TF protein -> Target Gene) to connect these layers.
full.graph <- combine_layers(graph1 = graph.rna, graph2 = graph.prot)
full.graph <- combine_layers(graph1 = full.graph, graph2 = graph.cyto)
full.graph <- combine_layers(graph1 = full.graph,
graph2 = hmp_T2D$interaction.TF)
# get_graph_stats(full.graph)5.2 Merging with correlations
To connect omics layers for which no interaction information is available, we propose to use a threshold on the correlation between the expression profiles of two or more omics data.
The strategy is as follows: we isolate the omics from the data and calculate the correlations between this omics and the other data.
all_data <- reduce(hmp_T2D$data, cbind)
# omic = gut
gut_list <- get_list_mol_cluster(cluster.info, user.block = "GUT")
omic_data <- lapply(gut_list, function(x)dplyr::select(hmp_T2D$data$GUT, x))
# other data = "RNA", "PROT", "CYTO"
other_data_list <- get_list_mol_cluster(cluster.info,
user.block = c("RNA", "PROT", "CYTO"))
other_data <- lapply(other_data_list, function(x)dplyr::select(all_data, x))
# get interaction between gut data and other data
interaction_df_gut <- get_interaction_from_correlation(X = omic_data,
Y = other_data,
threshold = 0.99)
# and merge with full graph
full.graph <- combine_layers(graph1 = full.graph,
graph2 = graph.gut,
interaction.df = interaction_df_gut$All)# omic = Clinical
clinical_list <- get_list_mol_cluster(cluster.info, user.block = "CLINICAL")
omic_data <- lapply(clinical_list,
function(x)dplyr::select(hmp_T2D$data$CLINICAL, x))
# other data = "RNA", "PROT", "CYTO", "GUT"
other_data_list <- get_list_mol_cluster(cluster.info,
user.block = c("RNA", "PROT",
"CYTO", "GUT"))
other_data <- lapply(other_data_list, function(x)dplyr::select(all_data, x))
# get interaction between gut data and other data
interaction_df_clinical <- get_interaction_from_correlation(X = omic_data
, Y = other_data,
threshold = 0.99)
# and merge with full graph
full.graph <- combine_layers(graph1 = full.graph,
graph2 = graph.clinical,
interaction.df = interaction_df_clinical$All)# omic = Metab
metab_list <- get_list_mol_cluster(cluster.info, user.block = "METAB")
omic_data <- lapply(metab_list, function(x)dplyr::select(hmp_T2D$data$METAB, x))
# other data = "RNA", "PROT", "CYTO", "GUT", "CLINICAL"
other_data_list <- get_list_mol_cluster(cluster.info,
user.block = c("RNA", "PROT", "CYTO",
"GUT", "CLINICAL"))
other_data <- lapply(other_data_list, function(x)dplyr::select(all_data, x))
# get interaction between gut data and other data
interaction_df_metab <- get_interaction_from_correlation(X = omic_data,
Y = other_data,
threshold = 0.99)
# and merge with full graph
full.graph <- combine_layers(graph1 = full.graph,
graph2 = graph.metab,
interaction.df = interaction_df_metab$All)6 Addition of supplemental layers
For the interpretation of the MO integration results, the use of additional information layers or molecules can be useful to enrich the network.
6.1 Over Representation Analysis
ORA is a common step to include knowledge.
The function get_interaction_from_ORA perform the ORA analysis from the
desired molecules and return an interaction graph with the enriched terms and
the corresponding molecules.
Then, the interaction graph with the new vertices can be linked to the network as illustrated in the previous step.
Here, ORA was performed with RNA, PROT, and CYTO against the Gene Ontology.
# ORA by cluster/All
mol_ora <- get_list_mol_cluster(cluster.info,
user.block = c("RNA", "PROT", "CYTO"))
# get ORA interaction graph by cluster
graph.go <- get_interaction_from_ORA(query = mol_ora,
sources = "GO",
organism = "hsapiens",
signif.value = TRUE)
# merge
full.graph <- combine_layers(graph1 = full.graph, graph2 = graph.go)6.2 External knowledge
Additionally, knowledge from external sources can be included in the network.
In the following chunk, we performed disease-related gene enrichment analysis
from medlineRanker (http://cbdm-01.zdv.uni-mainz.de/~jfontain/cms/?page_id=4).
We converted the results into a data.frame (with the columns from and to)
and this acted as an interaction database.
# medlineRanker -> database
medlineranker.res.df <- hmp_T2D$medlineranker.res.df %>%
dplyr::select(Disease, symbol) %>%
set_names(c("from", "to"))
mol_list <- get_list_mol_cluster(cluster.info = cluster.info,
user.block = c("RNA", "PROT", "CYTO"))
graph.medlineranker <- get_interaction_from_database(X = mol_list,
db = medlineranker.res.df,
type = "Disease",
user.ego = TRUE)
# get_graph_stats(graph.medlineranker)
# merging
full.graph <- combine_layers(graph1 = full.graph, graph2 = graph.medlineranker)We complete the MO network preparation with attribute cleaning and addition of several attributes such as:
- mode = “core” if the vertex was originally present in the data; “extended” otherwise
- sparse = TRUE if the vertex was present in kinetic cluster signature; FALSE otherwise
- type = type of omics (“RNA”,“PROT”,“CLINICAL”,“CYTO”,“GUT”,“METAB”,“GO”, “Disease”)
- cluster = ‘1’, ‘-1’ or ‘NA’ (for vertices not originally present in the original data)
# graph cleaning
graph_cleaning <- function(X, cluster.info){
# no reusability
X <- igraph::simplify(X)
va <- vertex_attr(X)
viewed_mol <- c()
for(omic in unique(cluster.info$block)){
mol <- intersect(cluster.info %>% dplyr::filter(.$block == omic) %>%
pull(molecule), V(X)$name)
viewed_mol <- c(viewed_mol, mol)
X <- set_vertex_attr(graph = X,
name = "type",
index = mol,
value = omic)
X <- set_vertex_attr(graph = X,
name = "mode",
index = mol,
value = "core")
}
# add medline ranker and go
mol <- intersect(map(graph.go, ~ as_data_frame(.x)$to) %>%
unlist %>% unique(), V(X)$name) # only GO terms
viewed_mol <- c(viewed_mol, mol)
X <- set_vertex_attr(graph = X, name = "type", index = mol, value = "GO")
X <- set_vertex_attr(graph = X, name = "mode",
index = mol, value = "extended")
mol <- intersect(as.character(medlineranker.res.df$from), V(X)$name)
viewed_mol <- c(viewed_mol, mol)
X <- set_vertex_attr(graph = X, name = "type",
index = mol, value = "Disease")
X <- set_vertex_attr(graph = X, name = "mode",
index = mol, value = "extended")
other_mol <- setdiff(V(X), viewed_mol)
if(!is_empty(other_mol)){
X <- set_vertex_attr(graph = X, name = "mode",
index = other_mol, value = "extended")
}
X <- set_vertex_attr(graph = X, name = "mode",
index = intersect(cluster.info$molecule, V(X)$name),
value = "core")
# signature
mol <- intersect(V(X)$name, hmp_T2D$getCluster.sparse.res$molecule)
X <- set_vertex_attr(graph = X, name = "sparse", index = mol, value = TRUE)
mol <- setdiff(V(X)$name, hmp_T2D$getCluster.sparse.res$molecule)
X <- set_vertex_attr(graph = X, name = "sparse", index = mol, value = FALSE)
return(X)
}FULL <- lapply(full.graph, function(x) graph_cleaning(x, cluster.info))
get_graph_stats(FULL)## node.c node.i edge edge.density cluster:-1 cluster:1 type:CLINICAL
## All 2113 123 89511 0.03582260 681 238 39
## 1 494 76 5581 0.03441556 NA 238 18
## -1 1730 69 62529 0.03866256 681 NA 22
## type:CYTO type:Disease type:GO type:GUT type:METAB type:PROT type:RNA
## All 37 178 172 58 105 872 775
## 1 15 110 19 33 56 150 169
## -1 24 174 126 25 49 758 621
## mode:core mode:extended sparse:FALSE sparse:TRUE
## All 1009 1227 2132 104
## 1 292 278 525 45
## -1 738 1061 1739 607 Network exploration
7.1 Basics network exploration
We can use basic graph statistics to explore the network such as degree distribution, modularity, and short path.
# degree analysis
d <- degree(FULL$All)
hist(d)
d[max(d)]
# modularity # Warnings: can take several minutes
res.mod <- walktrap.community(FULL$All)
# ...
# modularity
sp <- shortest.paths(FULL$All)7.2 Random walk with restart
RWR is a powerful tool to explore the MO networks which simulates a particle
that randomly walk on the network.
From a starting point (seed) it ranks the other vertices based on their
proximity with the seed and the network structure.
We use RWR for function prediction and molecular mechanism identification.
In the example below, the seeds were the GO terms vertices.
# seeds = all vertices -> takes 5 minutes to run on regular computer
# seeds <- V(FULL$All)$name
# rwr_res <- random_walk_restart(FULL, seeds)
# seed = some GO terms
seeds <- head(V(FULL$All)$name[V(FULL$All)$type == "GO"])
rwr_res <- random_walk_restart(FULL, seeds)7.2.1 Find vertices with specific attributes
After the RWR analysis, we implemented several functions to extract valuable information.
To identify MO molecular functions, the seed can be a GO term and we are interested to identify vertices with different omics type within the closest nodes.
The function rwr_find_seeds_between_attributes can identify which seeds were
able to reach vertices with different attributes (ex: type) within the
closest k (ex: 15) vertices.
The function summary_plot_rwr_attributes displays the number of different
values for a seed attribute as a bar graph.
rwr_type_k15 <- rwr_find_seeds_between_attributes(X = rwr_res,
attribute = "type", k = 15)
# a summary plot function
summary_plot_rwr_attributes(rwr_type_k15)
summary_plot_rwr_attributes(rwr_type_k15$All)
Alternatively, we can be interested to find functions or molecules which link different kinetic cluster (to find regulatory mechanisms).
rwr_type_k15 <- rwr_find_seeds_between_attributes(X = rwr_res$All,
attribute = "cluster", k = 15)
summary_plot_rwr_attributes(rwr_type_k15)
A RWR subnetworks can also be displayed with plot_rwr_subnetwork
from a specific seed.
sub_res <- rwr_type_k15$`GO:0005737`
sub <- plot_rwr_subnetwork(sub_res, legend = TRUE, plot = TRUE)
7.2.2 Function prediction
Finally, RWR can also be used for function prediction. From an annotated genes, the predicted function can be the closest vertex of the type “GO”.
We generalized this principle to identify, from a seed of interest, the closest
node (or top closest nodes) with specific attributes and value.
In the example below, the gene “ZNF263” is linked to the 5 closest nodes of type = ‘GO’ and type = ‘Disease’.
rwr_res <- random_walk_restart(FULL$All, seed = "ZNF263")
# closest GO term
rwr_find_closest_type(rwr_res, seed = "ZNF263", attribute = "type",
value = "GO", top = 5)## $ZNF263
## # A tibble: 5 × 7
## NodeNames Score SeedName cluster type mode sparse
## <chr> <dbl> <chr> <chr> <chr> <chr> <lgl>
## 1 GO:0008022 0.00000989 ZNF263 <NA> GO extended FALSE
## 2 GO:0012505 0.00000984 ZNF263 <NA> GO extended FALSE
## 3 GO:0044877 0.00000962 ZNF263 <NA> GO extended FALSE
## 4 GO:0060997 0.000000965 ZNF263 <NA> GO extended FALSE
## 5 GO:0048583 0.000000963 ZNF263 <NA> GO extended FALSE
##
## attr(,"class")
## [1] "rwr.closest"# closest Disease
rwr_find_closest_type(rwr_res, seed = "ZNF263", attribute = "type",
value = "Disease", top = 5)## $ZNF263
## # A tibble: 5 × 7
## NodeNames Score SeedName cluster type mode sparse
## <chr> <dbl> <chr> <chr> <chr> <chr> <lgl>
## 1 Carcinoma, Renal Cell 0.000000970 ZNF263 <NA> Disease exte… FALSE
## 2 Hypogonadism 0.000000968 ZNF263 <NA> Disease exte… FALSE
## 3 Nasopharyngeal Neoplasms 0.000000959 ZNF263 <NA> Disease exte… FALSE
## 4 Familial Mediterranean Fever 0.000000956 ZNF263 <NA> Disease exte… FALSE
## 5 Spondylitis, Ankylosing 0.000000956 ZNF263 <NA> Disease exte… FALSE
##
## attr(,"class")
## [1] "rwr.closest"# closest nodes with an attribute "cluster" and the value "-1"
rwr_find_closest_type(rwr_res, seed = "ZNF263", attribute = "cluster",
value = "-1", top = 5)## $ZNF263
## # A tibble: 5 × 7
## NodeNames Score SeedName cluster type mode sparse
## <chr> <dbl> <chr> <chr> <chr> <chr> <lgl>
## 1 ATF7 0.000880 ZNF263 -1 RNA core FALSE
## 2 TET1 0.000824 ZNF263 -1 RNA core FALSE
## 3 LOC642943 0.00000941 ZNF263 -1 RNA core FALSE
## 4 LOC284801 0.00000506 ZNF263 -1 RNA core FALSE
## 5 ROR1.AS1 0.000000820 ZNF263 -1 RNA core FALSE
##
## attr(,"class")
## [1] "rwr.closest"seeds <- V(FULL$All)$name[V(FULL$All)$type %in% c("GO", "Disease")]sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
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## locale:
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## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] igraph_1.3.5 forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10
## [5] purrr_0.3.5 readr_2.1.3 tidyr_1.2.1 tibble_3.1.8
## [9] tidyverse_1.3.2 timeOmics_1.10.0 mixOmics_6.22.0 ggplot2_3.3.6
## [13] lattice_0.20-45 MASS_7.3-58.1 netOmics_1.4.0 BiocStyle_2.26.0
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## loaded via a namespace (and not attached):
## [1] googledrive_2.0.0 colorspace_2.0-3
## [3] ellipsis_0.3.2 corpcor_1.6.10
## [5] XVector_0.38.0 fs_1.5.2
## [7] farver_2.1.1 hexbin_1.28.2
## [9] ggrepel_0.9.1 bit64_4.0.5
## [11] lubridate_1.8.0 AnnotationDbi_1.60.0
## [13] RSpectra_0.16-1 fansi_1.0.3
## [15] xml2_1.3.3 codetools_0.2-18
## [17] cachem_1.0.6 knitr_1.40
## [19] jsonlite_1.8.3 broom_1.0.1
## [21] GO.db_3.16.0 dbplyr_2.2.1
## [23] png_0.1-7 supraHex_1.36.0
## [25] graph_1.76.0 BiocManager_1.30.19
## [27] compiler_4.2.1 httr_1.4.4
## [29] backports_1.4.1 assertthat_0.2.1
## [31] Matrix_1.5-1 fastmap_1.1.0
## [33] lazyeval_0.2.2 gargle_1.2.1
## [35] cli_3.4.1 htmltools_0.5.3
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## [43] Biobase_2.58.0 cellranger_1.1.0
## [45] jquerylib_0.1.4 vctrs_0.5.0
## [47] Biostrings_2.66.0 ape_5.6-2
## [49] nlme_3.1-160 lmtest_0.9-40
## [51] xfun_0.34 rvest_1.0.3
## [53] lifecycle_1.0.3 googlesheets4_1.0.1
## [55] zlibbioc_1.44.0 zoo_1.8-11
## [57] scales_1.2.1 minet_3.56.0
## [59] hms_1.1.2 RandomWalkRestartMH_1.18.0
## [61] parallel_4.2.1 gprofiler2_0.2.1
## [63] RColorBrewer_1.1-3 yaml_2.3.6
## [65] memoise_2.0.1 gridExtra_2.3
## [67] sass_0.4.2 stringi_1.7.8
## [69] RSQLite_2.2.18 highr_0.9
## [71] S4Vectors_0.36.0 BiocGenerics_0.44.0
## [73] BiocParallel_1.32.0 GenomeInfoDb_1.34.0
## [75] rlang_1.0.6 pkgconfig_2.0.3
## [77] bitops_1.0-7 matrixStats_0.62.0
## [79] dnet_1.1.7 evaluate_0.17
## [81] labeling_0.4.2 htmlwidgets_1.5.4
## [83] bit_4.0.4 tidyselect_1.2.0
## [85] plyr_1.8.7 magrittr_2.0.3
## [87] bookdown_0.29 R6_2.5.1
## [89] magick_2.7.3 IRanges_2.32.0
## [91] generics_0.1.3 DBI_1.1.3
## [93] haven_2.5.1 pillar_1.8.1
## [95] withr_2.5.0 KEGGREST_1.38.0
## [97] RCurl_1.98-1.9 modelr_0.1.9
## [99] crayon_1.5.2 rARPACK_0.11-0
## [101] utf8_1.2.2 ellipse_0.4.3
## [103] plotly_4.10.0 tzdb_0.3.0
## [105] rmarkdown_2.17 readxl_1.4.1
## [107] grid_4.2.1 data.table_1.14.4
## [109] propr_4.2.6 blob_1.2.3
## [111] Rgraphviz_2.42.0 reprex_2.0.2
## [113] digest_0.6.30 stats4_4.2.1
## [115] munsell_0.5.0 viridisLite_0.4.1
## [117] bslib_0.4.0References
Bodein, Antoine, Marie-Pier Scott-Boyer, Olivier Perin, Kim-Anh Le Cao, and Arnaud Droit. 2020. “Interpretation of Network-Based Integration from Multi-Omics Longitudinal Data.” bioRxiv.
Friedman, Jonathan, and Eric J Alm. 2012. “Inferring Correlation Networks from Genomic Survey Data.”
Sailani, M Reza, Ahmed A Metwally, Wenyu Zhou, Sophia Miryam Schüssler-Fiorenza Rose, Sara Ahadi, Kevin Contrepois, Tejaswini Mishra, et al. 2020. “Deep Longitudinal Multiomics Profiling Reveals Two Biological Seasonal Patterns in California.” Nature Communications 11 (1): 1–12.