3.1 From a data frame

In the data frame the first and second columns should be composed of source and target nodes.
A sample appropriate data frame is brought below:

This is a co-expression dataset obtained from a paper by Salavaty et al.²

# Preparing the data
MyData <- coexpression.data

# Reconstructing the graph
My_graph <- graph_from_data_frame(d=MyData)

If you look at the class of My_graph you should see that it has an igraph class:

class(My_graph)
#> [1] "igraph"

3.2 From an adjacency matrix

A sample appropriate adjacency matrix is brought below:

• Note that the matrix has the same number of rows and columns.

# Preparing the data
MyData <- coexpression.adjacency        

# Reconstructing the graph
My_graph <- graph_from_adjacency_matrix(MyData)        

3.3 From an incidence matrix

A sample appropriate incidence matrix is brought below:

# Reconstructing the graph
My_graph <- graph_from_adjacency_matrix(MyData)        

3.4 From a SIF file

SIF is the common output format of the Cytoscape software.

# Reconstructing the graph
My_graph <- sif2igraph(Path = "Sample_SIF.sif")        

class(My_graph)
#> [1] "igraph"

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4.1 Network vertices

Network vertices (nodes) are required in order to calculate their centrality measures. Thus, before calculation of network centrality measures we need to obtain the name of required network vertices. To this end, we use the V function, which is obtained from the igraph package. However, you may provide a character vector of the name of your desired nodes manually.

• Note in many of the centrality index functions the entire network nodes are assessed if no vector of desired vertices is provided.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
My_graph_vertices <- V(My_graph)        

head(My_graph_vertices)
#> + 6/794 vertices, named, from 775cff6:
#> [1] ADAMTS9-AS2 C8orf34-AS1 CADM3-AS1   FAM83A-AS1  FENDRR      LANCL1-AS1

4.2 Degree centrality

Degree centrality is the most commonly used local centrality measure which could be calculated via the degree function obtained from the igraph package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating degree centrality
My_graph_degree <- degree(My_graph, v = GraphVertices, normalized = FALSE) 

head(My_graph_degree)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>         172         121         168          26         189         176

Degree centrality could be also calculated for directed graphs via specifying the mode parameter.

4.3 Betweenness centrality

Betweenness centrality, like degree centrality, is one of the most commonly used centrality measures but is representative of the global centrality of a node. This centrality metric could also be calculated using a function obtained from the igraph package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating betweenness centrality
My_graph_betweenness <- betweenness(My_graph, v = GraphVertices,    
                                    directed = FALSE, normalized = FALSE)

head(My_graph_betweenness)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>   21719.857   28185.199   26946.625    2940.467   33333.369   21830.511

Betweenness centrality could be also calculated for directed and/or weighted graphs via specifying the directed and weights parameters, respectively.

4.4 Neighborhood connectivity

Neighborhood connectivity is one of the other important centrality measures that reflect the semi-local centrality of a node. This centrality measure was first represented in a Science paper³ in 2002 and is for the first time calculable in R environment via the influential package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating neighborhood connectivity
neighrhood.co <- neighborhood.connectivity(graph = My_graph,    
                                           vertices = GraphVertices,
                                           mode = "all")

head(neighrhood.co)
#>  ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>   11.290698    4.983471    7.970238    3.000000   15.153439   13.465909

Neighborhood connectivity could be also calculated for directed graphs via specifying the mode parameter.

4.5 H-index

H-index is H-index is another semi-local centrality measure that was inspired from its application in assessing the impact of researchers and is for the first time calculable in R environment via the influential package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating H-index
h.index <- h_index(graph = My_graph,    
                   vertices = GraphVertices,
                   mode = "all")

head(h.index)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>          11           9          11           2          12          12

H-index could be also calculated for directed graphs via specifying the mode parameter.

4.6 Local H-index

Local H-index (LH-index) is a semi-local centrality measure and an improved version of H-index centrality that leverages the H-index to the second order neighbors of a node and is for the first time calculable in R environment via the influential package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating Local H-index
lh.index <- lh_index(graph = My_graph,    
                   vertices = GraphVertices,
                   mode = "all")

head(lh.index)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>        1165         446         994          34        1289        1265

Local H-index could be also calculated for directed graphs via specifying the mode parameter.

4.7 Collective Influence

Collective Influence (CI) is a global centrality measure that calculates the product of the reduced degree (degree - 1) of a node and the total reduced degree of all nodes at a distance d from the node. This centrality measure is for the first time provided in an R package.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating Collective Influence
ci <- collective.influence(graph = My_graph,    
                          vertices = GraphVertices,
                          mode = "all", d=3)

head(ci)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>        9918       70560       39078         675       10716        7350

Collective Influence could be also calculated for directed graphs via specifying the mode parameter.

4.8 ClusterRank

ClusterRank is a local centrality measure that makes a connection between local and semi-local characteristics of a node and at the same time removes the negative effects of local clustering.

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculating ClusterRank
cr <- clusterRank(graph = My_graph,    
                  vids = GraphVertices,
                  directed = FALSE, loops = TRUE)

head(cr)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>   63.459812    5.185675   21.111776    1.280000  135.098278   81.255195

ClusterRank could be also calculated for directed graphs via specifying the directed parameter.

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5.1 Conditional probability of deviation from means

The function cond.prob.analysis assesses the conditional probability of deviation of two centrality measures (or any other two continuous variables) from their corresponding means in opposite directions.

# Preparing the data
MyData <- centrality.measures        

# Assessing the conditional probability
My.conditional.prob <- cond.prob.analysis(data = MyData,       
                                          nodes.colname = rownames(MyData),
                                          Desired.colname = "BC",
                                          Condition.colname = "NC")

print(My.conditional.prob)
#> $ConditionalProbability
#> [1] 51.61871
#> 
#> $ConditionalProbability_split.half.sample
#> [1] 51.73611

• As you can see in the results, the whole data is also randomly splitted into half in order to further test the validity of conditional probability assessments.
• The higher the conditional probability the more two centrality measures behave in contrary manners.

5.2 Nature of association (considering dependent and independent)

The function double.cent.assess could be used to automatically assess both the distribution mode of centrality measures (two continuous variables) and the nature of their association. The analyses done through this formula are as follows:

1. Normality assessment:
   • Variables with lower than 5000 observations: Shapiro-Wilk test
   • Variables with over 5000 observations: Anderson-Darling test
     
     
2. Assessment of non-linear/non-monotonic correlation:
   • Non-linearity assessment: Fitting a generalized additive model (GAM) with integrated smoothness approximations using the mgcv package
     
     
   • Non-monotonicity assessment: Comparing the squared coefficients of the correlation based on Spearman’s rank correlation analysis and ranked regression test with non-linear splines.
     • Squared coefficient of Spearman’s rank correlation > R-squared ranked regression with non-linear splines: Monotonic
     • Squared coefficient of Spearman’s rank correlation < R-squared ranked regression with non-linear splines: Non-monotonic
       
       
3. Dependence assessment:
   • Hoeffding’s independence test: Hoeffding’s test of independence is a test based on the population measure of deviation from independence which computes a D Statistics ranging from -0.5 to 1: Greater D values indicate a higher dependence between variables.
   • Descriptive non-linear non-parametric dependence test: This assessment is based on non-linear non-parametric statistics (NNS) which outputs a dependence value ranging from 0 to 1. For further details please refer to the NNS R package⁴: Greater values indicate a higher dependence between variables.
     
     
4. Correlation assessment: As the correlation between most of the centrality measures follows a non-monotonic form, this part of the assessment is done based on the NNS statistics which itself calculates the correlation based on partial moments and outputs a correlation value ranging from -1 to 1. For further details please refer to the NNS R package.
   
   
5. Assessment of conditional probability of deviation from means This step assesses the conditional probability of deviation of two centrality measures (or any other two continuous variables) from their corresponding means in opposite directions.
   • The independent centrality measure (variable) is considered as the condition variable and the other as the desired one.
   • As you will see in the results, the whole data is also randomly splitted into half in order to further test the validity of conditional probability assessments.
   • The higher the conditional probability the more two centrality measures behave in contrary manners.

# Preparing the data
MyData <- centrality.measures        

# Association assessment
My.metrics.assessment <- double.cent.assess(data = MyData,       
                                            nodes.colname = rownames(MyData),
                                            dependent.colname = "BC",
                                            independent.colname = "NC")

print(My.metrics.assessment)
#> $Summary_statistics
#>         BC NC
#> Min.              0.000000000                   1.2000
#> 1st Qu.           0.000000000                  66.0000
#> Median            0.000000000                 156.0000
#> Mean              0.005813357                 132.3443
#> 3rd Qu.           0.000340000                 179.3214
#> Max.              0.529464720                 192.0000
#> 
#> $Normality_results
#>                               p.value
#> BC    1.415450e-50
#> NC 9.411737e-30
#> 
#> $Dependent_Normality
#> [1] "Non-normally distributed"
#> 
#> $Independent_Normality
#> [1] "Non-normally distributed"
#> 
#> $GAM_nonlinear.nonmonotonic.results
#>      edf  p-value 
#> 8.992406 0.000000 
#> 
#> $Association_type
#> [1] "nonlinear-nonmonotonic"
#> 
#> $HoeffdingD_Statistic
#>         D_statistic P_value
#> Results  0.01770279   1e-08
#> 
#> $Dependence_Significance
#>                       Hoeffding
#> Results Significantly dependent
#> 
#> $NNS_dep_results
#>         Correlation Dependence
#> Results  -0.7948106  0.8647164
#> 
#> $ConditionalProbability
#> [1] 55.35386
#> 
#> $ConditionalProbability_split.half.sample
#> [1] 55.90331

Note: It should also be noted that as a single regression line does not fit all models with a certain degree of freedom, based on the size and correlation mode of the variables provided, this function might return an error due to incapability of running step 2. In this case, you may follow each step manually or as an alternative run the other function named double.cent.assess.noRegression which does not perform any regression test and consequently it is not required to determine the dependent and independent variables.

5.3 Nature of association (without considering dependence direction)

The function double.cent.assess.noRegression could be used to automatically assess both the distribution mode of centrality measures (two continuous variables) and the nature of their association. The analyses done through this formula are as follows:

1. Normality assessment:
   • Variables with lower than 5000 observations: Shapiro-Wilk test
   • Variables with over 5000 observations: Anderson–Darling test
     
     
2. Dependence assessment:
   • Hoeffding’s independence test: Hoeffding’s test of independence is a test based on the population measure of deviation from independence which computes a D Statistics ranging from -0.5 to 1: Greater D values indicate a higher dependence between variables.
   • Descriptive non-linear non-parametric dependence test: This assessment is based on non-linear non-parametric statistics (NNS) which outputs a dependence value ranging from 0 to 1. For further details please refer to the NNS R package: Greater values indicate a higher dependence between variables.
     
     
3. Correlation assessment: As the correlation between most of the centrality measures follows a non-monotonic form, this part of the assessment is done based on the NNS statistics which itself calculates the correlation based on partial moments and outputs a correlation value ranging from -1 to 1. For further details please refer to the NNS R package.
   
   
4. Assessment of conditional probability of deviation from means This step assesses the conditional probability of deviation of two centrality measures (or any other two continuous variables) from their corresponding means in opposite directions.
   • The centrality2 variable is considered as the condition variable and the other (centrality1) as the desired one.
   • As you will see in the results, the whole data is also randomly splitted into half in order to further test the validity of conditional probability assessments.
   • The higher the conditional probability the more two centrality measures behave in contrary manners.

# Preparing the data
MyData <- centrality.measures        

# Association assessment
My.metrics.assessment <- double.cent.assess.noRegression(data = MyData,       
                                                         nodes.colname = rownames(MyData),
                                                         centrality1.colname = "BC",
                                                         centrality2.colname = "NC")

print(My.metrics.assessment)
#> $Summary_statistics
#>         BC NC
#> Min.              0.000000000                   1.2000
#> 1st Qu.           0.000000000                  66.0000
#> Median            0.000000000                 156.0000
#> Mean              0.005813357                 132.3443
#> 3rd Qu.           0.000340000                 179.3214
#> Max.              0.529464720                 192.0000
#> 
#> $Normality_results
#>                               p.value
#> BC    1.415450e-50
#> NC 9.411737e-30
#> 
#> $Centrality1_Normality
#> [1] "Non-normally distributed"
#> 
#> $Centrality2_Normality
#> [1] "Non-normally distributed"
#> 
#> $HoeffdingD_Statistic
#>         D_statistic P_value
#> Results  0.01770279   1e-08
#> 
#> $Dependence_Significance
#>                       Hoeffding
#> Results Significantly dependent
#> 
#> $NNS_dep_results
#>         Correlation Dependence
#> Results  -0.7948106  0.8647164
#> 
#> $ConditionalProbability
#> [1] 55.35386
#> 
#> $ConditionalProbability_split.half.sample
#> [1] 55.68163

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6.1 Integrated Value of Influence (IVI) from centrality measures

# Preparing the data
MyData <- centrality.measures        

# Calculation of IVI
My.vertices.IVI <- ivi.from.indices(DC = MyData$DC,       
                                   CR = MyData$CR,
                                   NC = MyData$NC,
                                   LH_index = MyData$LH_index,
                                   BC = MyData$BC,
                                   CI = MyData$CI)

head(My.vertices.IVI)
#> [1] 24.670056  8.344337 18.621049  1.017768 29.437028 33.512598

6.2 Integrated Value of Influence (IVI) from a graph

# Preparing the data
MyData <- coexpression.data        

# Reconstructing the graph
My_graph <- graph_from_data_frame(MyData)        

# Extracting the vertices
GraphVertices <- V(My_graph)        

# Calculation of IVI
My.vertices.IVI <- ivi(graph = My_graph, vertices = GraphVertices, 
                       weights = NULL, directed = FALSE, mode = "all",
                       loops = TRUE, d = 3, scale = "range")

head(My.vertices.IVI)
#> ADAMTS9-AS2 C8orf34-AS1   CADM3-AS1  FAM83A-AS1      FENDRR  LANCL1-AS1 
#>    39.53878    19.94999    38.20524     1.12371   100.00000    47.49356

IVI could be also calculated for directed and/or weighted graphs via specifying the directed, mode, and weights parameters.

Check out our paper⁵ for a more complete description of the IVI formula and all of its underpinning methods and analyses.

The following tutorial video demonstrates how to simply calculate the IVI value of all of the nodes within a network.

6.3 Network visualization

The cent_network.vis is a function for the visualization of a network based on applying a centrality measure to the size and color of network nodes. The centrality of network nodes could be calculated by any means and based on any centrality index. Here, we demonstrate the visualization of a network according to IVI values.

# Reconstructing the graph
set.seed(70)
My_graph <-  igraph::sample_gnm(n = 50, m = 120, directed = TRUE)

# Calculating the IVI values
My_graph_IVI <- ivi(My_graph, directed = TRUE)

# Visualizing the graph based on IVI values
My_graph_IVI_Vis <- cent_network.vis(graph = My_graph,
                                     cent.metric = My_graph_IVI,
                                     directed = TRUE,
                                     plot.title = "IVI-based Network",
                                     legend.title = "IVI value")

My_graph_IVI_Vis

The above figure illustrates a simple use case of the function cent_network.vis. You can apply this function to directed/undirected and/or weighted/unweighted networks. Also, a complete flexibility (list of arguments) have been provided for the adjustment of colors, transparencies, sizes, titles, etc. Additionally, several different layouts have been provided that could be conveniently applied to a network.

In the case of highly crowded networks, the “grid” layout would be most appropriate.

The following tutorial video demonstrates how to visualize a network based on the centrality of nodes (e.g. their IVI values).

6.4 IVI shiny app

A shiny app has also been developed for the calculation of IVI as well as IVI-based network visualization, which is accessible using the following command.
influential::runShinyApp("IVI")
You can also access the shiny app online at the Influential Software Package server.

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10.1 Preparing the differential/regression data

Suppose we have time-course transcriptomics data and have previously performed differential expression analysis for each step-wise comparison between time points. Suppose we have also performed regression or trajectory analysis to identify genes with significant alterations across the full time course.

# Prepare sample feature names
gene.names <- paste("gene", 1:2000, sep = "_")

set.seed(60)
tp2.vs.tp1.DEGs <- data.frame(
  logFC = rnorm(n = 700, mean = 2, sd = 4),
  FDR = runif(n = 700, min = 0.0001, max = 0.049)
)
rownames(tp2.vs.tp1.DEGs) <- sample(gene.names, size = 700)

set.seed(70)
tp3.vs.tp2.DEGs <- data.frame(
  logFC = rnorm(n = 1300, mean = -1, sd = 5),
  FDR = runif(n = 1300, min = 0.0011, max = 0.039)
)
rownames(tp3.vs.tp2.DEGs) <- sample(gene.names, size = 1300)

set.seed(80)
regression.data <- data.frame(
  R_squared = runif(n = 800, min = 0.1, max = 0.85)
)
rownames(regression.data) <- sample(gene.names, size = 800)

10.2 Assembling the Diff_data

Use the function diff_data.assembly to automatically generate the Diff_data table for the ExIR model.

my_Diff_data <- diff_data.assembly(tp2.vs.tp1.DEGs,
                                   tp3.vs.tp2.DEGs,
                                   regression.data)

my_Diff_data[c(1:10),]

Have a look at the top 10 rows of the Diff_data data frame:

10.3 Preparing the experimental data (Exptl_data)

The recommended input format for non-Seurat omics data is a matrix or data frame with features in rows and samples/cells in columns. This is the default expected orientation:

Exptl_data_orientation = "features_rows"

For bulk omics data, the input should usually be normalized and log-transformed before running exir. Alternatively, if the input is raw count-like bulk RNA-seq data, users can set:

normalize = TRUE

In that case, ExIR applies TMM normalization followed by logCPM transformation using edgeR. This normalization strategy is suitable for many bulk RNA-seq count datasets, but users should confirm that it is appropriate for their specific data modality. If another normalization method is more appropriate, users should pre-normalize the data and keep normalize = FALSE.

Here, we prepare a simple normalized bulk-like expression matrix with genes in rows and samples in columns.

set.seed(60)

MyExptl_data <- matrix(
  data = runif(n = 100000, min = 2, max = 300),
  nrow = 2000,
  ncol = 50,
  dimnames = list(
    gene.names,
    c(
      paste("cancer_sample", 1:25, sep = "_"),
      paste("normal_sample", 1:25, sep = "_")
    )
  )
)

# Log-transform the data to mimic normalized log-scale bulk expression values
MyExptl_data <- log2(MyExptl_data)

MyExptl_data[1:5, c(1:5, 45:50)] %>% t()

Have a look at top 5 cancer and normal samples (transposed for better visualization) of the Exptl_data:

When Exptl_data_orientation = "features_columns" The sample conditions can be supplied as a vector in the same order as the columns of MyExptl_data:

condition <- c(rep("C", 25), rep("N", 25))
MyExptl_data$condition <- condition

Alternatively, if Exptl_data_orientation = "features_rows", the condition can be provided as a row in the input data and the name of that row can be passed to the condition argument. For example:

MyExptl_data_with_condition <- rbind(
  condition = condition,
  MyExptl_data
)

# In this case, condition = "condition"

However, for matrix-based analyses, it is generally cleaner to provide the condition as a separate vector.

10.4 Optional pseudo-sampling for large datasets

For large datasets, especially single-cell datasets with many cells, ExIR can perform pseudo-sampling before running the main model. Pseudo-sampling is recommended when the number of samples/cells is greater than 500 or when computational resources are limited.

For bulk data, pseudo-sampling is performed within each condition group by randomly partitioning samples into non-overlapping groups and averaging normalized log-expression values within each group.

For single-cell RNA-seq data, pseudo-sampling is performed by randomly partitioning cells within each condition group, summing raw counts within each pseudo-sample, and then applying TMM normalization and logCPM transformation using edgeR.

The argument:

pseudo_samples_per_group = 100

means that ExIR will generate 100 pseudo-samples per condition group. For example, if a condition contains 500 cells and pseudo_samples_per_group = 100, each pseudo-sample will contain 5 cells. If another condition contains 536 cells, 36 pseudo-samples will contain 6 cells and 64 pseudo-samples will contain 5 cells.

10.5 Conservative feature filtering

ExIR includes an optional conservative feature-filtering step before the main RF, PCA, and correlation analyses:

feature_filter = TRUE

This is not a highly variable gene filter. Instead, it removes features with insufficient prevalence, insufficient total signal if requested, or essentially zero variance. This helps reduce non-informative features before the expensive full feature-feature correlation analysis while still preserving biologically relevant non-DE mediators.

By default, features present in Diff_data and Desired_list are retained even if they fail the conservative expression/prevalence filters:

always_keep_diff_features = TRUE

This helps preserve candidate differential or user-specified features while reducing uninformative background features.

10.6 Running the ExIR model

Prepare the required arguments for the exir model.

# The table of differential/regression data previously prepared
my_Diff_data

# Column indices of differential values in Diff_data
Diff_value <- c(1, 3)

# Column index of regression values in Diff_data
Regr_value <- 5

# Column indices of significance values in Diff_data
Sig_value <- c(2, 4)

# The matrix of normalized experimental data previously prepared
MyExptl_data

# The condition vector in the same order as the samples/columns of MyExptl_data
condition <- c(rep("C", 25), rep("N", 25))

# Optional desired list of features
set.seed(60)
MyDesired_list <- sample(gene.names, size = 500)

# Run the ExIR model
My.exir <- exir(
  Desired_list = MyDesired_list,
  Diff_data = my_Diff_data,
  Diff_value = Diff_value,
  Regr_value = Regr_value,
  Sig_value = Sig_value,
  Exptl_data = MyExptl_data,
  Exptl_data_type = "bulk",
  condition = condition,
  Exptl_data_orientation = "features_rows",
  normalize = FALSE,
  pseudo_sample = FALSE,
  feature_filter = TRUE,
  cor_thresh_method = "mr",
  mr = 100,
  seed = 60,
  verbose = FALSE
)

names(My.exir)
#> [1] "Driver table"         "DE-mediator table"    "Biomarker table"      "Graph"

class(My.exir)
#> [1] "ExIR_Result"

If the input bulk RNA-seq data are raw count-like values, normalize = TRUE can be used:

My.exir <- exir(
  Desired_list = MyDesired_list,
  Diff_data = my_Diff_data,
  Diff_value = Diff_value,
  Regr_value = Regr_value,
  Sig_value = Sig_value,
  Exptl_data = raw_count_matrix,
  Exptl_data_type = "bulk",
  condition = condition,
  Exptl_data_orientation = "features_rows",
  normalize = TRUE,
  feature_filter = TRUE,
  cor_thresh_method = "mr",
  mr = 100,
  seed = 60,
  verbose = FALSE
)

The output of exir is an object of class ExIR_Result, containing a graph and one or more result tables depending on the input data and model settings.

Have a look at the output tables:

• Drivers

• Biomarkers

• DE-mediators

10.7 Running exir on single-cell RNA-seq data

For single-cell RNA-seq data, raw counts are recommended when pseudo-sampling is used. The input can be a matrix, sparse matrix, or Seurat object.

For a sparse count matrix with genes in rows and cells in columns:

My.exir.sc <- exir(
  Desired_list = MyDesired_list,
  Diff_data = my_Diff_data,
  Diff_value = Diff_value,
  Regr_value = Regr_value,
  Sig_value = Sig_value,
  Exptl_data = sc_counts,
  Exptl_data_type = "sc",
  condition = cell_condition,
  Exptl_data_orientation = "features_rows",
  pseudo_sample = TRUE,
  pseudo_samples_per_group = 100,
  feature_filter = TRUE,
  cor_thresh_method = "mr",
  mr = 20,
  seed = 60,
  verbose = FALSE
)

For a Seurat object:

My.exir.seurat <- exir(
  Desired_list = MyDesired_list,
  Diff_data = my_Diff_data,
  Diff_value = Diff_value,
  Regr_value = Regr_value,
  Sig_value = Sig_value,
  Exptl_data = seurat_object,
  Exptl_data_type = "sc",
  condition = "condition",
  assay = "RNA",
  layer = "counts",
  pseudo_sample = TRUE,
  pseudo_samples_per_group = 100,
  feature_filter = TRUE,
  cor_thresh_method = "mr",
  mr = 20,
  seed = 60,
  verbose = FALSE
)

When a Seurat object is supplied, condition should be the name of a metadata column. The assay and layer arguments specify which assay/layer should be used.

If single-cell data are not pseudo-sampled, users should provide appropriately normalized single-cell data and set:

pseudo_sample = FALSE
normalize = FALSE

The following tutorial video demonstrates how to run the ExIR model on a sample experimental data.

You can also computationally simulate knockout and/or up-regulation of the top candidate features outputted by ExIR to evaluate the impact of their manipulation on the flow of information/signaling and the integrity of the network prior to experimental validation.

10.8 ExIR visualization

The exir.vis function visualizes the output of the ExIR model. The function gets the output of the ExIR model as a single argument and returns a plot of the top prioritized features of all available classes. Here, we visualize the top five candidates of the results of the ExIR model obtained in the previous step.

My.exir.Vis <- exir.vis(exir.results = My.exir, 
                        n = 5,
                        y.axis.title = "Gene")

My.exir.Vis

A complete set of arguments is available for adjusting the visual features of the plot and selecting the desired classes, feature types, and number of top candidates.

The following tutorial video demonstrates how to visualize the results of the ExIR model.

10.9 ExIR shiny app

A shiny app has also been developed for Running the ExIR model, visualization of its results as well as computational simulation of knockout and/or up-regulation of its top candidate outputs, which is accessible using the following command.
influential::runShinyApp("ExIR")
You can also access the shiny app online at the Influential Software Package server.

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