| Title: | Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis |
|---|---|
| Description: | The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. |
| Authors: | Lang Ho Lee <[email protected]> and Sasha A. Singh <[email protected]> |
| Maintainer: | Lang Ho Lee <[email protected]> and Sasha A. Singh <[email protected]> |
| License: | GPL-3 |
| Version: | 1.25.0 |
| Built: | 2024-11-19 04:41:24 UTC |
| Source: | https://github.com/bioc/XINA |
Add plot legend and locate it outside of a network plot
add_legend(legend_location = "bottomright", ...)add_legend(legend_location = "bottomright", ...)
legend_location |
Network centrality score matrix |
... |
Numeric, complex, or logical vectors. |
a legend to a plot
'alluvial_enriched’ draws an alluvial plot and finds comigrated proteins. The comigration is a group of proteins that show the same expression pattern, classified and evaluated by XINA clustering, in at least two conditions. XINA can reduce the dataset complexity by filtering based on the number of comigrated proteins (size, ’comigration_size’ parameter) and perform an enrichment test (P-value of Fisher’s exact test, ’pval_threshold’) to determine significance of enriched comigrations. The Fisher’s exact test can only be done for two conditions at a time. The following 2x2 table was used to calculate the P-value from the Fisher’s exact test. To evaluate significance of co-migrated proteins from cluster #1 in control to cluster #2 in test group,
| - | cluster #1 in control | other clusters in control |
| cluster #2 in test | 65 (TP) | 175 (FP) |
| other clusters in test | 35 (FN) | 979 (TN) |
alluvial_enriched(clustering_result, selected_conditions, comigration_size = 0, pval_threshold = 1, pval_method = "fdr", cex = 0.7, alpha = 0.3)alluvial_enriched(clustering_result, selected_conditions, comigration_size = 0, pval_threshold = 1, pval_method = "fdr", cex = 0.7, alpha = 0.3)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
selected_conditions |
A vector of condition names used in XINA clustering results. The number of selected conditions should be at least two. |
comigration_size |
The number of proteins comigrated together in the selected conditions of XINA clustering results. Default is 0 |
pval_threshold |
This option is avaiable only when you selected two conditions for comigration search. |
pval_method |
Method for p-value adjustment. See p.adjust |
cex |
Scaling of fonts of category labels. Default if 0.7. See alluvial |
alpha |
Transparency of the stripes. Default if 0.3. See alluvial |
A data frame containing comigrations and an alluvial plot showing comigrations
# load XINA example data data(xina_example) # Get the experimental conditions in the example data classes <- as.vector(example_clusters$condition) # Get comigrations without any thresholds all_comigrations <- alluvial_enriched(example_clusters, classes) # Get comigrations that have >= 5 size (the number of comigrated proteins) all_cor_enriched <- alluvial_enriched(example_clusters, classes, comigration_size=5) # Get all the comigrations between Control and Stimulus1 comigrations_Control_Stimulus1 <- alluvial_enriched(example_clusters, c(classes[1],classes[2])) # Get comigrations between Control and Stimulus1, that have >=5 size comigrations_Control_Stimulus1_over5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5) # Get comigrations between Control and Stimulus1, # that have >= 5 size and enrichment FDR <= 0.01 comigrations_Control_Stimulus1_pval0.01_size5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5, pval_threshold=0.01) # Get comigrations between Control and Stimulus1, # that have >= 5 size and enrichment Benjamini & Yekutieli <= 0.01 comigrations_Control_Stimulus1_BY0.01_size5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5, pval_threshold=0.01, pval_method="BY")# load XINA example data data(xina_example) # Get the experimental conditions in the example data classes <- as.vector(example_clusters$condition) # Get comigrations without any thresholds all_comigrations <- alluvial_enriched(example_clusters, classes) # Get comigrations that have >= 5 size (the number of comigrated proteins) all_cor_enriched <- alluvial_enriched(example_clusters, classes, comigration_size=5) # Get all the comigrations between Control and Stimulus1 comigrations_Control_Stimulus1 <- alluvial_enriched(example_clusters, c(classes[1],classes[2])) # Get comigrations between Control and Stimulus1, that have >=5 size comigrations_Control_Stimulus1_over5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5) # Get comigrations between Control and Stimulus1, # that have >= 5 size and enrichment FDR <= 0.01 comigrations_Control_Stimulus1_pval0.01_size5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5, pval_threshold=0.01) # Get comigrations between Control and Stimulus1, # that have >= 5 size and enrichment Benjamini & Yekutieli <= 0.01 comigrations_Control_Stimulus1_BY0.01_size5 <- alluvial_enriched(example_clusters, c(classes[1],classes[2]), comigration_size=5, pval_threshold=0.01, pval_method="BY")
Fisher's exact test to calculate the significance over all comigrations. The following 2x2 table was used to calculate p-value from Fisher's exact test. To evaluate significance of comigrated proteins from cluster #1 in control to cluster #2 in test condition,
| cluster #1 in control | other clusters in control | |
| cluster #2 in test | 65 (TP) | 175 (FP) |
| other clusters in test | 35 (FN) | 979 (TN) |
'alluvial_enrichment_tests' also provides another statistical methods including Hypergeometric test and Chi-square test.
alluvial_enrichment_tests(count_table, c1, c2, non_cluster = 0, test_type = "fisher")alluvial_enrichment_tests(count_table, c1, c2, non_cluster = 0, test_type = "fisher")
count_table |
A data frame generated by using count. |
c1 |
A selected cluster in the first condition. |
c2 |
A selected cluster in the second condition. |
non_cluster |
The cluster number for proteins that were not detected in a specific sample. Default is 0. |
test_type |
Enrichment test type. 'fisher' = Fisher's exact test, 'hyper' = Hypergeometric test, 'chisq' = Chi-square test |
P-value of comigration enrichment test and 2x2 table information
'calculate_centrality_scores' computes network centrality scores
calculate_centrality_scores(net, centrality_type = "Degree")calculate_centrality_scores(net, centrality_type = "Degree")
net |
protein-protein interaction network of igraph |
centrality_type |
the maximum number of clusters |
A vector of network centrality scores
Calculate image size based on the number of clusters
default_size(max_cluster)default_size(max_cluster)
max_cluster |
the maximum number of clusters |
A vector of plot width and height
'draw_alluvial_plot' draw a alluvial plot
draw_alluvial_plot(clustering_result, selected_conditions, count_table, alluvia_colors = NULL, cex = 0.7, alpha = 0.3)draw_alluvial_plot(clustering_result, selected_conditions, count_table, alluvia_colors = NULL, cex = 0.7, alpha = 0.3)
clustering_result |
A list containing XINA clustering results. See xina_clustering. |
selected_conditions |
A vector of condition names used in XINA clustering results. The number of selected conditions should be at least two. |
count_table |
A data frame generated by using count. |
alluvia_colors |
A vector containing the user-defined colors for each alluvium. |
cex |
Size of cluster number on block axis. Default if 0.7. See alluvial. |
alpha |
Transparency of alluvia colors. Default is 0.3. See alluvial. |
An alluvial plot displaying comigrations and the data frame containing the input count_table with colors.
# load XINA example data data(xina_example) # get a vector of experimental conditions analyzed in the clustering results classes <- as.vector(example_clusters$condition) comigrations_size_over5 <- alluvial_enriched(example_clusters, classes, comigration_size=5) draw_alluvial_plot(example_clusters, classes, comigrations_size_over5)# load XINA example data data(xina_example) # get a vector of experimental conditions analyzed in the clustering results classes <- as.vector(example_clusters$condition) comigrations_size_over5 <- alluvial_enriched(example_clusters, classes, comigration_size=5) draw_alluvial_plot(example_clusters, classes, comigrations_size_over5)
aligned. XINA clustering results aligned by conditions
data_column. Column names for data matrix
out_dir. Not available in this example dataset
nClusters. The number of user-desired clusters. It's 30 in the example.
max_cluster. The number of clusters found in the dataset. It's 21 in the example.
chosen_model. The chosen covariance model for the example dataset. It's VEI in the example
optimal_BIC. BIC at the optimized clustering. It's 29473.57 in the example
condition. The experimental conditions in the dataset.
color_for_condition. The default color for the conditions that will be used in XINA plot drawing.
color_for_clusters. The default color for the clusters that will be used in XINA clustering plot.
norm_method. The used normalization method to standardize the input data. It's "sum_normalization" in the example.
A list with the example XINA clustering result
Extract data column names from XINA clustering result
extract_data_column(col_head_of_clustering)extract_data_column(col_head_of_clustering)
col_head_of_clustering |
Column names of XINA clustering result |
A vector containing column names of data matrix
Compare clusters and find similar ones
find_similar_clusters(clustering_result, threshold = 0.95)find_similar_clusters(clustering_result, threshold = 0.95)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
threshold |
Pearson's r threshold to find similar ones |
Write a csv file containing similar clustering information based on the given Pearson's R threshold
Count the number of comigrated proteins using count
generate_count_table(clustering_result, selected_conditions, comigration_size)generate_count_table(clustering_result, selected_conditions, comigration_size)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
selected_conditions |
A vector of condition names used in XINA clustering results. |
comigration_size |
The number of proteins comigrated together in the selected conditions of XINA clustering results. Default is 0. |
A data frame containing comigrations.
Merge input kinetics files
generate_superset(f_names, data_column, delim = ",", norm = "sum_normalization")generate_superset(f_names, data_column, delim = ",", norm = "sum_normalization")
f_names |
A vector of .csv file paths containing kinetics data |
data_column |
A vector of column names containing data matrix |
delim |
The delimiter of input file (default is ',') |
norm |
The normalization method. It should be one of c('sum_normalization', 'zscore'). Default is 'sum_normalization'. |
A data frame containing kinetics data obtained from files in the f_names vector
Pre-defined 30 colors
get_color_for_nodes()get_color_for_nodes()
A vector for color code of XINA graphics
Generate color series for XINA graphics
get_colors(nClusters, set = "", colorset = NULL)get_colors(nClusters, set = "", colorset = NULL)
nClusters |
The number of clusters |
set |
Pre-defined color series set |
colorset |
manually defined color codes |
A vector for color code of XINA graphics
'get_comigrations_by_name' finds proteins comigrated with the given proteins
get_comigrations_by_name(clustering_result, selected_conditions, protein_list, cex = 0.7, alpha = 0.3)get_comigrations_by_name(clustering_result, selected_conditions, protein_list, cex = 0.7, alpha = 0.3)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
selected_conditions |
A vector of condition names used in XINA clustering results. The number of selected conditions should be at least two. |
protein_list |
A vector containing gene names. |
cex |
Size of cluster number on block axis. Default if 0.7. See alluvial |
alpha |
Transparency of alluvia colors. Default is 0.3. See alluvial |
An alluvial plot displaying comigrations and the data frame containing comigrations of the input proteins
# load XINA example data data(xina_example) # the clustering result table all_proteins <- as.character(example_clusters$aligned$`Gene name`) # get a vector of experimental conditions analyzed in the clustering results classes <- as.vector(example_clusters$condition) comigrated_prots_all <- get_comigrations_by_name(example_clusters, classes, all_proteins[1:3])# load XINA example data data(xina_example) # the clustering result table all_proteins <- as.character(example_clusters$aligned$`Gene name`) # get a vector of experimental conditions analyzed in the clustering results classes <- as.vector(example_clusters$condition) comigrated_prots_all <- get_comigrations_by_name(example_clusters, classes, all_proteins[1:3])
get comigrations that at least one biased cluster is involved in. Biased clusters are defined by
get_condition_biased_comigrations(clustering_result, count_table = NULL, selected_conditions, condition_composition, threshold_percent = 50, color_for_null = "gray", color_for_highly_matched = "red4", cex = 0.7, alpha = 0.3)get_condition_biased_comigrations(clustering_result, count_table = NULL, selected_conditions, condition_composition, threshold_percent = 50, color_for_null = "gray", color_for_highly_matched = "red4", cex = 0.7, alpha = 0.3)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
count_table |
A data frame generated by using count. If count_table is NULL (by default), XINA will consider all the comigrations. |
selected_conditions |
A vector of condition names used in XINA clustering results. The number of selected conditions should be at least two. |
condition_composition |
The resulting data frame of 'plot_condition_compositions'. See plot_condition_compositions. |
threshold_percent |
Default is 50. The percentage threshold for finding condition-biased clusters |
color_for_null |
A color for non-condition-biased comigrations. Default is 'gray' |
color_for_highly_matched |
A color for comigrations that are involved with more than two condition-biased clusters. Default is 'red4' |
cex |
Size of cluster number on block axis. Default if 0.7. See alluvial. |
alpha |
Transparency of alluvia colors. Default is 0.3. See alluvial. |
An alluvial plot displaying comigrations and the data frame containing condition-biased comigrations.
# load XINA example data data(xina_example) # get a vector of experimental conditions analyzed in the clustering results conditions <- as.vector(example_clusters$condition) # get condition composition information condition_composition <- plot_condition_compositions(example_clusters) comigrations_size10 <- alluvial_enriched(example_clusters, conditions, comigration_size=10) # Finding condition-biased comigrations by 50% threshold condition_biased_comigrations <- get_condition_biased_comigrations(clustering_result=example_clusters, count_table=comigrations_size10, selected_conditions=conditions, condition_composition=condition_composition) # Finding condition-biased comigrations by 70% threshold condition_biased_comigrations <- get_condition_biased_comigrations(clustering_result=example_clusters, count_table=comigrations_size10, selected_conditions=conditions, condition_composition=condition_composition, threshold_percent=70)# load XINA example data data(xina_example) # get a vector of experimental conditions analyzed in the clustering results conditions <- as.vector(example_clusters$condition) # get condition composition information condition_composition <- plot_condition_compositions(example_clusters) comigrations_size10 <- alluvial_enriched(example_clusters, conditions, comigration_size=10) # Finding condition-biased comigrations by 50% threshold condition_biased_comigrations <- get_condition_biased_comigrations(clustering_result=example_clusters, count_table=comigrations_size10, selected_conditions=conditions, condition_composition=condition_composition) # Finding condition-biased comigrations by 70% threshold condition_biased_comigrations <- get_condition_biased_comigrations(clustering_result=example_clusters, count_table=comigrations_size10, selected_conditions=conditions, condition_composition=condition_composition, threshold_percent=70)
Get igraph layout by the number of nodes
get_layout(subnet_condition)get_layout(subnet_condition)
subnet_condition |
A igraph sub-network |
igraph network layout
Get mTOR pathway genes
get_mTOR_proteins(time_points, conditions)get_mTOR_proteins(time_points, conditions)
time_points |
A vector containing time points of the data matrix |
conditions |
A vector containing condition information, for example normal, disease and drug treated disase. |
A vector containing mTOR pathway gene names
Get randomized time-series data
get_random_data(time_points, conditions, num_total, percent.sign = 0.1, equal = TRUE)get_random_data(time_points, conditions, num_total, percent.sign = 0.1, equal = TRUE)
time_points |
A vector containing time points of the data matrix |
conditions |
A vector containing condition information, for example normal, disease and drug treated disase. |
num_total |
The number of total proteins to be generated |
percent.sign |
Percentage of differentially expressed proteins. Ignored when equal=FALSE. |
equal |
If equal is TRUE, all the conditions will have numbers between 0 and 1. If it is FALSE, the first three conditions will have different ranges. First condition will have numbers from 0.3 to 0.4. Second condition will have numbers from 0.6 to 0.8. Third condition will have numbers from 0.3 to 0.5. Other conditions will have numbers from 0 to 1. |
A list containing ramdomly generated data matrix
Calculate statistics of the given data for XINA network analysis
get_stats(centrality_results, na.rm = FALSE)get_stats(centrality_results, na.rm = FALSE)
centrality_results |
Network centrality score data frame calculated by XINA network module |
na.rm |
If it is FALSE, no exclusion of NA values. |
A data frame containing statistics of XINA network centrality scores
Predefined ggplot theme for removing ticks, titles and labels of X and Y axis
get_theme_blank()get_theme_blank()
A ggplot theme
Get proteins with no known interactions within the cluster based on the used protein-protein interaction database source
get_unknown_ppi_nodes(xina_result, cl)get_unknown_ppi_nodes(xina_result, cl)
xina_result |
A list containing XINA network analysis results. See xina_analysis |
cl |
the clustering number of XINA clustering results. See xina_clustering |
A data frame containing proteins with no known interactions within the cluster based on the used protein-protein interaction database source
# load XINA example data data(xina_example) # load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # Extract unknown PPI nodes in the cluster #1 get_unknown_ppi_nodes(xina_result_example, 1)# load XINA example data data(xina_example) # load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # Extract unknown PPI nodes in the cluster #1 get_unknown_ppi_nodes(xina_result_example, 1)
Characters of human genes
A character vector containing 19,396 human genes
https://www.ncbi.nlm.nih.gov/gene
Human gene description corresponding to 'gn' vector
A character vector containing 19,396 human gene descriptions
https://www.ncbi.nlm.nih.gov/gene
gene_symbol_1. Gene name interacting with gene name in 'gene_symbol_2'
gene_symbol_2. Gene name interacting with gene name in 'gene_symbol_1'
Experiment_type. Experimental or computational methods supporting the interaction
A data frame containing HRPD protein-protein interaction data
Customized function for vector length calculation
length2(x, na.rm = FALSE)length2(x, na.rm = FALSE)
x |
A vector |
na.rm |
If it is FALSE, no exclusion of NA values. |
A vector length
Get previous XINA clustering results to R space
load_previous_results(clustering_dir = getwd(), data_column = NULL, fp_clusters = "xina_clusters.csv")load_previous_results(clustering_dir = getwd(), data_column = NULL, fp_clusters = "xina_clusters.csv")
clustering_dir |
The directory path of XINA clustering results |
data_column |
A vector containing column names of data matrix |
fp_clusters |
File path of XINA clustering results |
Comma-separated file containing aligned XINA clustering results.
# Load XINA's example data data(xina_example) write.csv(example_clusters$aligned,"xina_clusters_aligned.csv") write.csv(example_clusters$clusters,"xina_clusters.csv") # Reload the clustering result example_clusters_reloaded <- load_previous_results(".")# Load XINA's example data data(xina_example) write.csv(example_clusters$aligned,"xina_clusters_aligned.csv") write.csv(example_clusters$clusters,"xina_clusters.csv") # Reload the clustering result example_clusters_reloaded <- load_previous_results(".")
Generate random proteomics dataset for testing XINA 'make_random_xina_data' will make random proteomics data for XINA test. The generated data will have three conditions and seven time points, c("0hr", "2hr", "6hr", "12hr", "24hr", "48hr", "72hr").
make_random_xina_data(n = 500, mtor = TRUE, time_points = c("0hr", "2hr", "6hr", "12hr", "24hr", "48hr", "72hr"), conditions = c("Control", "Stimulus1", "Stimulus2"))make_random_xina_data(n = 500, mtor = TRUE, time_points = c("0hr", "2hr", "6hr", "12hr", "24hr", "48hr", "72hr"), conditions = c("Control", "Stimulus1", "Stimulus2"))
n |
The number of proteins for one condition. Default is 500. |
mtor |
If it is TRUE (default), mTOR pathway genes will be significant. If it is FALSE, randomly selected genes will be significant in first three conditions. |
time_points |
A vector containing time points of the data matrix |
conditions |
A vector containing condition information, for example normal, disease and drug treated disase. |
Three comma-separated files containing time-series data for XINA
make_random_xina_data() g1 <- read.csv("Control.csv", check.names=FALSE, stringsAsFactors = FALSE) g2 <- read.csv("Stimulus1.csv", check.names=FALSE, stringsAsFactors = FALSE) g3 <- read.csv("Stimulus2.csv", check.names=FALSE, stringsAsFactors = FALSE) head(g1) head(g2) head(g3)make_random_xina_data() g1 <- read.csv("Control.csv", check.names=FALSE, stringsAsFactors = FALSE) g2 <- read.csv("Stimulus1.csv", check.names=FALSE, stringsAsFactors = FALSE) g3 <- read.csv("Stimulus2.csv", check.names=FALSE, stringsAsFactors = FALSE) head(g1) head(g2) head(g3)
'mutate_colors' generates new color scheme for XINA clustering plot based on condition composition results (plot_condition_compositions). If any clusters have higher percentage than the 'threshold_percent', XINA will assign new colors in accordance to 'color_for_condition'. If not, XINA will give 'gray' color or user-defined color via 'null_color' parameter.
mutate_colors(condition_composition, color_for_condition, null_color = "gray", threshold_percent = 50)mutate_colors(condition_composition, color_for_condition, null_color = "gray", threshold_percent = 50)
condition_composition |
A data frame generated by plot_condition_compositions |
color_for_condition |
A vector like 'color_for_condition' of xina_clustering |
null_color |
Default is 'gray'. This color is for clusters that are not biased to any of experimental conditions |
threshold_percent |
Default is 50. The percentage threshold for giving new colors |
A data frame containing statistics of XINA network centrality scores
# load XINA example data data(xina_example) # Plot condition composition pie-chart with default option condition_composition <- plot_condition_compositions(example_clusters) example_clusters$color_for_clusters <- mutate_colors(condition_composition, example_clusters$color_for_condition) plot_clusters(example_clusters, xval=c(0,2,6,12,24,48,72), xylab=FALSE)# load XINA example data data(xina_example) # Plot condition composition pie-chart with default option condition_composition <- plot_condition_compositions(example_clusters) example_clusters$color_for_clusters <- mutate_colors(condition_composition, example_clusters$color_for_condition) plot_clusters(example_clusters, xval=c(0,2,6,12,24,48,72), xylab=FALSE)
Organize XINA clustering information by gene name
organize_clusters(clustering_dir = getwd(), super_ds, file_out = TRUE)organize_clusters(clustering_dir = getwd(), super_ds, file_out = TRUE)
clustering_dir |
The directory path of XINA clustering results |
super_ds |
XINA clusters |
file_out |
If it is TRUE, it writes the aligned clustering informaion to "xina_clusters_aligned.csv" file. |
Comma-separated file containing aligned XINA clustering results.
Draw all the clustering results. 'plot_clusters' draws two plots, scaled and unscaled line graphs. Scaled graphs have same y limits that are 0 to 1 by default, but can be changed via 'y_lim' parameter.
plot_clusters(clustering_result, y_lim = NULL, xval = NULL, xtickmark = NULL, xylab = TRUE, ggplot_theme = NULL)plot_clusters(clustering_result, y_lim = NULL, xval = NULL, xtickmark = NULL, xylab = TRUE, ggplot_theme = NULL)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
y_lim |
Y axis limit. If you set y_lim=c(0,1), 'plot_clusters' will plot line graphs scaled from 0 to 1 in y-axis Default is NULL, which means unscaled line graphs. |
xval |
XINA basically considers time points as a ordinary variable, like 1,2,3,4...n. You can make the time points as a continuous variable using xval. |
xtickmark |
Change X axis tick marks. Default is data_column of the clustering result list. |
xylab |
If it is FALSE, x and y labels will be blank. If it is TRUE (defualt), x and y labels will be shown. |
ggplot_theme |
This is ggplot theme to modify XINA clustering plot. |
Line graphs of all the clusters
library(ggplot2) # load XINA example data data(xina_example) # Draw clustering plots plot_clusters(example_clusters) # Apply theme to the clustering plot theme1 <- theme(title=element_text(size=8, face='bold'), axis.text.x = element_text(size=7), axis.text.y = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) plot_clusters(example_clusters, ggplot_theme=theme1)library(ggplot2) # load XINA example data data(xina_example) # Draw clustering plots plot_clusters(example_clusters) # Apply theme to the clustering plot theme1 <- theme(title=element_text(size=8, face='bold'), axis.text.x = element_text(size=7), axis.text.y = element_blank(), axis.ticks.x = element_blank(), axis.ticks.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank()) plot_clusters(example_clusters, ggplot_theme=theme1)
Draw line graphs of all the proteins in the given dataset
plot_clusters_all(clustering_result, selected_condition = NULL)plot_clusters_all(clustering_result, selected_condition = NULL)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
selected_condition |
A condition name to draw the kinetics plot |
a list containing clustering results and pdf file containing a BIC plot in current working directory.
# load XINA example data data(xina_example) # Plot kinetics of all the proteins in Control plot_clusters_all(example_clusters, selected_condition="Control") # Plot kinetics of all the proteins in Stimulus1 plot_clusters_all(example_clusters, selected_condition="Stimulus1") # Plot kinetics of all the proteins in Stimulus2 plot_clusters_all(example_clusters, selected_condition="Stimulus2") # Plot kinetics of all the proteins in three data plot_clusters_all(example_clusters)# load XINA example data data(xina_example) # Plot kinetics of all the proteins in Control plot_clusters_all(example_clusters, selected_condition="Control") # Plot kinetics of all the proteins in Stimulus1 plot_clusters_all(example_clusters, selected_condition="Stimulus1") # Plot kinetics of all the proteins in Stimulus2 plot_clusters_all(example_clusters, selected_condition="Stimulus2") # Plot kinetics of all the proteins in three data plot_clusters_all(example_clusters)
computes condition composition of the XINA clustering results and draws pie-charts.
plot_condition_compositions(clustering_result, bullseye = FALSE, ggplot_theme = NULL)plot_condition_compositions(clustering_result, bullseye = FALSE, ggplot_theme = NULL)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
bullseye |
If it is TRUE, draw bullseye plot instead of the pie-chart. Default is FALSE |
ggplot_theme |
This is ggplot theme to modify condition composition pie-chart and bulles eye plots. |
A condition composition plot and a data frame containing condition compositions of the clusters
# load XINA example data data(xina_example) # Plot condition composition pie-chart with default option plot_condition_compositions(example_clusters) # Make a new color code for conditions condition_colors <- c("tomato","steelblue1","gold") names(condition_colors) <- example_clusters$condition example_clusters$color_for_condition <- condition_colors # Draw condition composition pie-chart with the new color code plot_condition_compositions(example_clusters) # Draw condition composition bullseye plot plot_condition_compositions(example_clusters, bullseye = TRUE)# load XINA example data data(xina_example) # Plot condition composition pie-chart with default option plot_condition_compositions(example_clusters) # Make a new color code for conditions condition_colors <- c("tomato","steelblue1","gold") names(condition_colors) <- example_clusters$condition example_clusters$color_for_condition <- condition_colors # Draw condition composition pie-chart with the new color code plot_condition_compositions(example_clusters) # Draw condition composition bullseye plot plot_condition_compositions(example_clusters, bullseye = TRUE)
Plot GO and KEGG enrichment results
plot_enrichment_results(enriched_results, term_description = "term_description", sig_score = "pvalue", num_terms = 0, get_log = TRUE, fill_color = "darkgray")plot_enrichment_results(enriched_results, term_description = "term_description", sig_score = "pvalue", num_terms = 0, get_log = TRUE, fill_color = "darkgray")
enriched_results |
GO or KEGG enrichment results. See xina_enrichment and xina_enrichment |
term_description |
Description of terms to be drawn on Y axis. Default is "term_description" of XINA enrichment results. |
sig_score |
significant score to plot on X axis. Default is "pvalue". |
num_terms |
The number of terms to be plotted. Default is 0, which menas no limit. |
get_log |
If this is TRUE, 'plot_enrichment_results' will take -log10 of p-values. |
fill_color |
Default is 'darkgray'. You can change color of bars. |
ggplot bar graph
## Not run: library(STRINGdb) # load XINA example data data(xina_example) # Get STRING database for protein-protein intereaction information string_db <- STRINGdb$new( version="10", species=9606, score_threshold=0, input_directory="" ) string_db # XINA analysis with STRING DB xina_result <- xina_analysis(example_clusters, string_db) # Select proteins that showed cluster #1 in the Stimulus2 condition subgroup <- subset(example_clusters$aligned, Stimulus2==1) protein_list <- as.vector(subgroup$`Gene name`) # Enrichment test and get significantly enriched functional terms # that have adjuseted p-value less than 0.1 kegg_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "KEGG", pval_threshold=0.1) plot_enrichment_results(kegg_enriched$KEGG, num_terms=10) ## End(Not run)## Not run: library(STRINGdb) # load XINA example data data(xina_example) # Get STRING database for protein-protein intereaction information string_db <- STRINGdb$new( version="10", species=9606, score_threshold=0, input_directory="" ) string_db # XINA analysis with STRING DB xina_result <- xina_analysis(example_clusters, string_db) # Select proteins that showed cluster #1 in the Stimulus2 condition subgroup <- subset(example_clusters$aligned, Stimulus2==1) protein_list <- as.vector(subgroup$`Gene name`) # Enrichment test and get significantly enriched functional terms # that have adjuseted p-value less than 0.1 kegg_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "KEGG", pval_threshold=0.1) plot_enrichment_results(kegg_enriched$KEGG, num_terms=10) ## End(Not run)
Give ranks based on network centrality scores
rank_centrality(centrality_score, type, num_breaks = 5)rank_centrality(centrality_score, type, num_breaks = 5)
centrality_score |
Network centrality score matrix |
type |
Network centrality score type, such as 'Eigenvector' |
num_breaks |
The number of ranks |
A vector containing ranks
gene_symbol_1. Gene name interacting with gene name in 'gene_symbol_2'
gene_symbol_2. Gene name interacting with gene name in 'gene_symbol_1'
PPI_Source. Data original source
A data frame containing STRING protein-protein interaction data
xina_analysis is to analyze protein-protein interaction(PPI) networks using STRINGdb and igraph R package. This module computes PPI networks within each XINA clusters.
xina_analysis(clustering_result, ppi_db, is_stringdb = TRUE, flag_simplify = TRUE, node_shape = "sphere", num_clusters_in_row = 5, img_size = NULL, img_qual = 300)xina_analysis(clustering_result, ppi_db, is_stringdb = TRUE, flag_simplify = TRUE, node_shape = "sphere", num_clusters_in_row = 5, img_size = NULL, img_qual = 300)
clustering_result |
A list containing XINA clustering results. See xina_clustering |
ppi_db |
STRINGdb object |
is_stringdb |
If it is TRUE (default), XINA will process 'ppi_db' as STRINGdb, but it is FALSE, XINA will accepts your 'ppi_db' as it is. You can make your own igraph network using customized PPI information instead of STRINGdb. |
flag_simplify |
If it is TRUE (default), XINA will exclude unconnected proteins |
node_shape |
You can choose node shape. Default is "sphere". See shapes |
num_clusters_in_row |
The number of clusters in a row on the XINA network plot. Default is 5. |
img_size |
Set the image size. For width=1000 and height=1500, it is img_size=c(1000,1500). |
img_qual |
Set the image resolution. Default is 300. |
A PNG file (XINA_Cluster_Networks.png) displaying PPI network plots of all the clusters and a list containing XINA network analysis results.
| Item | Description |
| All_network | PPI network of all the input proteins |
| Sub_network | A list containing PPI networks of each clusters |
| Data | XINA clustering results. See xina_clustering |
| Nodes | A list of proteins in each cluster |
| Conditions | A list of experimental condition of proteins in each cluster |
| Titles | A list of plot titles for XINA plotting |
| out_dir | A directory path storing XINA network analysis results |
| is_stringdb | False = different PPI DB and TRUE = STRING DB |
## Not run: # load XINA example data data(xina_example) # use the following code for utilizing up-to-date STRING DB tax_id <- 9606 # for human # tax_id <- 10090 # for mouse library(STRINGdb) library(igraph) string_db <- STRINGdb$new( version='10', species=tax_id, score_threshold=0, input_directory='' ) string_db xina_result <- xina_analysis(example_clusters, string_db, flag_simplify=FALSE) # Run XINA with a protein-protein interaction edgelist data(HPRD) net_all <- simplify(graph_from_data_frame(d=hprd_ppi, directed=FALSE), remove.multiple = FALSE, remove.loops = TRUE) xina_result <- xina_analysis(example_clusters, net_all, is_stringdb=FALSE, flag_simplify=FALSE) ## End(Not run)## Not run: # load XINA example data data(xina_example) # use the following code for utilizing up-to-date STRING DB tax_id <- 9606 # for human # tax_id <- 10090 # for mouse library(STRINGdb) library(igraph) string_db <- STRINGdb$new( version='10', species=tax_id, score_threshold=0, input_directory='' ) string_db xina_result <- xina_analysis(example_clusters, string_db, flag_simplify=FALSE) # Run XINA with a protein-protein interaction edgelist data(HPRD) net_all <- simplify(graph_from_data_frame(d=hprd_ppi, directed=FALSE), remove.multiple = FALSE, remove.loops = TRUE) xina_result <- xina_analysis(example_clusters, net_all, is_stringdb=FALSE, flag_simplify=FALSE) ## End(Not run)
Clustering multiplexed time-series omics data to find co-abundance profiles
xina_clustering(f_names, data_column, out_dir = getwd(), nClusters = 20, norm = "sum_normalization", chosen_model = "")xina_clustering(f_names, data_column, out_dir = getwd(), nClusters = 20, norm = "sum_normalization", chosen_model = "")
f_names |
A vector containing input file (.csv) paths |
data_column |
A vector containing column names (1st row of the input file) of data matrix |
out_dir |
A directory path for saving clustering results. (default: out_dir=getwd()) |
nClusters |
The number of desired maximum clusters |
norm |
Default is "sum_normalization". Sum-normalization is to divide the data matrix by row sum. If you want to know more about sum-normalization, see https://www.ncbi.nlm.nih.gov/pubmed/19861354. "zscore" is to calculate Z score for each protein. See scale. |
chosen_model |
You can choose a specific model rather than testing all the models that are available in mclust. mclustModelNames If you want k-means clustering instead of the model-based clustering, use "kmeans" here. |
a plot containing a BIC plot in current working directory and a list containing below information:
| Item | Description |
| clusters | XINA clustering results |
| aligned | XINA clustering results aligned by ID |
| data_column | Data matrix column names |
| out_dir | The directory path containing XINA results |
| nClusters | The number of clusters desired by user |
| max_cluster | The number of clusters optimized by BIC |
| chosen_model | The used covariance model for model-based clustering |
| optimal_BIC | BIC of the optimized covariance model |
| condition | Experimental conditions of the user input data |
| color_for_condition | Colors assigned to each experimental conditions which is used for condition composition plot |
| color_for_clusters | Colors assigned to each clusters which is used for XINA clustering plot |
| norm_method | Used normalization method |
# Generate random multiplexed time-series data random_data_info <- make_random_xina_data() # Data files data_files <- paste(random_data_info$conditions, ".csv", sep='') # time points of the data matrix data_column <- random_data_info$time_points # mclust requires the fixed random seed to get reproduce the clustering results set.seed(0) # Run the model-based clustering to find co-abundance profiles example_clusters <- xina_clustering(data_files, data_column=data_column, nClusters=30) # Run k-means clustering to find co-abundance profiles example_clusters <- xina_clustering(data_files, data_column=data_column, nClusters=30, chosen_model="kmeans")# Generate random multiplexed time-series data random_data_info <- make_random_xina_data() # Data files data_files <- paste(random_data_info$conditions, ".csv", sep='') # time points of the data matrix data_column <- random_data_info$time_points # mclust requires the fixed random seed to get reproduce the clustering results set.seed(0) # Run the model-based clustering to find co-abundance profiles example_clusters <- xina_clustering(data_files, data_column=data_column, nClusters=30) # Run k-means clustering to find co-abundance profiles example_clusters <- xina_clustering(data_files, data_column=data_column, nClusters=30, chosen_model="kmeans")
xina_enrichment conducts functional enrichment tests using gene ontology or KEGG pathway terms for a given protein list
xina_enrichment(string_db, protein_list, enrichment_type = "GO", pval_threshold = 0.05, methodMT = "fdr")xina_enrichment(string_db, protein_list, enrichment_type = "GO", pval_threshold = 0.05, methodMT = "fdr")
string_db |
STRINGdb object |
protein_list |
A vector of gene names to draw protein-protein interaction network. |
enrichment_type |
A functional annotation for the enrichment test. 'enrichment_type' should be one of 'GO' and 'KEGG', |
pval_threshold |
P-value threshold to get significantly enriched terms from the given proteins |
methodMT |
Method for p-value adjustment. See get_enrichment. Default is 'fdr'. |
A list of data frames containing enrichment results
## Not run: library(STRINGdb) library(Biobase) # load XINA example data data(xina_example) # Get STRING database for protein-protein intereaction information string_db <- STRINGdb$new( version="10", species=9606, score_threshold=0, input_directory="" ) string_db # XINA analysis with STRING DB xina_result <- xina_analysis(example_clusters, string_db) # Select proteins that showed cluster #1 in the Stimulus2 condition subgroup <- subset(example_clusters$aligned, Stimulus2==1) protein_list <- as.vector(subgroup$`Gene name`) # Enrichment test using KEGG pathway terms that have adjuseted p-value less than 0.1 kegg_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "KEGG", pval_threshold=0.1) plot_enrichment_results(kegg_enriched$KEGG, num_terms=10) # Enrichment test using GO terms that have adjuseted p-value less than 0.1 go_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "GO", pval_threshold=0.1) plot_enrichment_results(go_enriched$Component, num_terms=10) ## End(Not run)## Not run: library(STRINGdb) library(Biobase) # load XINA example data data(xina_example) # Get STRING database for protein-protein intereaction information string_db <- STRINGdb$new( version="10", species=9606, score_threshold=0, input_directory="" ) string_db # XINA analysis with STRING DB xina_result <- xina_analysis(example_clusters, string_db) # Select proteins that showed cluster #1 in the Stimulus2 condition subgroup <- subset(example_clusters$aligned, Stimulus2==1) protein_list <- as.vector(subgroup$`Gene name`) # Enrichment test using KEGG pathway terms that have adjuseted p-value less than 0.1 kegg_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "KEGG", pval_threshold=0.1) plot_enrichment_results(kegg_enriched$KEGG, num_terms=10) # Enrichment test using GO terms that have adjuseted p-value less than 0.1 go_enriched <- xina_enrichment(string_db, protein_list, enrichment_type = "GO", pval_threshold=0.1) plot_enrichment_results(go_enriched$Component, num_terms=10) ## End(Not run)
xina_plot_all is to draw protein-protein interaction network plots of all the clusters
xina_plot_all(xina_result, clustering_result, condition = "all", centrality_type = NULL, flag_simplify = TRUE, num_breaks = 5, layout_specified = "", vertex_label_flag = FALSE, vertex.label.color = "black", vertex.color = "", edge.color = NULL, vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", legend_location = "bottom", num_clusters_in_row = 5, flag_unknown_only = FALSE, img_size = NULL, img_qual = 300)xina_plot_all(xina_result, clustering_result, condition = "all", centrality_type = NULL, flag_simplify = TRUE, num_breaks = 5, layout_specified = "", vertex_label_flag = FALSE, vertex.label.color = "black", vertex.color = "", edge.color = NULL, vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", legend_location = "bottom", num_clusters_in_row = 5, flag_unknown_only = FALSE, img_size = NULL, img_qual = 300)
xina_result |
A list containing XINA network analysis results. See xina_analysis |
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clustering_result |
A list containing XINA clustering results. See xina_clustering |
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condition |
Default is 'all', which means use all the proteins to draw graphs. If you specify the experimental condition name used for XINA clustering, xina_plot_all will draw graphs using specific condition's proteins. |
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centrality_type |
'centrality_type' should be one of c('Degree', 'Eigenvector', 'Hub', 'Authority', 'Closeness', 'Betweenness')
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flag_simplify |
If it is TRUE (default), XINA will exclude unconnected proteins |
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num_breaks |
'num_breaks' is the number of ranks based on network centrality. Default is 5. |
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layout_specified |
This can change network layout. 'layout_specified' should be one of c('sphere', 'star', 'gem', 'tree', 'circle', 'random', 'nicely'). XINA's layouts are based on igraph's layout. See layout_
Default is 'layout_nicely' of igraph |
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vertex_label_flag |
If vertex_label_flag is TRUE (default), igraph network graphs will be labeled by gene names If vertex_label_flag is FALSE, igraph network graphs will be drawn without labels |
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vertex.label.color |
Color of labels. Default is black |
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vertex.color |
Color of nodes. Default is pink. |
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edge.color |
Color of edges. Default is pink. |
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vertex.label.dist |
Distance between node and label. Default is 0.6 |
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vertex.label.cex |
Size of labels Default is 0.8 |
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edge.arrow.size |
Size of edges Default is 0.4 |
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vertex.size |
Size of nodes Default is 10 |
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vertex.shape |
You can choose node shape. Default is 'sphere'. See shapes |
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legend_location |
If centrality_type is chosen, xina_plot_single add the color legend guiding rank of nodes based on the centrality score. Default is 'bottomright', but you can choose one of these 'bottomright', 'bottom', 'bottomleft', 'left', 'topleft', 'top', 'topright', 'right' and 'center'. |
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num_clusters_in_row |
The number of clusters in a row on the XINA network plot. Default is 5. |
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flag_unknown_only |
If this is TRUE, 'xina_plot_all' will plot proteins that do not have any protein-protein interaction in the given database |
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img_size |
Set the image size. For width=1000 and height=1500, it is img_size=c(1000,1500). Default is c(3000,3000) |
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img_qual |
Set the image resolution. Default is 300. |
PNG images of PPI network plots of all the clusters
## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # XINA network plots xina_plot_all(xina_result_example, example_clusters) # XINA network plots for Control condition xina_plot_all(xina_result_example, example_clusters, condition='Control')## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # XINA network plots xina_plot_all(xina_result_example, example_clusters) # XINA network plots for Control condition xina_plot_all(xina_result_example, example_clusters, condition='Control')
xina_plot_bycluster is to draw protein-protein interaction network plots of each cluster
xina_plot_bycluster(xina_result, clustering_result, cl = NULL, condition = "all", flag_legend = TRUE, centrality_type = NULL, flag_simplify = TRUE, layout_specified = "", vertex_label_flag = TRUE, vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", vertex.color = "", edge.color = "darkgray", legend_location = "bottom", flag_unknown_only = FALSE)xina_plot_bycluster(xina_result, clustering_result, cl = NULL, condition = "all", flag_legend = TRUE, centrality_type = NULL, flag_simplify = TRUE, layout_specified = "", vertex_label_flag = TRUE, vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", vertex.color = "", edge.color = "darkgray", legend_location = "bottom", flag_unknown_only = FALSE)
xina_result |
A list containing XINA network analysis results. See xina_analysis |
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clustering_result |
A list containing XINA clustering results. See xina_clustering |
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cl |
Cluster number in the XINA clustering results |
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condition |
Default is 'all', which means use all the proteins to draw graphs. If you specify the experimental condition name used for XINA clustering, |
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flag_legend |
If it is TRUE, a legend will be printed out together. |
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centrality_type |
'centrality_type' should be one of c('Degree', 'Eigenvector', 'Hub', 'Authority', 'Closeness', 'Betweenness')
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flag_simplify |
If it is TRUE (default), XINA will exclude unconnected proteins |
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layout_specified |
This can change network layout. 'layout_specified' should be one of c('sphere', 'star', 'gem', 'tree', 'circle', 'random', 'nicely'). XINA's layouts are based on igraph's layout. See layout_
Default is 'layout_nicely' of igraph |
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vertex_label_flag |
If vertex_label_flag is TRUE (default), igraph network graphs will be labeled by gene names If vertex_label_flag is FALSE, igraph network graphs will be drawn without labels |
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vertex.label.dist |
Distance between node and label. Default is 0.6 |
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vertex.label.cex |
Size of labels Default is 0.8 |
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edge.arrow.size |
Size of edges Default is 0.4 |
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vertex.size |
Size of nodes Default is 10 |
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vertex.shape |
You can choose node shape. Default is 'sphere'. See shapes |
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vertex.color |
Color of nodes. Default is pink. |
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edge.color |
Color of edges. Default is pink. |
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legend_location |
If centrality_type is chosen, xina_plot_single add the color legend guiding rank of nodes based on the centrality score. Default is 'bottomright', but you can choose one of these 'bottomright', 'bottom', 'bottomleft', 'left', 'topleft', 'top', 'topright', 'right' and 'center'. |
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flag_unknown_only |
If this is TRUE, 'xina_plot_bycluster' will plot proteins that do not have any protein-protein interaction in the given database |
A PNG file (XINA_Cluster_Networks.png) displaying protein-protein interaction network plots of all the clusters and a list containing XINA network analysis results
PNG images of PPI network plots of all the clusters
## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # plot cluster #1 xina_plot_bycluster(xina_result_example, example_clusters, cl=1) # plot PPI network of Control condition in cluster #1 xina_plot_bycluster(xina_result_example, example_clusters, cl=1, condition='Control')## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # plot cluster #1 xina_plot_bycluster(xina_result_example, example_clusters, cl=1) # plot PPI network of Control condition in cluster #1 xina_plot_bycluster(xina_result_example, example_clusters, cl=1, condition='Control')
xina_plot_single draws protein-protein interaction network plot for given 'protein_list'.
xina_plot_single(xina_result, protein_list, centrality_type = NULL, layout_specified = "", vertex_label_flag = TRUE, main = NULL, vertex.label.color = "black", vertex.color = NA, edge.color = "darkgray", vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", legend_location = "bottom", num_breaks = 5, digits_round_up = 5, flag_simplify = TRUE, flag_legend = TRUE)xina_plot_single(xina_result, protein_list, centrality_type = NULL, layout_specified = "", vertex_label_flag = TRUE, main = NULL, vertex.label.color = "black", vertex.color = NA, edge.color = "darkgray", vertex.label.dist = 0.6, vertex.label.cex = 0.8, edge.arrow.size = 0.4, vertex.size = 10, vertex.shape = "sphere", legend_location = "bottom", num_breaks = 5, digits_round_up = 5, flag_simplify = TRUE, flag_legend = TRUE)
xina_result |
A list containing XINA network analysis results. See xina_analysis |
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protein_list |
A vector of gene names to draw a protein-protein interaction network graph. |
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centrality_type |
'centrality_type' should be one of c('Degree', 'Eigenvector', 'Hub', 'Authority', 'Closeness', 'Betweenness')
|
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layout_specified |
This can change network layout. 'layout_specified' should be one of c('sphere', 'star', 'gem', 'tree', 'circle', 'random', 'nicely'). XINA's layouts are based on igraph's layout. See layout_
Default is 'layout_nicely' of igraph |
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vertex_label_flag |
If vertex_label_flag is TRUE (default), igraph network graphs will be labeled by gene names If vertex_label_flag is FALSE, igraph network graphs will be drawn without labels |
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main |
Title of network figure. IF it is NULL (default), it will be the number of plotted proteins |
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vertex.label.color |
Color of labels. Default is black |
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vertex.color |
Color of nodes. Default is pink. |
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edge.color |
Color of edges. Default is pink. |
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vertex.label.dist |
Distance between node and label. Default is 0.6 |
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vertex.label.cex |
Size of labels Default is 0.8 |
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edge.arrow.size |
Size of edges Default is 0.4 |
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vertex.size |
Size of nodes Default is 10 |
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vertex.shape |
You can choose node shape. Default is 'sphere'. See shapes |
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legend_location |
If centrality_type is chosen, 'xina_plot_single' adds the color legend guiding rank of nodes based on the centrality score. Default is 'bottomright', but you can choose one of these 'bottomright', 'bottom', 'bottomleft', 'left', 'topleft', 'top', 'topright', 'right' and 'center'. |
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num_breaks |
'num_breaks' is the number of ranks based on network centrality. Default is 5. |
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digits_round_up |
See Round |
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flag_simplify |
If it is TRUE (default), XINA will exclude unconnected proteins |
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flag_legend |
If it is TRUE, a legend will be printed out together. |
A PNG file (XINA_Cluster_Networks.png) displaying protein-protein interaction network plots of all the clusters and a list containing XINA network analysis results
## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # get gene names that are clustered to #21 in "Stimulus2" condition subgroup <- subset(example_clusters$aligned, Stimulus2==21) protein_list <- subgroup$`Gene name` # Calculate protein-protein interaction network xina_plot_single(xina_result_example, protein_list) # Calculate protein-protein interaction network and Eigenvector centrality eigen_info <- xina_plot_single(xina_result_example, protein_list, centrality_type='Eigenvector')## the following code is to show how it works quickly ## load XINA example data data(xina_example) ## load the previously processed XINA analysis results # if you want to learn how to run 'xina_analysis', please see \link[XINA]{xina_analysis} data(xina_result_example) # get gene names that are clustered to #21 in "Stimulus2" condition subgroup <- subset(example_clusters$aligned, Stimulus2==21) protein_list <- subgroup$`Gene name` # Calculate protein-protein interaction network xina_plot_single(xina_result_example, protein_list) # Calculate protein-protein interaction network and Eigenvector centrality eigen_info <- xina_plot_single(xina_result_example, protein_list, centrality_type='Eigenvector')
All_network. PPI network of all the input proteins
Sub_network. A list containing PPI networks of each clusters
Data. XINA clustering results. See xina_clustering
Nodes. A list of proteins in each cluster
Conditions. A list of experimental condition of proteins in each cluster
Titles. A list of plot titles for XINA plotting
out_dir. A directory path storing XINA network analysis results
is_stringdb. False = different PPI DB and TRUE = STRING DB
A data frame containing STRING protein-protein interaction data
XINA