# Conduct Network analysis A convenient and fast network analysis function, with output results suitable for cytoscape and gephi ```r network_analysis2( input, inputtype, n, threshold, method = "spearman", display = TRUE, input2, input2type ) ``` ## Arguments - `input`: Input dataframe with otu/gene/taxa in row and sample ID in column,at least 5 replicates(more than 8 replicates are recommened). - `inputtype`: Input dataframe type 1:dataframe with first column of OTUID and last column of taxonomy 2:dataframe with first column of OTUID/taxonomy 3:dataframe of all numeric - `n`: Only keep otu/gene/taxa appearing in n sample size - `threshold`: Threshold of correlation r value - `method`: A character string indicating which correlation coefficient is to be computed. One of "pearson" or "spearman" - `display`: If display a preview plot of network based on igraph. FALSE for the first attempt is recommended in case of too many vertices and edges. - `input2`: A second input data frame with otu/gene/taxa in row and sample ID in column. Default:NULL - `input2type`: The second input data frame type. Details the same as above. Default:NULL ## Returns One list contains a statistics table of network vertices/nodes and an adjacency table. One preview plot of network in the plot interface and an igraph object(named `igraph1`) in global environment. ## Details 1. We had optimized the correlation algorithm to achieve a faster running speed. It takes less than 2 minute to calculate dataframe correlation and p value which more than 400 samples and 10000 OTUs for computer with dual Core i5 processor. However, too many vertices(\>2000) or links(\>10000) may slow the statistical process and visualization,so we recommend that in your first attempt,set `display` paramter as `F` to have a preview. Then you can adjust your n/threshold/method paramter to generate a suitable visualization network 2. We display a preview plot so as to adjusting your network. Generally a global figure (like we show in examples) with less than 1000 vertices and 5000 edges/links is recommended. Further more,we recommend you to output the statistics and adjacency table and use software like cytoscape or gephi for better visualization. ## Note 1. Replicates should be at least 5,more than 8 is recommend. 2. In case of too many edges/links or not a global network plot, you can stop the process immediately to provent wasting too much time. ## Examples ```r { ### Data preparation ### data(testotu) rownames(testotu) <- testotu[, 1] inputotu <- testotu[, -c(1, ncol(testotu))] head(inputotu) set.seed(999) ### One input network analysis ### network_result <- network_analysis2( inputotu, 3, 10, 0.9, "spearman", TRUE ) # Nodes information table network_nodes <- network_result$Nodes_info head(network_nodes) # Adjacency table network_adjacency <- network_result$Adjacency_column_table head(network_adjacency) # Complete adjacency matrix network_matrix <- network_result$Adjacency_matrix print(network_matrix[1:10, 1:10]) # igraph object igraph_object <- network_result$Igraph_object network_stat(igraph_object) # In case you want to see statistics again # or do other analysis based on igraph. ### Two inputs network analysis ### inputotu1 <- inputotu[1:456, ] inputotu2 <- inputotu[524:975, ] network_result <- network_analysis2( input = inputotu1, inputtype = 3, input2 = inputotu2, input2type = 3, n = 10, threshold = 0.85, method = "spearman", display = TRUE ) #### Incorrect demonstration !! ### { network_result <- safe_run(network_analysis2(inputotu, 3, 3, 0.8, "spearman", TRUE)) } # Total edges/links: 10199 # Total vertices: 826 # Too many edges and not a global network } ``` ## Author(s) Wang Ningqi [2434066068@qq.com](mailto:2434066068@qq.com)

network_analysis2 function