Analysis Pipeline: Get pathway networks from KEGG
In our method, we choose KEGG(http://www.kegg.jp/) pathway networks as a reference to generate a Pathway Fingerprint. KEGG provides KGML files of pathways for users, which enables automatic drawing of KEGG pathways and provides facilities for computational analysis and modeling of gene/protein networks and chemical networks. We downloaded the latest (2020.11.8) KGML files of all the human pathways in KEGG and translated the KGML files to network.Then we got a total number of 338 pathway networks for further analysis.
Identification of DEGs(differentially expressed genes)
There are different methods for the different three types of data to identify DEGs. We processed the microarray data by limma, besides, we also selected some cancer samples of the same type of cancer and compared them with the control group by edgeR.In both limma and edgeR, we only chose the genes whose log2 fold change (logFC) was greater than 1 and false discovery rate (FDR) was less than 0.05.
We defined a new S4 class PFP to store the score. PFP also provides six major methods for this S4 class:
genes_score(): Gene score, adding function to specified selection group/ pathway.sub_PFP(): A portion of the PFP can be selected by group, slice, path name, and ID.show(): Display the network group name, group size, and PFP score for each channelplot_PFP(): Display PFP fingerprinting.refnet_names(): Extract base network group namesrank_PFP(): To achieve the path weight ranking, the preferred P value, and then the PFP score. Detailed instructions for this five methods refer to package function help.
We also defined a new S4 class PFPRefnet to store the reference pathway network information of KEGG, it provides six methods for this S4 class::
network(): Reference path network of KEGG.net_info(): Pathway information.group(): Group information.refnet_names(): The access information of the reference network.subnet(): A portion of the PFPRefnet can be selected by group, slice, path name, and ID.show(): Show the number of pathways in each group of the reference network.
PFP scores calculation
- Input differential gene list
- Extract the network information of KEGG base map.
- The channel fingerprint score was calculated and converted to PFP format.
Then the PFP can be calculated as following:
# load the data -- gene list of human; the PFPRefnet object
# of human; the PFP object to test; the list of different
# genes.
data("gene_list_hsa")
data("PFPRefnet_hsa")
data("PFP_test1")
data("data_std")
# Step1: calculate the similarity score of network.
PFP_test <- calc_PFP_score(genes = gene_list_hsa, PFPRefnet = PFPRefnet_hsa)
# Step2: rank the pathway by the PFP score.
rank1 <- rank_PFP(object = PFP_test, total_rank = TRUE, thresh_value = 0.5)We study the target pathway, the pathway with the highest score after ranking.Below is a simple example.
# Step1: select the max score of pathway.
pathway_select <- refnet_info(rank1)[1, "id"]
gene_test <- pathways_score(rank1)$genes_score[[pathway_select]]$ENTREZID
# Step2: get the correlation coefficient score of the edge.
edges_coexp <- get_exp_cor_edges(gene_test, data_std)
# Step3: Find the difference genes that are of focus.
gene_list2 <- unique(c(edges_coexp$source, edges_coexp$target))
# Step4: Find the edge to focus on.
edges_kegg <- get_bg_related_kegg(gene_list2, PFPRefnet = PFPRefnet_hsa,
rm_duplicated = TRUE)
# Step5: Find the associated network
require(org.Hs.eg.db)
net_test <- get_asso_net(edges_coexp = edges_coexp, edges_kegg = edges_kegg,
if_symbol = TRUE, gene_info_db = org.Hs.eg.db)Network fingerprint visualization
PFP provides the plot_PFP() function to visualize the network fingerprint of a single query network. First we show an example of PFP score.
plot_PFP(PFP_test)
Plot the scores from high to low.
plot_PFP(rank1)