PathON is a tool for user to analyze the on/off status of Drosophila major signaling pathways by evaluating the core components or TF downstream genes of each major signaling pathways. The annotation of these pathways was assembled by manual curation of literature. There are 3 different use cases for PathON.

1. Use Case 1: Analysis of hit list
   

   User inputs a list of genes, for example, selected hits from experiment or screening result or differentially expressing genes comparing different conditions/genome types. PathON will do the gene set enrichment based on either pathway core components or pathway specific TF downstream genes. User might also adjust the enrichment background if the experiment was done not using genome-scale reagents. The enrichment result is summarized in a table format with pvalues and up-regulated/down-regulated genes.

2. Use Case 2: Analysis of full dataset Analysis of full dataset

   User might upload the full dataset without setting any cutoff. The full datasets might come from 2 different samples, for example, the expression levels of wild type control and mutant or screening result of 2 experimental conditions. User might calculate the log2 ratio of the fold change and then upload the data. PathON will sort the data and looked for activated pathways among the genes that score at the 2 directions (up and/or down-regulated). User might also directly import the expression values/screening results using headers to specify the sample and PathON will first compute the averages if there is duplicate per sample. Secondly, PathON calculates the log2 ratio of fold changes between experimental sample and control sample, then do the enrichment analysis. If the expression values of only one sample is uploaded, PathON will compute the ratio based on the average of the full dataset then do the enrichment. The cutoff of PathON analysis can be user defined percentage (eg. 10%) or user defined log2ratio. The enrichment result is summarized in a table format with pvalues and up-regulated/down-regulated genes. In addition, user has the option to visualize the results by heatmap or network. The heatmap view shows the input data (eg. ratio of expression change) for each pathway members by color while the network view shows the PPI data of pathway members using edges with node colors reflecting user input data.

3. Use Case 3: Analysis of full RNA-seq datasets from target tissue and source tissues for cross-talk
   User can upload two different datasets from different sources and analyze the cross-talk. For example, a bulk RNA-seq dataset of gut tumor (log2 ratio of changes in tumor sample vs WT sample) and another bulk RNA-seq datasets of muscle (log2 ratio of changes in tumor sample vs WT sample). PathON can help identify the ligands that might be secreted from tumor and the signaling pathways that are activated in muscle by these ligands. The enrichment result is summarized in a table format with pvalues and up-regulated/down-regulated genes for the target tissue. The ligand expressions from source tissue and receptor expressions from target tissue are also summarized in another table. The heatmaps of pathway core components and TF target genes are provided comparing the expression in source tissue and target tissue side by side.

Input Files

• Use Case 1: both the input and background file are simply a list of genes of FBgns, one per line.
  Sample file: PathON_Hits_MAPK_Signaling_Screen.txt
  
  
• Use Case 2: two formats are supported
  
  1. 1.) Precalculated Ratios: user needs to pre-calculate the log2 ratios before uploading the file and the input is a two- column file [gene, log2ratio].
     Sample file: PathON_YK_fbgn_ratios_target.txt
  2. 2.) Reading or expression levels: user has the option to directly input the readings or expression levels of 2 conditions with single or multiple samples and PathON can calculate the ratio from your raw data before doing the analysis. To do so, user needs to submit an excel file with the column headers to specific the samples as the first line. The header must contain the following names.
     gene: fbgn or gene name (fbgn is preferred)
     control: one or more columns
     data: one or more columns
     Sample file: PathON_YK_fbgn_expression_values.xlxs
     
     
• Use Case 3:two datasets of pre-calculated ratios
  PathON_YK_fbgn_ratios_target.txt
  PathON_WS_fbgn_ratios_source.txt

Note: Excel file or txt/csv files are supported. Tab separation is preferred (comma separation also works).