iPathwayGuide Frequently-Asked Questions
When one shares a report with somebody else, the recipient gets a link and they will be able to access the report and do everything that the owner can do. The owner has the opportunity to decide whether the recipient is allowed to share the report further. Once the recipient clicks on the link they will be sent to the application to accept the share; they will then go into their account and click the accept share button. Once the link has been used it cannot be reused. So if a recipient of a share forwards the email to somebody else, the second person will not be able to see the report. This is by design so data cannot be shared without the permission of the owner. Also, if one clicks on the same link more than once, the second time, the link will not work.
On your dashboard, you can click on the little shopping cart and then click on “purchase with balance.” The report will become available right away.
On the Pathways tab, on the right hand side, “pathway details” then “Gene table” then the “+” sign. When you click on that, you will see a table with the genes on the pathway. there you can sort, search for specific genes, etc. You can also download the entire table with the usual download icon.
When you change the p-value, the number of differentially expressed genes will change. That in turn, will change every single results in every single type of analysis performed: GO terms, pathway, upstream regulator, drugs, mechanisms, etc. The results will be an entirely different from the results before. Hence, this will be a new analysis. A good thing to do is to analyze your data with a couple of different thresholds and then do a meta-analysis of the results. this way, you will be able to tell whether your conclusions change depending on the threshold used. Obviously, you would place more trust in findings that remain the same regarding of the threshold used.
The answer here is very similar to the one above. When you change the organism, the annotations will be different. This would be a totally different experiment basically. This is an additional reason for which this has to be a different analysis.
iPathwayGuide supports directly human, mouse and rat. For other organisms, you need to first map your genes to orthologs. Many of our users have done this very successfully.
Here is an example in which iPathwayGuide was used to make important discoveries involving mackerel and sardines:
When you complete and publish your research, please share it with us. We will be happy to make your research more visible by sharing it with our users.
The basics are always the same regarding of the assay. The first step is to map the results of your assays onto genes. The assay can be very different but in most cases you can map the results on genes. For instance, for a methylation assay, you could associate each differentially methylated region to the gene immediately downstream of it. There will be some that won’t be mapped, but most areas that have an effect on transcriptional control are expected to be mapped correctly. Then you calculate an effect size. that can be a log fold change of differential expression or differential protein abundance or methylation, etc. It is often possible to calculate a p-value, as well. Once you have genes, effect sizes, and p-values, you can upload this in iPathwayGuide. You should specify the type of assay the data came from so you can do a meta-analysis later integrating multiple types of data, if those additional data become available.
Regarding the figures, you need to download them in .SVG format from iPathwayGuide. Any downloadable figure in our software can be downloaded as SVG. The SVG is a scalable vector format that can scale to any size and offers perfect resolution. Since we are talking about images, please keep in mind that you can customize most figures. In some, you can even select the colors. In many, you can select which genes to include and in which order. Please contact us if you have any questions about these capabilities or if you want to have a short meeting to show you how to do any such customization.
Of course, you are allowed to publish any and all figures from our software. That’s the whole point. The software is meant to create figures and give you results that can be directly published so you can save time and be more productive.
We would kindly ask that you either maintain the “(c) Advaita Corporation 2022” in the figure, or mention in the caption something like “Figure obtained with iPathwayGuide (AdvaitaBio)”. Just mentioning the software in the Methods would not be sufficient since it would not be clear what figures where produced with it.
I would like to take this opportunity to remind you a couple of things:
- we are here to help if you need any help with the Methods section or even if you have any questions from the reviewers about anything to do with our analysis.
- many figures can be customized in terms of content, order, etc. Some figures can be customized for colors, as well. Please let us know if you want us to show you how to do this.
- once the paper is published, if you let us know, we may be able to highlight your research in a short piece that we can send to our mailing list. That will greatly increase the visibility of your research.
Here is a step-by-step guide to find your old analysis:
- Click on the little “i” icon to the right of your file. That “i” stands for information:
2. In the pop-up window, select the previous version you wish to open and click on the date. That will open that particular set of analysis results:
Yes. Please download it here.
iPathwayGuide supports analysis of Human, mouse, and rat. It supports the following files formats:
CuffDiff
DESeq
EdgeR
SAS/JMP Genomics
nSolver (NanoString Technologies)
Generic tab delimited .txt file (must contain gene symbol or uniprot ID, log2FC, p-value)
SCIEX SWATH 2.0 proteomics data files
Select Affymetrix CEL files*
*Supported Affy CEL Files may take several minutes to upload
Human
Human Genome U133
Human Genome U133A 2.0
Human Genome U133 Plus 2.0
Human Genome U95
Human Genome U35K
Mouse
Mouse Expression Set 430
Mouse Expression Set 430 2.0
Mouse Genome 430A 2.0
Rat
Rat Expression Set 230
Rat Genome 230 2.0
Rat Genome U34
Please make sure your are using the “…gene_exp.diff” file that comes from CuffLinks. There are some applications that claim to emulate CuffDiff output (e.g. Galaxy). If you are using one of these applications, please make sure the output file has all columns populated. See below for specific columns that must be present. Also, use this link to view the Cuffdiff manual.
Column number | Column name | Example | Description |
---|---|---|---|
1 | Tested id | XLOC_000001 | A unique identifier describing the transcipt, gene, primary transcript, or CDS being tested |
2 | gene | Lypla1 | The gene_name(s) or gene_id(s) being tested |
3 | locus | chr1:4797771-4835363 | Genomic coordinates for easy browsing to the genes or transcripts being tested. |
4 | sample 1 | Liver | Label (or number if no labels provided) of the first sample being tested |
5 | sample 2 | Brain | Label (or number if no labels provided) of the second sample being tested |
6 | Test status | NOTEST | Can be one of OK (test successful), NOTEST (not enough alignments for testing), LOWDATA (too complex or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or other numerical exception prevents testing. |
7 | FPKMx | 8.01089 | FPKM of the gene in sample x |
8 | FPKMy | 8.551545 | FPKM of the gene in sample y |
9 | log2(FPKMy/FPKMx) | 0.06531 | The (base 2) log of the fold change y/x |
10 | test stat | 0.860902 | The value of the test statistic used to compute significance of the observed change in FPKM |
11 | p | value 0.389292 | The uncorrected p-value of the test statistic |
12 | q | value 0.985216 | The FDR-adjusted p-value of the test statistic |
13 | significant | no | Can be either “yes” or “no”, depending on whether p is greater then the FDR after Benjamini-Hochberg correction for multiple-testing |
Generally, each analysis takes about 15 minutes to complete. If there are other analyses queued ahead of yours, it may take a bit longer. You will receive an email as soon as the analysis is complete.
It is a great idea to use the software and explore the capabilities before making a purchase. The way to do this is to create a free account on our web site www.advaitabio.com. Once you create an account, you will have full access to several analysis results. These datasets cover a range of experiment types, conditions, and analysis outcomes. You will be able to fully use the software to explore these data sets in whichever way you choose. You will have full access to interact with all of the demo analyses and experience all of the features of iPathwayGuide. We also provide sample data files, which will show you some of the formats that can be used to upload data for analysis. An alternative would be to setup a time and let one of us to show you around the software. You will learn more in a much shorter time, and you will also be able to get immediate answers to any questions you may have.
Yes! From the dashboard, just click share on any completed report. Then enter the email address for the person you wish to share it with. If they do not have an account, they will be prompted to create on. Once registered, they will be able to view the report.

We report the ‘Creation Time’ based on Coordinated Universal Time (UTC).
iPathwayGuide is designed to work with all the latest major browser platforms:
- Google Chrome
- Mozilla Firefox
- Apple Safari (Mac only, iOS not supported yet)
- Microsoft Internet Explorer 11 – Some image download capabilities may not function
Yes. From the login menu, click reset password. You will receive an email with the new password.
A list of databases and versions is available from within each report. See our Release Notes to see the latest data.
Citing iPathwayGuide
Using Advaita Bio’s products or content for any form of publication (e.g. print, electronically) requires researchers to cite them. Please use one of the options below for citations:
“The Data (significantly impacted pathways, biological processes, molecular interactions, miRNAs, SNPs, etc.) were analyzed using Advaita Bio’s iPathwayGuide (Draghici,2007, Donato, 2013).
A systems biology approach for pathway level analysis
S Draghici, P Khatri, AL Tarca, K Amin, A Done, C Voichita, C Georgescu, …
https://genome.cshlp.org/content/17/10/1537.short
Genome research 17 (10), 1537-1545, Analysis and correction of crosstalk effects in pathway analysis
M Donato, Z Xu, A Tomoiaga, JG Granneman, RG MacKenzie, R Bao, …
Genome research 23 (11), 1885-1893
https://pubmed.ncbi.nlm.nih.gov/23934932/
GEO2R does not perform normalization for Affymetrix CEL files. The Advaita iPathwayGuide CEL file uploader currently utilizes the Gene-chip Robust Multi-array Average (GCRMA) normalization method. As such, there can be discrepancies between CEL files processed with GEO2R vs. iPathwayGuide.