iPathwayGuide™ Frequently-Asked Questions
All six tabs draw on the same gene set and interaction database. Four are pure layout changes with no filtering:
- Subnetworks — groups genes into connected components to show whether the DE list forms coherent modules.
- Hub Genes — positions the most-connected genes at the center and isolated genes on the outer ring.
- Interactions — uses a circular layout ordered clockwise by absolute log2 fold change, where chord length reflects how similarly two interacting genes are changing.
- Regulators — emphasizes directed regulator→target relationships.
Two tabs apply a filter:
- Mechanisms — keeps the minimum set of interactions needed to connect the genes, selecting edges by evidence score and directional coherence. This is the most defensible view for a mechanism claim, since every edge shown survived an evidence filter.
- Gatekeepers — isolates bottleneck genes: the only route between otherwise separate parts of the network. Useful for identifying which single gene, if removed, would disconnect the response.
Because GO is a hierarchy, a significant specific term automatically makes its parent terms appear significant too. HSP and SCDP both correct for this redundancy, but in opposite directions. HSP strips the driving genes out of a significant term’s ancestors before testing them — only the most specific term in each branch survives, unless a parent has independent evidence of its own. SCDP does the reverse: it collapses a cluster of significant sibling terms up into the least general ancestor that covers them all. This is why the same term can rank near the top in one method and disappear entirely in the other — the underlying biology hasn’t changed, only how credit for the DE genes is distributed between general and specific terms. A term’s p-value is computed from a different gene set under each scheme, which reorders the results.
Recommendation: For most analyses, HSP is recommended as it surfaces the most specific biological enrichments.
To change the name of the individual contrasts within a meta-analysis, you must first open the report and navigate to the “Summary” page. At the top of that page, below the banner with various modules, you should see a window with the name of your meta-analysis. Click on the name of your analysis, and that window will expand and give you the option to edit the names of your individual contrasts, as well as the report title and report description. Please note this will only work on meta-analyses you own, if the report was created by someone else and shared with you, the report’s creator must be the one to edit the titles.
The report is deprecated” message is there to inform you that the report is not based on the latest knowledge available. If you choose to update the report, the analysis is done again, from scratch, with the latest version of all annotations. If you do that, your new, re-analyzed results will reflect the most up-to-date knowledge. We recommend that you update your analysis results for all the data sets that you care about or work on. If you choose not to update, you can have access to the deprecated report by clicking on the little “i” symbol (for information) on the very right of the report. That will open a window showing you all previous versions of your analysis results. You can access any of them by clicking on the date.
You can find a more detailed explanation with screen captures in our iPathwayGuide™ FAQ under “How do I find a deprecated analysis?”
- Prepare a list of genes with a header “Symbol”, in one single column. Please use HGNC/MGI symbols only.
- You can use in-built reference background gene set, or a custom background gene set for enrichment analysis
- If you opt for enrichment with custom background gene set, prepare of list of background genes, with a header “Symbol”, in one single column. Please use HGNC/MGI symbols only.
- In user dashboard, click on “Analyze a new experiment”
- Intake form loads and in intake form, select “Only my genes of interest/DE genes” under “What data would you like to analyze” drop down menu
- Select appropriate organism and Data type
- Upload the input file by clicking on “Choose file” and select file prepared in step 1
- Uploaded data is displayed in file content preview.
- Under “Gene symbol” column, select the column with genes (gene symbols) with header “Symbol”
- Click on “select the reference type” drop down menu
- If you want to use custom background, follow steps 13 to 16. If you want to use an in-built reference gene set, follow step 12.
- To use the in-built reference gene set, click on “all genes/proteins in the transcriptome/proteome” available under “select the reference type” drop down menu and skip steps 13-16.
- To use custom background, click on “a custom set of genes” available under “select the reference type” drop down menu.
- Click on “Choose file” available under “Upload reference file (Mandatory)” to upload custom background gene set. Upload file prepared in step 3.
- Uploaded data is displayed in file content preview.
- Under “Gene symbol” column, select the column with genes with header “Symbol”
- Click on upload and proceed with analysis.
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.
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.
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.
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 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.
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.
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.
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.
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.
Question: I went back to analyses that I had previously paid for, opened them and a warning came up for indicated that the analyses were “deprecated” so I hit the re-analyze button only to find that the files are now locked and require payment to open. What is going on and how do I access the analysis I already paid for?
We periodically update our knowledge base with the latest annotations and references. This is necessary in order to offer our users an analysis that uses the most recent knowledge available. This in fact, is one of the main value points that we provide to our users. Just to give you an idea, the sheer data available now is approximatively 4 times larger than what we had 18 months ago. Furthermore, brand new analysis capabilities are added all the time. For instance, during the past year alone we added an extremely powerful network analysis which is able to discover new mechanisms, as well as a very sophisticated upstream regulator analysis. If you have not seen these yet, it would be worth your time to schedule a 30 min phone call for us to show these to you.
When new knowledge becomes available, our platform lets you know by marking your existing analyses as “deprecated”. Thus, you are aware that they are not based on the latest knowledge available in the field. It is better to find this out from us, rather than from the reviewers of your grant or paper pointing out that your analysis does not mention such and such recent discovery. You original analysis is still fully available to you. You can simply close the warning messages at the top by clicking the X symbol at the top left, immediately to the left of the analysis title. Every single piece of information will still be there, as you saw it when the analysis was first performed.
When new knowledge or new analysis capabilities become available, you may or may not choose to analyze the data again, using the latest knowledge and capabilities available. Please keep in mind that updating an existing analysis involves a resubmission of the original data and a complete re-analysis of that data from scratch. If you choose do resubmit the data for a new analysis and you have a valid subscription, this additional analysis will incur no additional cost but it will count as part of your analyses. However, if your subscription has expired, you will need to either renew it or pay for the individual analyses that you wish to have done.
Here is a blog comparing the single analysis vs. subscription usage: https://advaitabio.com/ipathwayguide/subscription-vs-purchasing-individual-analyses-which-one-do-i-want/
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.
Here are the legends describing the symbols used on the pathway diagrams, gene networks and GO directed acyclic graph, respectively. Click an image for a larger view.
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
- Microsoft Edge
- 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:
– Functional analysis [Target analysis/Impact analysis/Molecular Signature analysis/Gene Ontology Term Enrichment analysis/Upstream gene analysis etc] was done using iPathwayGuide™TM (AdvaitaBio Corporation, https://ipathwayguide.advaitabio.com/) [Reference 1 and Reference 2].
– DEGs were analysed using iPathwayGuide™TM (AdvaitaBio Corporation, https://ipathwayguide.advaitabio.com/)[Reference 1 and Reference 2].
– The [Chord Diagrams, Pathway Image, Network image etc.] were generated using iPathwayGuide™TM (AdvaitaBio Corporation, https://ipathwayguide.advaitabio.com/)[Reference 1 and Reference 2].
References:
- Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R. A systems biology approach for pathway level analysis. Genome Res. 2007 Oct;17(10):1537-45. doi: 10.1101/gr.6202607. Epub 2007 Sep 4. PMID: 17785539; PMCID: PMC1987343.
- Donato M, Xu Z, Tomoiaga A, Granneman JG, Mackenzie RG, Bao R, Than NG, Westfall PH, Romero R, Draghici S. Analysis and correction of crosstalk effects in pathway analysis. Genome Res. 2013 Nov;23(11):1885-93. doi: 10.1101/gr.153551.112. Epub 2013 Aug 9. PMID: 23934932; PMCID: PMC3814888.
If you need additional explanation on how the methods work or if you ever have any scientific questions from the reviewers about the analysis, we will be happy to help preparing the response to reviewers.
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™.