Hi Shradheya R.R. Gupta , getting to pathway analysis requires a few preceding steps related to the analysis of your raw expression data (shown above). For scientists who (1) would like to reanalyze a GEO dataset and (2) are not (yet) familiar with writing code, I'd suggest using "GEO2R" within NCBI: https://www.ncbi.nlm.nih.gov/geo/geo2r/. You need the GEO Accession (e.g. "GSE#####"). Once here, you will need to compute differential expression based on the biological comparison of interest. Then, you should download the data, apply a statistical (+/- fold-change cutoff), then use a pathway database for gene-set enrichment analysis. For this, I would recommend enrichR (https://maayanlab.cloud/Enrichr/), which curates a variety of databases.

I hope this helps, and let me know if you get stuck.

Thank you very much Mark Pepin Sir,

I want to work on GSE60486 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60486). However, GEO2R detects errors in it. The GSE60486 has reanalyzed around 1000 GSM datasets together and made a single dataset.

The dataset I have has around 1000 columns headed by GSM ID's (Image).

I've attached a dataset link with 149 columns to this post.

(36 MB .csv file)

https://drive.google.com/file/d/1Yz2-1JO_11oo-yfCJm_lTIPDKgn3Q5iG/view?usp=sharing

May you help me by suggesting what the data is like and what other files I may need, as I have this dataset only.

Thank you.

For starters, you will need an index (to unblind each sample) to compute the differential expression. You can then use this to create a model matrix (i.e. using "Limma" to do the Diff-ex). I'm afraid that the analysis of this particular meta-analysis will require coding in R.

Alternatively, you can analyze one of the datasets used in this meta-dataset (e.g. GSE1643) using the GEO2R feature.

Sir can you share a tutorial or workbook or something like that. I can work in R, Python and Linux.

Thanks