<p>A few points:</p>
<p>Firstly, VScode isn't comparable to R. The former is a text editor with a lot of available third-party plugins. The latter is a programming language for statistical analysis. For all stats, I would strongly suggest learning how to use R, and there are even some DEG analysis libraries in R.</p>
<p>Secondly, it sounds like you got a gene expression matrix. Usually, this has transcripts as rows and samples as columns, though yours sounds transposed. You might have to refer to the original data accessions to identify what the numbers are. It could be standardised values like TPM, or could be raw counts. These matrices are often used as input for differential expression analyses, or how to report their output.</p>
<p>Ideally, you'll be looking for a dataset with a standard output that you could process with an R library like <code>DESeq2</code>. This is commonly used for RNA-seq analyses. <a href="https://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html" rel="nofollow noreferrer">Here</a> is there tutorial on how to process data.</p>

CC BY-SA 4.0
2024-09-30T13:19:49.853Z
2024-09-30T13:19:49.853Z
dthorbur
<p>I'm a Biotech student and I've been trying to solve this problem since over a year now for a research project, basically we identified common and unique genes for a cancer subtype by first using GEO2R followed by applying filters for them in excel then copy pasting the filtered gene column into biovenn software. A senior/supervisor pointed out that one of the datasets has some issues, so we basically have to scrap this and start again using better and newer datasets. I have received suggestions from other seniors to use R or VS code. I thought VS code might be more suitable for me because I had some background in python. I got up to the point where we loaded a sample dataset into data wrangler but we're at a loss as to what to do from here. I expect to see columns for subtype, gene, logfc, expected p values, etc but what I see is a column heading having each gene from the datasets and row headers having all the cancer subtypes with only numbers in the matrix. This got me very confused and no matter where I look up to I'm not getting any relevant information to solve my queries. Our supervisor is also expecting us to use these genes to find out the (aberrant) glycosylation profile of their respective proteins and compare this to the normal glycosylation patterns. Can someone please help me out with these two issues?</p>

CC BY-SA 4.0
2024-09-28T23:06:29.923Z
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
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<r><gene-expression><cancer><genetics><public-dataset>
How do I compare multiple cancer datasets to find the common and unique genes and difference in gene expression among them?
# How do I compare multiple cancer datasets to find the common and unique genes and difference in gene expression among them?

(Asked by: username_0 on 2024-09-28)

I'm a Biotech student and I've been trying to solve this problem since over a year now for a research project, basically we identified common and unique genes for a cancer subtype by first using GEO2R followed by applying filters for them in excel then copy pasting the filtered gene column into biovenn software. A senior/supervisor pointed out that one of the datasets has some issues, so we basically have to scrap this and start again using better and newer datasets. I have received suggestions from other seniors to use R or VS code. I thought VS code might be more suitable for me because I had some background in python. I got up to the point where we loaded a sample dataset into data wrangler but we're at a loss as to what to do from here. I expect to see columns for subtype, gene, logfc, expected p values, etc but what I see is a column heading having each gene from the datasets and row headers having all the cancer subtypes with only numbers in the matrix. This got me very confused and no matter where I look up to I'm not getting any relevant information to solve my queries. Our supervisor is also expecting us to use these genes to find out the (aberrant) glycosylation profile of their respective proteins and compare this to the normal glycosylation patterns. Can someone please help me out with these two issues?

**Tags:** r, gene-expression, cancer, genetics, public-dataset
> **Comments:**
> **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  

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## 1 answers

### Answer by username_2 on 2024-09-30. Score: 2

A few points:

Firstly, VScode isn't comparable to R. The former is a text editor with a lot of available third-party plugins. The latter is a programming language for statistical analysis. For all stats, I would strongly suggest learning how to use R, and there are even some DEG analysis libraries in R.

Secondly, it sounds like you got a gene expression matrix. Usually, this has transcripts as rows and samples as columns, though yours sounds transposed. You might have to refer to the original data accessions to identify what the numbers are. It could be standardised values like TPM, or could be raw counts. These matrices are often used as input for differential expression analyses, or how to report their output.

Ideally, you'll be looking for a dataset with a standard output that you could process with an R library like `DESeq2`. This is commonly used for RNA-seq analyses. [Here](https://www.bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) is there tutorial on how to process data.


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