This is literally exactly the resource I was looking for several months ago. Glad to finally have it now. It's so nice to have example code and clear explanation.
Thanks so much. This video is really useful. Besides, how can we prepare data to combine different factors to draw forest plot or to construct risk models? Could you please share this similar R code? Thanks again!
Greetings Miss Khusbu! Again a powerful video and it was really comprehensive. I have one question and will appreciate your guidance on it. If we perform survival analysis on an RNA-seq data from TCGA, and let’s say the expression matrix has 20K genes and 200 patients. After survival analysis I found 30 genes that has significant survival difference. So I want to pursue further and perform a multivariate cox regression of these 30 genes. Now my confusion is that what expression matrix we should use in multivariate cox model. Should we reduce initial expression matrix to only 30 genes as variables(columns) and 200 patients (as rows) or should we use the original expression matrix (having 20K genes and 200 patients and only put 30 genes in the cox equation : coxph(Surv(time, event) ~ gene1+ gene2 + gene3..+ gene 30 , data)). Will highly appreciate your comment on that. Thanks and keep doing the great work.
I don't recommend to reduce the matrix to 30 genes. You should use the entire dataset and provide 30 genes in cox equation. Also, check for multicollinearity between 30 genes, as correlations between genes can cause instability in model estimates. If collinearity is found, you should use feature selection methods to include most relevant and independent predictors in the model.
Thank you so much for your amazing content. I just wanted to know how I could extract the TCGA counts for some non-coding regions specified in a bed file. Suggestions would be really helpful. Thanks!
Thank you very much for the excellent presentation. I am relatively new to TCGA-based R analysis. I was wondering if I can apply the same process to plot survival curves for a particular mutation using SNV data, such as the effect of BRCA1 mutation on the overall survival of ovarian cancer patients. Are there any significant changes that I need to make in the workflow to achieve this?
Thank you for this very helpful video. If I want to know correlation (pearson R-value) between some genes in TCGA-Breast Cancer , do I have to use fpkm_unstrand? Could you make video about this? Again, I really appreciate your effort!!
Hello didi .. I need to talk to you .. can you pls send ur contact details .. it's about my current project .. i have some questions based on bioinformatics
I chose unstranded data for demonstration purposes. If your data is generated using a stranded protocol, you should choose stranded or reverse stranded accordingly.