Thanks for the information. How to visulaize CNV amplification data like how can i add amplification status like Amplification or Loss inside the input file? and amplificaation status want to see along with SNV.
Very informative. I have installed IBM CPLEX optimization Studio 22.1.1 (academia) in windows 11 machine. And I have R 4.3.1 . I have tried to link CPLEX in R studio but could not able to make it. If it is possible, could you please make a video how to link CPLEX with R ? I tried different solutions from stackoverflow or from Chatgpt but could not able to make it. There is not much working solution as well. Please can u upload the tutorial regarding this ? Thanks in advance :)
Thanks for the video, it is really useful!! It would have been nice if you showed the imported count_table like you did with the taxonomy file because you used a final.full.count_table that is not produced with the Mothur SOP, is it?? I think a few of us are struggling with the comment lines in the count_table in mothur. Any advice is greatly appreciated 🙃
It would be nice to know where some of the functions you are using are coming from (without having to visit github). I cannot find locf, nobc or forbak in nomemica. I checked the zoo package. It does not have those but similar ones (na.locf for both LOCF and NOBC).
Nice presentation. However, I find difficult to find a good account of the difference between the different classes of missings (MCAR, MAR, MNAR). After reading the description of these types of classes by different youtubers I am just left a loss. Perhaps no one can explain these things?
I have been trying to figure out this part of my analysis for so long and was struggling with where to even begin but this video was perfect! So easy to understand and straight to the point, you're amazing. Thank you!
when it comes to dealing with data science, python have most of modeling in Sklearn. You need pandas only to deal with data frames. In R there are 600 ways to open csv file! Also I find the RDS format in R extremely confusing. Agree that RStudio is more established and easier to work with. Jupyter Lab needs way more development to reach RStudio functionality.
I wonder if can upgrade the codes making it possible to use more CPU or GPU to analyse in order to reduce the use of memories.Because i found that excpet memories were highly used,the CPU and GPU were almost unused with the only 20%to30% occupying rates
Hello, could you please help me, I used this code to fit the trendline of my data, but the line did not show "> abline(lm(Rawdata$Elevation~Rawdata$Bulk, data=Rawdata))". What could be the possible problem? Your video has been helpful as I am a new user of R. I have successfully implemented your tips until this point. Thanks
Thank you so much for the explanation! Could you elaborate more about p adjusted value at 17:45 when you mentioned that it is for multiple gene comparison? Specifically, how does it relate to multiple gene comparisons? Does it account for the high number of differentially expressed genes we found?
what can we do when this happens: > summary(exprs(gse)) GSM1480987 GSM1480988 GSM1480989 GSM1480990 GSM1480991 GSM1480992 Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical Mode:logical ? Thank you for your video!!!!
why Module eigenegene is called first conponent of PCA and why this First component is required in WGCNA. Average gene expression is of a module is not enough for ME caluculation.
Hi, there were a few errors in the code which I can't find solutions to online: Error in read_delim("final.full.count_table", delim = "\t", escape_double = FALSE, : could not find function "read_delim" Warning in install.packages : package ‘ggtree’ is not available for this version of R Warning in install.packages : package ‘treeio’ is not available for this version of R If anyone has experience with these issues, please comment