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6

You can't cbind a bunch of obscure object types. If you want merged count tables you should do this: mdat <- do.call(cbind,lapply(dat,assay)) Where row.names are Ensembl gene IDs and col.names are the SRR accessions. Then run your table writing command. - If you want the coordinates of your genes then do this to make a bed with the genomic locations:...


2

Try running this: source("https://bioconductor.org/biocLite.R") biocLite("hdf5r") Just a different method of installing packages besides github. It is more centred around bioinformatics packages as well so it is pretty useful.


2

It seems RStudio server doesn't know it's limitations. Currently, Projects are limited to 1GB of RAM. Using the R package ulimit which mirrors the function of ulimit and allows implementation of memory limits in linux I was able to set my own limitations within the R session. After setting the limit to 1GB I see the ulimit limitation being tripped when ...


2

To do so one workaround it to have your data in "long format" and then use the column that holds the "gene names" as the x variable while plotting. You can use FetchData() to extract data from a Seurat object. VlnPlot's default is the data slot (of the active assay if using Seurat v3 I suppose). And you can specify which cells and genes to retrieve. ...


2

A different approach if you are using Seurat3, is DietSeurat(). It allows you to diet the object by removing the components that you don't need. For example you can keep the normalised/scaled matrix and remove the raw counts. This approach could reduce space and memory usage, while keeping all your genes in place.


1

You can simply use subset function in R and use the code given below, new_dataframe <- subset(name_of_your_dataframe,Column1 == 1 && Column2 == "T1") Please keep in mind that R is case sensitive and you should be careful while using the variable names.


1

The Dockerfile is generated by galaxy-utils, so I'm not entirely sure what it looks like. However you should be able to generate such a "mulled" container as documented here. In short, something of the form: mulled-build build 'bioconductor-csaw=someversion--somebuildnumber' 'someotherpackage=someversion--somebuildnumber' ... That probably ...


1

If you haven't already done so I would make a complete installation of Anaconda, particularly good with bash. The I would either install using Anaconda Navigator, or ... # Create an R environment and activate it conda create -n r_env r-essentials r-base python=3.7 conda activate r_env # install Reurat conda install -c bioconda r-seurat # installing ...


1

Velocyto.R I'm not sure that your issues is from RStudio cloud but more about how you are trying to open the file. The file that you are trying to import is not a velocyto file but a .loom file that can be open using the velocyto.R package. I'm not very familiar with this kind of of .loom file but on the velocyto.R tutorial, it is specified to do the ...


1

This seems to be an encoding problem (certain characters not being recognized), see this, this and also this. Probably the file "/cloud/project/SCG71.loom" is encoded with a format that is not included in your "locale". Check the output of Sys.getlocale() and see if your input file's encoding is listed. If not, you will need to try something like Sys....


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Is your memory usage low enough that you can initially read everything in and then filter later? If so you could read the Seurat object in like normal and then use subset against a vector of your gene names. subsample <- subset(seurat_obj, features = my_genes)


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From the UniProt.ws manual: columns shows which kinds of data can be returned for the UniProt.ws object. So, you will need to go over the output of columns(your_uniprotws_object) if it includes the data type you are looking for. And here is a related question.


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Do you have a small sample of the proteins you are looking for and what you have tried so far in R using Uniprot.ws ? (including output of it), it could be helpful to identify your problem and how to solve it. Personally, I would rather consider Bioconductor as a platform for providing R packages dedicated to bioinformatics than a package to analyze data (...


1

To make a confusion matrix of 4 lists against 4 other lists, you need to get the length of all intersecting genes for each pair. Here an example with letters instead of gene names. set.seed(11) list1_up <- sample(letters[1:26], 20) list1_down <- sample(letters[1:26], 20) list2_up <- sample(letters[1:26], 20) list2_down <- sample(letters[1:26], ...


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