rna | test runner for modern modules | Runtime Evironment library

Python 3 treats strings of bytes as a different object from strings of characters. The distinction matters, since a given string of characters can be encoded to bytes in different ways. E.g. in UTF-8, ä is the two bytes c3 a4 (in hex), while in ISO-8859-1 (Latin 1), it's just the single byte e4.

Like the comment from @Theophrastus says, subprocess.check_output() returns bytes, matching the low-level API. You need to decode it to characters based on what the expected encoding is. E.g.

If I understand what you're doing correctly, your data looks a bit like this:

I encountered the same issue. The problem was in duplicated IDs in my GTF file. Removing those duplicates solved the issue. See the discussion on Cellranger GitHub: https://github.com/10XGenomics/cellranger/issues/125

If we want to name the list, loop over the list, extract the first element of 'expected_count' ('nm1') and use that to assign the names of the list

Elya, you can simplify what you want to do. Most importantly, you do not need to loop over your data.
I assume your example data is representative for your dataset. In particular, the information in other is separated by a semi-colon. In this case you can split the string by it and extract from your 3rd column the part required to add to the "gene_id" fragment in your 2nd column. Based on your comment I append a solution for multiple hits in the other column.

Note: the following names these columns "info", "gene_id", and "other". Make sure to adapt the variable naming.

data

It can be a bit confusing to use raster and terra together, so I will just use terra (but you can do the same with raster, using stack in stead of c and cellStats in stead of global.

Your example data

Your code is somewhat inefficient in the sense that it repeatedly splits the all the lines up. In the code below, this is only done once when they are first read in from the file. In addition, after reading they're transposed into columns of row since most of the processing is done with respect to what in each column.

features is just one string in one line of the file, not all the strings in that column.

Add each word to the unique_list set in the loop, and print the set at the end.

Following the linked answer, it seems quite natural how to extend it to your case. In the example below, I'm using some dummy data structured like the head() data you gave, since the csv link gave me a 404.