osf.io | Facilitating Open Science | REST library

First, glob_wildcards(INPUTDIR + "{basenames}_R1.fastq.gz") returns a Wildcards object that contains the key:value pair for each wildcard. If you want to get the basenames, it should be glob_wildcards(INPUTDIR + "{basenames}_R1.fastq.gz").basenames

Second, I assume all fastq.gz files are generated by decompress_h1n1 checkpoint, since you already include the fastqc output in aggregate_decompress_h1n1 function. You shouldn't include those outputs again in rule all, it leads to snakemake try to do fastqc before checkpoint got executed.

Third, you should also put your trim_qc outputs in the aggregate_decompress_h1n1 function, basically it's the same issue as fastqc, probably the same for salmon related rules too.

There might be a potential issue, I noticed you use wrapper for fastqc, I remember that official fastqc wrapper requires the output must have html and zip, while you have a raw prefix. But I didn't see a 0.80.3 release in official document, not sure if you are using some other repository to access wrapper

The wrong link was used in the question. The correct link can be found by going to https://osf.io/nkdw5/, then clicking on filter_raw.csv in the Data section, and then copying the link from the download button (upper right of page).

Here the solution that has worked better.

I would like to help you. However, there are the following problems with the data you provide:

1. The variable COND is missing
2. Only one unique value of the TASK variable (the CreateTableOne function does not accept variables with one unique value).
3. Only one unique value for the variable age.
4. The variable ID is repeated several times.

However, even without changing your data, you can see what your problem is. If you have data in this form, you cannot use CreateTableOne! This is because it counts every occurrence of the value m and every occurrence of the value k. And since you have multiple entries for one person, the CreateTableOne function will count each occurrence separately.

Please take a look at the solution I have proposed here How to describe unique values of grouped observations for several vars?.

Update 1

OKAY. Let's try to face your data. You have 54 patients with different IDs.

These are not suitable places to use lapply. The functions just take the data frames as inputs, so you can just use the functions. For example:

I am not aware of any package that already does this. But you can create all those combinations (2^n-1), where n is the number of columns in X (independent variables), and fit a linear regression model for each combination and then get coefficients/betas for each model.

Here is how I would do it, hope this helps