The backing from Pantera Capital appears significant, as the company is one of the top venture capital firms in crypto. > PLEASE do read the posting guide > and provide commented, minimal, self-contained, reproducible code.Nonfungible token (NFT) infrastructure startup Rarify has raised $10 million in Series A funding from Pantera Capital at a valuation of $100 million. > and provide commented, minimal, self-contained, reproducible code. >minimal, self-contained, reproducible code. >to R from Matlab and am trying to pick up good programming habits
![rarify data rarify data](https://cmi-workshop.readthedocs.io/en/latest/_images/data_comparison2.png)
>having to create a list of objects first? I am trying to switch >Is there a way to directly rarefy a matrix of counts without >rinse and repeat several time for each sample. >repeats) and then select those objects from the list. >integers between 1 and the number of objects for a sample (without >Alternately, using the same list, I could create a random index of >object, rinse and repeat several times for each sample. >regroup the resulting logical vector into a vector of counts by >and thereby get a logical vector of inclusions. >"sampling" package to select a sub-sample of 100 for each sample >associated identifier) in each sample and then use the wonderful >tedious looping task of making a list of all objects (with its >plan on rarefying several times for each sample. >each sample using simple random sampling without replacement. >samples) and would now like to rarefy these down to 100 counts in > I aimed for about 500 counts in each sample (I have about 80 >I have a matrix of counts for objects (rows) by samples (columns). >is list of matrices the other is array and you can use for loop for >and than it depends on what you want to do next. >If you want to do this several times, you need to save your result >choose a sample of 100 rows from your martix, what can be achived by >I am not experienced in Matlab and from your explanation I do not >am not sure how to do that without alot of loops, and am wondering if >Instead, I want to randomly sample 100 candies from each sample and >"candy" types (rows), which sample as you state would allow me. >I don't want to randomly select either the samples (columns) or the I have data that looks like the following: > Subject: Fwd: rarefy a matrix of counts > for loop but you said you have 80 columns which shall be no problem. Maybe somebody is clever enough to discard > From reading ?sample, I was a little unclear on whether sampling
![rarify data rarify data](https://norwegianveterinaryinstitute.github.io/BioinfTraining/phyloseq_tutorial_files/figure-markdown_github/downsampling%20the%20data%20to%20the%20sample%20with%20the%20smallest%20read%20count-1.png)
> AFAIK, sampling without replacement requires enumerating the entire > Note that this does sampling WITH replacement. > apply(df, 2, function(counts) sample(seq(along=counts), rep=T, > Here's a way using apply(), and the prob= argument of sample(): How about the following approach which generates a new sample using the > It looks like Tony is right: sampling without replacement requires listing > or 300 in the matrix cells instead of "red" or a matrix of counts by color > x > for (i in 2:ncol(DF)) x] > is that this code still samples the rows, not the elements, i.e.
![rarify data rarify data](https://community.rstudio.com/uploads/default/original/3X/7/8/78ba1379c94aebf13f119b64c58ea8600699884a.png)
![rarify data rarify data](https://forum-qiime2-org.s3.dualstack.us-west-2.amazonaws.com/original/2X/5/5d7b708ce57e9bfbca39a7a914a5663e433f8d8f.png)
> x > for (i in 2:ncol(DF)) x > if you want result in data frame On Wed, at 14:25 -0400, Brian Frappier wrote: Next message: Fwd: rarefy a matrix of counts.Previous message: Fwd: rarefy a matrix of counts.Fwd: rarefy a matrix of counts Manuel Morales Manuel.A.Morales at