I am having some issues in interpreting the results from prcomp().
Say I have a centered and scaled data.table called dat, with N columns and M rows. Indeed every column represents a feature and every row a record. I also got a M-dimensional vector of outcomes Y.
I wanted to know what the PCA of this system says. So I just executed:
dat.pca=prcomp(dat,retx=TRUE)
By the elbow method I decided to retain 5 PCA modes, accounting for 90% of the variance. Then, I got the following data.table:
dat.pcadata=as.data.table(dat.pca$x)
dat.pcadata has M rows and N columns, and each column corresponds to a PCA mode.
My question is: do I understand correctly if I say that now my system should be trained to forecast the outcomes Y using the first 5 columns of dat.pcadata as features?
Related
I have a list of matrices containing association measurements between GPS tracked animals. One matrix in the list is observed association rates, the others are association rates for randomized versions of the GPS tracking trajectories. For example, I currently have 99 permutations of randomized tracking trajectories resulting in a list of 99 animal association matrices, plus the observed association matrix. I am expecting that for the animals that belong to the same pack, the observed association rates will be higher than the randomized association rates. Accordingly, I would like to determine the rank of the observed rates compared to the randomized rates for each dyad (cell). Essentially, I am doing a rank-permutation test. However, since I am only really concerned with determining if the observed association data is greater than the randomized trajectory association data, any result just giving the rank of the observed cells is sufficient.
ls <- list(matrix(10:18,3,3), matrix(18:10,3,3))
I've seen using sapply can get the ranks of particular cells. Could I do the following for all cells and take the final number in the resulting vector to get the rank of the cell in that position in the list (knowing the position of the observed data in the list of matrices, e.g. last).
rank(sapply(ls, '[',1,1))
The ideal result would be a matrix of the same form as those in the list giving the rank of the observed data, although any similar solutions are welcome. Thanks in advance.
You can proceed that way, but there are cleaner and quicker methods to get what you want.
Here's some code that would take your ls produce a 3x3 matrix with the following properties:
if the entry in ls[[1]] is greater than the corresponding entry of ls[[2]], record a 1
if the entry in ls[[1]] is less than the corresponding entry of ls[[2]], record a 2
if the entries are equal, record a 1.5
result <- 1 * (ls[[1]] > ls[[2]]) + 2 * (ls[[1]] < ls[[2]]) + 1.5 * (ls[[1]] == ls[[2]])
How it works: when we do something like ls[[1]] > ls[[2]], we are ripping out the matrices of interest and directly comparing them. The result of this bit of code is a T/F-populated matrix, which is secretly coded as a 0/1 matrix. We can then multiply it by whatever coefficient we want to represent that situation.
I'm profiling tumor microenvironment and I want to show interactions between subpopulations that I found. I have a list of receptors and ligands for example, and I want to show that population A expresses ligand 1 and population C expresses receptor 1 so there's likely an interaction between these two populations through the expression of ligand-receptor 1.
I have been trying to use circlize to visualize these interactions by making a chordDiagram, but it requires an adjacency matrix as input and I do not understand how to create the matrix. The adjacency matrix is supposed to show the strength of the relationship between any two genes in my matrix. I have 6 unique populations of cells that can express any of the 485 ligands/receptors that I am interested in, and the goal is to show interactions between these populations through the ligands and receptors.
I found a tool to use in RStudio called BUS- gene.similarity: Calculate adjacency matrix for gene-gene interaction.
Maybe I am just using BUS incorrectly but it says: For gene expression data with M genes and N experiments, the adjacency matrix is in size of MxM. An adjacency matrix in size of MxM with rows and columns both standing for genes. Element in row i and column j indicates the similarity between gene i and gene j.
So, I made a matrix where each column is a subpopulation and each row is a ligand/receptor I want to show interactions with. The cells have expression values and it looks like this:
> head(Test)
A B C D E F
Adam10 440.755990 669.875468 748.7313995 702.991422 1872.033343 2515.074366
Adam17 369.813134 292.625603 363.0301707 434.905968 1183.152694 1375.424034
Agt 12.676036 28.269671 9.2428034 19.920561 121.587010 168.116735
Angpt1 22.807415 42.350205 25.5464603 16.010813 194.620550 99.383567
Angpt2 92.492760 186.167844 819.3679836 852.666499 669.642441 1608.748788
Angpt4 3.327743 0.693985 0.8292746 1.112826 5.463647 5.826927
Where A-F are my populations. Then I pass this matrix to BUS:
res<-gene.similarity(Test,measure="corr",net.trim="none")
Warning message:
In cor(mat) : the standard deviation is zero
But the output file which is supposed to be my adjacency matrix is full of NA's:
Adam10 Adam17
Adam10 1 NA
Adam17 NA 1
I thought maybe my matrix was too complex, so I compared only 2 cell populations with my ligands/receptors, but I get the exact same output.
I was expecting to get something like:
A:Adam10 A:Adam17
C:Adam10 6 1
E:Adam17 2 10
But, even if the res object gave me numbers instead of NA it does not maintain the identity of the population when making relationships amongst genes so it still would not produce my expected output.
I do not have to use BUS to make the matrix, so I don't necessarily need help troubleshooting that code, I just need SOME way to make an adjacency matrix.
I've never used circlize or Circos before so I apologize if my question is stupid.
Seems like you need to transform you matrix a little.
you can create a new matrix which has size (nrow(Test) x ncol(Text)) x (nrow(Test) x ncol(Text)), so in the example you gave, the new matrix will be 36x36, and the colnames and rownames will be the same which are A_Adam10, A_Adam17,..., A_Angpt4, B_Adam10,..., F_Angpt4.
With the help of a loop, you can load the similarity of each pair into the new matrix, and now you can plot the matrix. It's a little complicated, also takes a while to run the loop, but it's intuitive.
You're welcomed to check my github repo since I had a similar problem not too long ago, and I posted detailed code on there. I hope this will help you
I'm working with a numeric matrix M in R which is quite big (11000 rows per 20 columns). On this matrix, I'm performing a lot of correlation tests
=> the function cor.test(M[i,], M[j,], method='spearman') where i and j are two rows from the matrix (all possible combinations are tested).
The problem as you know is that I'm doing too many tests to get a very reliable p-value returned by this test.
My strategy to overcome this limitation would be to generate a new probability distribution by Bootstrap on my matrix M: I would like to get 100 random matrices generated from M to do the multiple correlations on these matrices and choose the right cut-off for the p-value to get a FDR of 5%.
My question is:
What is the most efficient way to randomize my matrix?
Since it's quite time consumming (I suppose) it could be interresting if the solution could be parallelized.
Thank you in advance for all the usefull answers that you'll provide to me.
In python there is a function random.sample() in module random. If you store M as list of rows, randomly sampling n rows from matrix M without replacement would be like this
M_sample = random.sample(M,n)
However, for bootstrapping, you might want to do random sampling with replacement. To do this, you can use numpy.random.choice():
import numpy
M_sample = numpy.random.choice(M,n,replace=True)
In R, we use sample() to randomly decide the row indices to take, and then use row access to take the rows from the matrices. Randomly sampling n rows from matrix M without replacement is done as follows:
indices = sample(nrow(M), n,replace=FALSE)
M_sample = M[indices, ]
And for randomly sampling with replacement, replace the first line with this:
indices = sample(nrow(M), n,replace=TRUE)
I am working with a large federal dataset with thousands of observations and thousands of variables. Replicate weights are provided. I am using the "survey" package in R to apply these weights:
els.weighted=svrepdesign(data=els, repweights = ~els$F3F1PNLWT,
combined.weights = TRUE).
I am interested in some categorical descriptive characteristics of a subset of the population, such as family living arrangements. I want to get these sorted out into a contingency table that shows frequency. I would like to sort people based on four variables (none of which are binary, but all of which are numeric) This is what I would like to get:
.
The blank boxes are where the cross-tabulation/frequency counts would show. (I only put in 3 columns beneath F1COMP for brevity's sake, but it has 9 outcomes – indexed 1-9)
My current code: svyby(~F1FCOMP, ~F1RTRCC +BYS33C +F1A10 +byurban, els.weighted, svytotal)
This code does sort the data, but it sorts every single combination, by default. I want them pared down to represent only specific subpopulations of each variable. I tried:
svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C==1 +F1A10==2 | F1A10==3 +byurban==3, els.weighted, svytotal)
But got stopped:
Error: unexpected '==' in "svyby(~F1FCOMP, ~F1RTRCC==2 |F1RTRCC==3 +BYS33C=="
Additionally, my current version of the code tells me how many cases occur for each combination, This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down.
This is a picture of what my current output looks like. There are hundreds more rows, 1 for each combination, when I keep scrolling down
.
You can see in that picture that I only get one number for F1FCOMP per row – the number of cases who fit the specified combination – a specific subpopulation. I want to know more about that subpopulation. That is, F1COMP has nine different outcomes (indexed 1-9), and I want to see how many of each subpopulation fits into each of the 9 outcomes of F1COMP.
Is there a general method for calling a row when you do not know whether you'll be referencing a matrix or a vector?
I want to subset results to those with accuracy greater than .5 and then select the row of the subset with the highest sensitivity. I repeat this process many times in a loop. The problem I'm running into is that in some runs of the model many rows of the results have accuracy greater than .5 and in some runs only one row has accuracy greater than .5.
To call the row with maximum accuracy I've written the code.
# Subset matrix to just rows with accuracy greater than .5
acc_ID = which(new_data[,"accuracy"] >= 0.5)
new_data2 = new_data[acc_ID,]
## Identify which row has the highest sensitivity
max_sensitivity_ID = which(new_data2[,"sensitivity"] == max(new_data2[,"sensitivity"]))[1]
The problem comes from the last line. If there is only one row with accuracy > .5. Then I need to remove the commas.
Note: this is a big data situation and I'm not uploading a replicable data example. I figure that someone out there will know a general method for calling a row without replicating the problem.
Use drop=FALSE to ensure new_data2 is always a matrix.
new_data2 = new_data[acc_ID,,drop=FALSE]