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 am trying to generate 10 vectors of binary data with specified correlation coefficients between each 2 of these vectors.
I have defined a correlation matrix "m" 10*10 which contains the correlations between the 10 vectors, and i have used it as an input to this function:
X<-rmvbin(1000, margprob=c(rep(0.5,10)), bincorr=m)
The problem is that when I run "cor(xI", I get a matrix different from the correlation matrix "m" that I have already set. I'm not quite sure if I had correctly defined margprob. I guess this is where the problem comes from and I don't know how should I define it otherwise. Any help, please ?
I have been trying to analyse some data which is 32 bit binary code. It has been converted into corresponding decimal value and I have grouped the data into several categories based on some similarity. Now, is there a way I can compute a measure of similarity/dissimilarity within each category ?
The concepts of standard deviation, variance etc do not really make sense here but I am sure the concept of hamming distance can be used to compare similarity between two strings.
However, how to compute a sensible metric using hamming distance for a group containing 10000 such binary codes (converted to decimal) ? How do I proceed in general ? And how can I achieve something using R as the rest of the calculations I have done is in R.
Any help would be deeply appreciated.
Kindly find below a sample of my data:
ID(Group5) BinCode
1 2621440
2 3670018
3 3670018
4 3670018
5 3670018
6 2621440
7 3670018
8 2621442
9 3670018
I'm in the enviable position of being able to set up the format for my data collection ahead of time, rather than being handed some crazy format and having to struggle with it. I'd like to make sure I'm setting it up in a way that minimizes headaches down the road, but I'm not very familiar with importing into multidimensional arrays so I'd like input. It also seems like a thought exercise that others might get some use from.
I am compiling a large number of data summaries (500+) with 23 single data values for each experiment and two additional vectors that vary between 100 and 1500 data values (these two vectors happen to always match in length for each sample, but their length is different for each sample). I'm having to store all of these in an Excel sheet which I'm currently building. I want to set it up in a way that efficiently stores this data for import into an R array.
I'm assuming that the longer dimensions, which vary in length, will have the max length (1500) and a bunch of NA's at the end rather than try to keep track of ragged data in Excel.
My current plan would be to store these in long form in Excel, with data labels in the first column (dim1, dim2,...), and the data summaries in each subsequent column (a, b, c...), since this saves the most space. Using a smaller number of dimensions as an example (7 single values, 2 vectors of length 1500), the data would look like this in Excel:
a b c...
dim1 2 5 7...
dim2 3 6 8...
dim3 6 8 2 ...
dim4 5 6 1...
dim5 6 2 1...
dim6 0 3 8...
dim7 8 5 4...
dim8 1 1 1...
dim8 2 2 2 ...
... continued x1500
dim9 4 4 4...
dim9 5 5 5 ...
...continued x1500
Can I easily import this, using the leftmost column to identify the dimensions of the array in long form? I don't see an easy way to do this using Reshape2, but perhaps I'm missing something. Or, do I need to have the data in paired columns?
It isn't clear to me whether this format is the most efficient way to organize this data for import into a multidimensional array, or if there is a better way. Eventually there will be a large number of samples so I'd like to think through this now rather than struggle later.
What is the most painless way to import this...or, is there a more efficient way of setting it up for easier import?
Hmm.. I can't think of a case that you would have to use melt. If you keep the current format, and add a heading to the 'dim' column then you should be able to work with that data fairly easily.
If you did transpose the data on 'dim' I think it would make things a lot more difficult.
It might good to know what variable types a,b,c,etc. are in order to make a better assessment.
I'm a novice R user, who's learning to use this coding language to deal with data problems in research. I am trying to understand how knowledge evolves within an industry by looking at patenting in subclasses. So far I managed to get the following:
# kn.matrices<-with(patents, table(Class,year,firm))
# kn.ind <- with(patents, table(Class, year))
patents is my datafile, with Subclass, app.yr, and short.name as three of the 14 columns
# for (k in 1:37)
# kn.firms = assign(paste("firm", k ,sep=''),kn.matrices[,,k])
There are 37 different firms (in the real dataset, here only 5)
This has given 37 firm-specific and 1 industry-specific 2635 by 29 matrices (in the real dataset). All firm-specific matrices are called firmk with k going from 1 until 37.
I would like to perform many operations in each of the firm-specific matrices (e.g. compare the numbers in app.yr 't' with the average of the 3 previous years across all rows) so I am looking for a way that allows me to loop the operations for every matrix named firm1,firm2,firm3...,firm37 and that generates new matrices with consistent naming, e.g. firm1.3yearcomparison
Hopefully I framed this question in an appropriate way. Any help would be greatly appreciated.
Following comments I'm trying to add a minimal reproducible example
year<-c(1990,1991,1989,1992,1993,1991,1990,1990,1989,1993,1991,1992,1991,1991,1991,1990,1989,1991,1992,1992,1991,1993)
firm<-(c("a","a","a","b","b","c","d","d","e","a","b","c","c","e","a","b","b","e","e","e","d","e"))
class<-c(1900,2000,3000,7710,18000,19000,36000,115000,212000,215000,253600,383000,471000,594000)
These three vectors thus represent columns in a spreadsheet that forms the "patents" matrix mentioned before.
it looks like you already have a 3 dimensional array with all your data. You can basically view this as your 38 matrices all piled one on top of the other. You don't want to split this into 38 matrices and use loops. Instead, you can use R's apply function and extraction functions. Just view the help topic on the apply() family and it should show you how to do what you want. Here are a few basic examples
examples:
# returns the sums of all columns for all matrices
apply(kn.matrices, 3, colSums)
# extract the 5th row of all matrices
kn.matrices[5, , ]
# extract the 5th column of all matrices
kn.matrices[, 5, ]
# extract the 5th matrix
kn.matrices[, , 5]
# mean of 5th column for all matrices
colMeans(kn.matrices[, 5, ])