sceList=lapply(samples,function(pro){# pro=samples[1]print(pro)tmp=Read10X(file.path(dir,pro))if(length(tmp)==2){ct=tmp[[1]]}else{ct=tmp}sce=CreateSeuratObject(counts=ct,project=pro,min.cells=5,min.features=300)return(sce)})do.call(rbind,lapply(sceList,dim)) 正好复习R语言基础的...
Up till now, our examples have dealt with using the sample function in R to select a random subset of the values in a vector. It is more likely you will be called upon to generate a random sample in R from an existingdata frames, randomly selecting rows from the larger set of observat...
rxLinMod(score ~ age + sex, data = myDF) # use multiple dependent variables in model formula # print and summarize results for comparison sampleDataDir <- rxGetOption("sampleDataDir") airlineDemoSmall <- file.path(sampleDataDir, "AirlineDemoSmall") airlineLinMod1 <- rxLinMod(ArrDelay ~ ...
If it is 1, there is a perfect correlation in the sample — there is no difference between the estimated y-value and the actual y-value. At the other extreme, if the coefficient of determination is 0, the regression equation is not helpful in predicting a y-value. For information about...
Sample4 -2.3023457 -2.7599186 1.3779952 -1.0862305 1.8887791 -0.08874553 > dim(m) [1] 1000 2000 Result 1. 欧式距离(Euclidean Distance) 先挑个软柿子捏。 参考了: > system.time( + { + r1<-dist(m, method = "euclidean") + } + )
sample — there is no difference between the estimated y-value and the actual y-value. At the other extreme, if the coefficient of determination is 0, the regression equation is not helpful in predicting a y-value. For information about how r2is calculated, see "Remarks," later in this ...
The total number of small molecules in a biological sample. Primary metabolites Metabolites that are produced by many unrelated taxa and are required for normal growth, development and reproduction. Tailoring enzymes Enzymes that modify non-ribosomal peptides, polyketide backbones and/or terpenoid backbo...
("sampleDataDir"), "claims.sas7bdat") # Import the data into a data frame in memory claimsIn <- rxImport(inData = claimsSasFileName) head(claimsIn) ### # Import a fixed format text file into an .xdf file ### # Specify input and output file...
Display some sample images. Get numImages = numel(imds.Labels); idx = randperm(numImages,16); figure for i = 1:16 subplot(4,4,i) I = readimage(imds,idx(i)); imshow(I) end Extract the training set and one-hot encode the categorical classification labels. Get XTrain = readall(...
N<-10000# Specify sample size Now, we can apply the rnorm function in order to draw N (i.e. 10000) random numbers: y_rnorm<-rnorm(N)# Draw N normally distributed valuesy_rnorm# Print values to RStudio console# -1.234715493 -1.252833873 -0.254778031 -1.526646627 1.097114685 2.488744223 0.779...