Description Usage Arguments Value Author(s) References Examples
Probabilistic imputation to reduce dropout effects in single cell sequencing data
1 | PRIME(sc_cnt, max_it = 5, alp = 1, err_max = 0.05)
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sc_cnt |
(M x N) dimensional input matrix. This matrix sould be in count scale (not a log scale) M: number of genes and N: Number of cells |
max_it |
Maximum number of iteration |
alp |
Shape parameter for a sigmoid function (probabilistic weight) |
err_max |
Error tolerance to stop the iteration |
Normalized imputation result in count scale
Hyundoo Jeong
Hyundoo Jeong and Zhandong Liu
PRIME: a probabilistic imputation method to reduce dropout effects in single cell RNA sequencing
1 2 3 4 5 | data(testdata)
PRIME_res <- PRIME(testdata)
pca_res <-prcomp(t(log10(1+PRIME_res)))
plot(pca_res$x[,1], pca_res$x[,2], bg = c("red", "blue","green")[factor(label)], type = "p", pch = 21, xlab = "PC1", ylab = "PC2")
legend("topleft", title="Cell types", c("G1","S","G2M"), fill = c("red", "blue","green"), horiz = TRUE)
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