lait.stat {st} | R Documentation |
lait.stat
, laicat.fun
, and lai.tscore
compute the ``correlation-predicted'' t-statistic of Lai (2008).
lait.stat(X, L, f=0.2, verbose=TRUE) lait.fun(L, f=0.2, verbose=TRUE) lai.tscore(gene, tscore, corr, f=0.2, plot=FALSE)
X |
data matrix. Note that the columns correspond to variables (``genes'') and the rows to samples. |
L |
vector with class labels for the two groups. |
verbose |
print out some (more or less useful) information during computation. |
f |
smoother span used in lowess (default value: 0.2) |
gene |
the gene for which the Lai t-score is computed |
tscore |
a vector with t-scores |
corr |
a matrix containing correlations |
plot |
show scatter plot correlations versus t-scores with predicted t-score |
The correlation-predicted t-statistic for a gene is the t-score predicted by local linear regression using all other genes. For mathematical details see Lai (2008).
lait.stat
returns a vector containing correlation-predicted t-statistic for each variable/gene.
The corresponding lait.fun
functions return a function that
computes the correlation-shared t-statistic when applied to a data matrix
(this is very useful for simulations).
The function lai.tscore
allows to compute the correlation-predicted t-statistic
for a gene given a correlation matrix and a vector of t-statistics.
Verena Zuber and Korbinian Strimmer (http://strimmerlab.org).
Lai, Y.. 2008. Genome-wide co-expression based prediction of differential expression. Bioinformatics 24:666-673.
# load st library library("st") # prostate data set data(singh2002) X = singh2002$x L = singh2002$y dim(X) # 102 6033 length(L) # 102 # compute correlation-predicted t-score for various choices # of smoothing span ## Not run: score1 = lait.stat(X, L, f=0.1) idx1 = order(abs(score1), decreasing=TRUE) idx1[1:10] # 1072 297 1130 4495 4523 4041 1089 955 373 3848 score3 = lait.stat(X, L, f=0.3) idx3 = order(abs(score3), decreasing=TRUE) idx3[1:10] # 1130 962 1688 1223 583 1118 955 297 698 1219 score5 = lait.stat(X, L, f=0.5) idx5 = order(abs(score5), decreasing=TRUE) idx5[1:10] # 698 962 1223 1219 739 1172 583 694 3785 3370 score7 = lait.stat(X, L, f=0.7) idx7 = order(abs(score7), decreasing=TRUE) idx7[1:10] # 698 739 1219 962 3785 725 694 735 3370 1172 # pick the one with highest correlation to Student t score t = studentt.stat(X, L) cor(t, score1, method="spearman") # 0.4265832 cor(t, score3, method="spearman") # 0.471273 cor(t, score5, method="spearman") # 0.4750564 cor(t, score7, method="spearman") # 0.4666669 ## End(Not run) # focus on gene 19 t = studentt.stat(X, L) R = centroids(X, L, powcor.pooled=TRUE, alpha=1, shrink=FALSE, verbose=TRUE)$powcor.pooled lai.tscore(gene=19, t, R, f=0.5, plot=TRUE)