EST (Expressed Sequence Tag)

The EST database contains sequence records from the bulk EST (Expressed Sequence Tag) division of GenBank. These are typically short single-pass reads from cDNA libraries often generated as large survey project. Data from EST can be used to catalog expressed genes for a particular organ, tissue or cell type or general for a species, and compare expression levels of genes in various library sources

UCSC Genes Track

The UCSC Genes track is a set of gene predictions based on data from RefSeq, Genbank, CCDS, UniProt, Rfam, and the tRNA Genes track. The track includes both protein-coding genes and non-coding RNA genes. Both types of genes can produce non-coding transcripts, but non-coding RNA genes do not produce protein-coding transcripts. This is a moderately conservative set of predictions. Transcripts of protein-coding genes require the support of one RefSeq RNA, or one GenBank RNA sequence plus at least one additional line of evidence. Transcripts of non-coding RNA genes require the support of one Rfam or tRNA prediction. Compared to RefSeq, this gene set has generally about 10% more protein-coding genes, approximately four times as many putative non-coding genes, and about twice as many splice variants.

Expressed Sequence Tags

Expressed Sequence Tags (ESTs)

ESTs are short (200–500 nucleotides) DNA sequences that can be used to identify a gene that is being expressed in a cell at a particular time.The Procedure:

• Isolate the messenger RNA (mRNA) from a particular tissue (e.g., liver)
• Treat it with reverse transcriptase. Reverse transcriptase is a DNA polymerase that uses RNA as its template. Thus it is able to make genetic information flow in the reverse (RNA ->DNA) of its normal direction (DNA -> RNA).
• This produces complementary DNA (cDNA). Note that cDNA differs from the normal gene in lacking the intron sequences.
• Sequence 200–500 nucleotides at both the 5′ and 3′ ends of each cDNA.
• Examine the database of the organism's genome to find a matching sequence.
• That is the gene that was expressed.

Kappa test

Calculate Cohen's kappa statistics for agreement and its confidence intervals followed by testing null-hypothesis that the extent of agreement is same as random, kappa statistic equals zero.

LM fitting

A data set has values y_i, each of which has an associated modelled value f_i (also sometimes referred to as ŷ_i). Here, the values y_i are called the observed values and the modelled values f_i are sometimes called the predicted values.

The "variability" of the data set is measured through different sums of squares:

 the total sum of squares (proportional to the sample variance);

 the regression sum of squares, also called the explained sum of squares.

, the sum of squares of residuals, also called the residual sum of squares.

In the above  is the mean of the observed data:

where n is the number of observations.

The notations  and  should be avoided, since in some texts their meaning is reversed to Residual sum of squares and Explained sum of squares, respectively.

The most general definition of the coefficient of determination is

Mitochondrial DNA

Mitochondrial DNA (mtDNA or mDNA^[2]) is the DNA located in organelles called mitochondria, structures withineukaryotic cells that convert the chemical energy from food into a form that cells can use, adenosine triphosphate (ATP). 

R: pair function

url = "http://stats191.stanford.edu/data/supervisor.table"
    supervisor.table = read.table(url, header = T)
    attach(supervisor.table)
    pairs(supervisor.table, pch = 23, bg = "orange", cex.labels = 6, cex = 2)

panel.cor.scale <- function(x, y, digits=2, prefix="", cex.cor)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r = (cor(x, y,use="pairwise"))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex * abs(r))
}


panel.cor <- function(x, y, digits=2, prefix="", cex.cor)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r = (cor(x, y,use="pairwise"))
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep="")
if(missing(cex.cor)) cex <- 0.8/strwidth(txt)
text(0.5, 0.5, txt, cex = cex )
}


panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col="cyan", ...)
}


pairs.panels <- function (x,y,smooth=TRUE,scale=FALSE)
{if (smooth ){
if (scale) {
pairs(x,diag.panel=panel.hist,upper.panel=panel.cor.scale,lower.panel=panel.smooth)
}
else {pairs(x,diag.panel=panel.hist,upper.panel=panel.cor,lower.panel=panel.smooth)
} #else {pairs(x,diag.panel=panel.hist,upper.panel=panel.cor,lower.panel=panel.smooth)
}
else #smooth is not true
{ if (scale) {pairs(x,diag.panel=panel.hist,upper.panel=panel.cor.scale)
} else {pairs(x,diag.panel=panel.hist,upper.panel=panel.cor) }
} #end of else (smooth)
} #end of function






pairs.panels(supervisor.table, pch = 23, bg = "orange", cex.labels = 6, cex = 2)