highSCREEN (version 0.1)

rankhits: title

Description

This function ranks the candidate compound hits based on the mean of "After" scores.

Usage

rankhits(dat, score.before="scorebefore", score.after="scoreafter", var="ma")

Arguments

dat

Data frane as the output of the function $formatRESULT$.

score.before

Specifies the name of the columns containing "Before" scores.

score.after

Specifies the name of the columns containing "After" scores.

var

Specifies the variable according to which the ranking is done.

Value

Data frame with a row for each hit. The columns are as follows:

ID

Has the complete information to identify a compound or control. It contains information about the main plate, the quadrant/plate and the well.

MainPlate

Specifies the main plate to wich the compound/control belongs.

Plate

Specifies the quadrant/plate to wich the compound/control belongs.

Norm

Specifies the normalization method that was applied for the specific compound.

well

Specifies the location (row and column) of the compound/control in the quadrant.

row

Specifies the row location in the quadrant.

col

Specifies the column location in the quadrant.

welltype

Specifies if the well is compound or control.

scorebefore

Replicates of the score from the "Before" data set.

scoreafter

Replicates of the score from the "After" data set.

IND2

Indicator variable specifying if the compound passes ($TRUE$) or fails ($FALSE$) QC2.

IND3

Indicator variable specifying if the compound passes ($TRUE$) or fails ($FALSE$) QC3.

diff

Difference between mean of "After" and "Before" replicates.

mb

Mean of "Before" replicates (MB).

sb

Standard deviation of "Before" replicates (SB).

rsb

Coefficient of variation of "Before" replicates. It is the ratio of SB and MB.

ma

Mean of "After" replicates (MA).

sa

Standard deviation of "After" replicates (SA)

rsa

Coefficient of variation of "After" replicates. It is the ratio of SB and MB.

ind_below

see $ind$ below

ind_above

see $ind$ below

ind

$TRUE$ (if both $ind_below$ and $ind_above$ are $TRUE$) if RSA is within the $1.5 \times IQR$, where interquartile range ($IQR$) is computed based on all hits.

Examples

set.seed(1234)
nc = 24
nr = 16

# create 1st triplicate of data matrix with compounds and controls
replicate1 = matrix(abs(rnorm(nr*nc)*0.01), nr, nc)

# create control map
cmap = data.frame(X1=c(rep("Control P", floor(nr/3)), 
rep(c("Control low", "Control med", "Control high"), 
(floor(nr/3)+nr-3*floor(nr/3))/3), rep("Control N", 
floor(nr/3))), X2=c(rep("Control N", floor(nr/3)), 
rep(c("Control low", "Control med", "Control high"), 
(floor(nr/3)+nr-3*floor(nr/3))/3), rep("Control P", floor(nr/3))))
cmap = cmap[seq(1,nr,2),]

# create 2nd triplicate of data matrix with compounds and controls
replicate2 = matrix(abs(rnorm(nr*nc)*0.01), nr, nc)

# create 3rd triplicate of data matrix with compounds and controls
replicate3 = matrix(abs(rnorm(nr*nc)*0.01), nr, nc)

# combine all triplicates for the before data
replicates_before = list(replicate1, replicate2, replicate3)
names(replicates_before) = c("Replicate1", "Replicate2", "Replicate3")

# create 1st triplicate of data matrix with compounds and controls
replicate1 = matrix(abs(rnorm(nr*nc)), nr, nc)

# create 2nd triplicate of data matrix with compounds and controls
replicate2 = matrix(abs(rnorm(nr*nc)), nr, nc)

# create 3rd triplicate of data matrix with compounds and controls
replicate3 = matrix(abs(rnorm(nr*nc)), nr, nc)

# combine all triplicates for the after data
replicates_after = list(replicate1, replicate2, replicate3)
names(replicates_after) = c("Replicate1", "Replicate2", "Replicate3")

# extract plate 1, replicate 1
dat1 = extractplate(replicates_before, replicates_after, plate=1, replicate=1)
# extract plate 1, replicate 2
dat2 = extractplate(replicates_before, replicates_after, plate=1, replicate=2)
# extract plate 1, replicate 3
dat3 = extractplate(replicates_before, replicates_after, plate=1, replicate=3)

# no normalizion
datraw1 = normplate("Main Plate 1", dat1[["datbefore"]], dat1[["datafter"]], cmap,
 plate=1, triplicate=1, norm="raw")
datraw2 = normplate("Main Plate 1", dat2[["datbefore"]], dat2[["datafter"]], cmap,
 plate=1, triplicate=2, norm="raw")
datraw3 = normplate("Main Pltae 1", dat3[["datbefore"]], dat3[["datafter"]], cmap,
 plate=1, triplicate=3, norm="raw")

# combine 3 triplicates
datraw = rbind(datraw1, datraw2, datraw3)

# reformat result
datraw = formatRESULT(datraw, triplicate="Triplicate", score="score", t="Time")

# c-score normalization
datnorm1 = normplate("Main Plate 1", dat1[["datbefore"]], dat1[["datafter"]], cmap,
 plate=1, triplicate=1, norm="cscore", 
poscont="Control P", negcont="Control N")
datnorm2 = normplate("Main Plate 1", dat2[["datbefore"]], dat2[["datafter"]], cmap,
 plate=1, triplicate=2, norm="cscore", 
poscont="Control P", negcont="Control N")
datnorm3 = normplate("Main Pltae 1", dat3[["datbefore"]], dat3[["datafter"]], cmap,
 plate=1, triplicate=3, norm="cscore", 
poscont="Control P", negcont="Control N")

# combine 3 triplicates
datnorm = rbind(datnorm1, datnorm2, datnorm3)

# reformat result
datnorm = formatRESULT(datnorm, triplicate="Triplicate", score="score", t="Time")

# identify hits
h = hits(datraw, datnorm, qc.mainplates="Main Plate 1", qc1.val=0.225, hit.val=3)

# rank hits in disending order of mean of "after" replicate scores "ma"
head(rankhits(h))