An example of large-scale screening analysis workflow using ZetaSuite
We provided an example data (generated from our in-house HTS2 screening dataset) for using ZetaSuite to explore the hits. To save the testing time, we provided a subsampled dataset. While this test data may not yield reasonable results, it can be used to see how the workflow is configured and executed.
we started with the pre-processed data set which was already removed the low quality rows and columns.
Users can find the example data set with data() command. The example input files include:
input matrix file, countMat, Each row represents gene with specific knocking-down siRNA pool, each column is an AS event. The values in the matrix are the processed fold change values between included exons and skipping exons read counts. Check the data by ‘data(“CountMat”)’.(we randomly pick-up 1609 genes and 100 AS events as example matrix)
input negative file, the wells treated with non-specific siRNAs, negGene. If users didn’t have the build-in negative controls, the non-expressed genes should be provided here. Check the data by ‘data(“negGene”)’.
input positive file, the wells treated with siRNAs targeting to PTB, posGene. The file is optimal is users don’t want to deduce a SVM curve. Check the data by ‘data(“posGene”)’.
input internal negative control (non-expressed genes), genes which annotated as non-expressed (RPKM<1) in HeLa cells, nonExpGene. Check the data by ‘data(“nonExpGene”)’.
step 1: QC figures
library(ZetaSuite)
data(countMat)
data(negGene)
data(posGene)
qcList <- QC(countMat,negGene,posGene)test_tSNE_QC is the global evaluation based on all the readouts. This figure can evaluate whether the positive and negative samples are well separated based on current all readouts.
qcList[[2]]
The below 3 figures are the quality evaluation of the individual readouts.
qcList[[1]]
grid::grid.draw(qcList[[3]])
qcList[[4]]
step 2: Normalized matrix
data(countMat)
data(negGene)
ZscoreVal <- Zscore(countMat,negGene)
ZscoreVal[1:5,1:5]## X5621.1 X157.1 X2451.1 X6798.1 X2570.1
## HLA-DOB -2.498000 1.8430201 0.20901748 0.7276768 1.80779786
## DOK2 1.665303 -2.3679279 0.45552333 2.6441584 1.49359264
## POU3F1 0.126659 -1.1401584 0.09130339 -0.8482665 -0.32759624
## VWC2 1.170266 0.3294906 -0.74381915 -0.7616473 1.52798191
## FGFBP3 2.042632 -1.7006933 -1.53998400 -0.7430438 0.06798774ZscoreVal is the normalized matrix, each row represents each knocking-down condition and each column is a specific readout (AS event). The values in the matrix are the normalized values.
step 3: Generating Zeta plot for positive and negative samples
data(countMat)
data(negGene)
data(posGene)
ZscoreVal <- Zscore(countMat,negGene)
ECList <- EventCoverage(ZscoreVal,negGene,posGene,binNum=100,combine=TRUE)
ECList[[2]][[1]]
ECList[[2]][[2]]
Step 4: Calculating Zeta score for each screened gene
data(ZseqList)
data(SVMcurve)
data(countMat)
data(negGene)
ZscoreVal <- Zscore(countMat,negGene)
zetaData <- Zeta(ZscoreVal,ZseqList,SVM=FALSE)zetaData is the zeta values for all tested knockding-down genes including positive and negative controls. The first column is the direction which knockding-down gene will lead to exon inclusion, whereas the second column is the knock-down genes will lead to exon skipping.
Step 5: Generating Screen Strength plot for hits selection
data(nonExpGene)
data(negGene)
data(posGene)
data(ZseqList)
data(countMat)
ZscoreVal <- Zscore(countMat,negGene)
zetaData <- Zeta(ZscoreVal,ZseqList,SVM=FALSE)
cutoffval <- FDRcutoff(zetaData,negGene,posGene,nonExpGene,combine=TRUE)
cutoffval[[2]][[1]]## TableGrob (1 x 2) "arrange": 2 grobs
## z cells name grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]cutoffval[[2]][[2]]