A Method to Search for Differentially Expressed Genes and to Detect Recurrent Chromosomal Copy Number Aberrations

Description

Implementation of a method based on fold change rank ordering statistics to search for differentially expressed genes or to detect recurrent chromosomal copy number aberrations. This package can be used for two biological conditions high-throughput dataset (microarray, RNA-seq, ...), for expression profiling dataset over time without replicates or for cytogenetics dataset (aCGH, Sequencing).

Details

Package fcros has the following functions:

Author(s)

Doulaye Dembele Maintainer: Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt, trim.opt);

   # perform Volcano plot
   fvalVolcanoPlot(af, thr = 0.01)

   # save fcros values in a file
   fcrosWrite(af, file = "test2delete_values.txt");

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(c(0,11), ncol = 1);
   id.up <- matrix(c(rep(0,11)), ncol = 1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown+1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup+1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);

   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1,2:21], type = "l", col = "blue", xlim = c(1,20),
          ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t, data.down[i,2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20),
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Example of sequencing data to test the rankReads function.

Description

This is a subset of data taken from the Bottomly dataset see http://bowtie-bio.sourceforge.net/recount/. The complete dataset has 36,536 rows or ENSEMBL identifiers (genes) and 21 columns (samples). For the "bott" data, the first 5,000 rows, first 3 and last 3 samples were used.

Usage

data(bott)

Format

A data frame with 5,000 rows and 7 columns.

gene:

ENSEMBL ID

SRX033480:

sequencing values for the first B6 mouse

SRX033488:

sequencing values for the second B6 mouse

SRX033481:

sequencing values for the third B6 mouse

SRX033493:

sequencing values for the first D2 mouse

SRX033486:

sequencing values for the second D2 mouse

SRX033494:

sequencing values for the third B6 mouse

Details

"bott" is a subset of a complet dataset obtained using a single RNA-seq reads from C57BL/6J (B6) and DBA/J2 (D2) mice.

References

Bottomly et al. Evaluating Gene Expression in C57BL/6J and DBA/J2 Mouse Striatum Using RNA-seq and Microarrays, PLoS One, 6(3)e17820, 2011

Examples

   data(bott)
   summary(bott)

Calculation of the sorted rank matrix from the dataset

Description

This is an internal function used to calculate the sorted rank matrix. It is used in the functions: fcros() and pfco().

Usage

calcSRmat(xdata, cont, test, log2.opt=0, trim.opt=0.25)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kaster P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC bioinformatics, 2014, 15:14

Examples

#    data(fdata);

Calculation of the sorted rank matrix from the dataset

Description

This is an internal function used to calculate the sorted rank matrix. It is used in the functions: fcrosMod() and pfcoMod().

Usage

calcSRmatMod(xdata, samp, log2.opt=0, trim.opt=0.25)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kaster P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC bioinformatics, 2014, 15:14

Examples

#    data(fdata);

Example of aCGH "data file" for the fcros package.

Description

This dataset is part of the study performed and published by Sircoulomb et al. 2010, BMC Cancer, 10:539. For our illustration, we used the first 10 patients' data and for only 3 chromosomes: 7, 8 and 9. The complete data are available from the Gene Expression Omnibus website under the accession number GSE17907.

Usage

data(cghData)

Format

A data frame with 33,3613 observations for 10 samples.

Probes:

a text for the unique probe ID

GSM447252:

a numeric vector with log2 change values

GSM447253:

a numeric vector with log2 change values

GSM447254:

a numeric vector with log2 change values

GSM447255:

a numeric vector with log2 change values

GSM447256:

a numeric vector with log2 change values

GSM447257:

a numeric vector with log2 change values

GSM447258:

a numeric vector with log2 change values

GSM447259:

a numeric vector with log2 change values

GSM447260:

a numeric vector with log2 change values

GSM447261:

a numeric vector with log2 change values

Details

This is a subset of a complete dataset obtained using array Comparative Genomic Hybridization technology. Agilent 244K design arrays have been used to monitor breast cancer patients.

References

F Sircoulomb, I Bekhouche, P Finetti, J Adelaide, AB Hamida, J Bonansea, S Raynaud, C Innocenti, E Charafe-Jauffret, C Tarpin, FB Ayed, P Viens, J Jacquemier, F Bertucci, D Birnbaum and M Chaffanet; Genome profiling of ERBB2-amplifief breast cancers. BMC Cancer, 2010, 10:539

Examples

   data(cghData)

   summary(cghData)

   #  perform boxplot of data sample values
   boxplot(cghData[,2:11])

Example of aCGH "info file" for the fcros package.

Description

This dataset is part of the Agilent 244K design array probes description file which is used in the publicatin of Sircoulomb et al. 2010, BMC Cancer, 10:539. For our illustration, we used only 3 chromosomes (7, 8 and 9) data. The complete data are available in the file "GPL9158-3352.txt", see the Gene Expression Omnibus website and accession number GSE17907.

Usage

data(cghInfo)

Format

A data frame with 33,3613 rows for 7 columns with information on probes.

Index:

a numeric used for the probe

ProbeName:

a text for the unique probe ID

GeneSymbol:

a text with the gene symbol associated with the probe

Chromosome:

a text with the chromosome index associated with the probe

Start:

a start position value for the sequence associated with the probe

End:

an end position value for the sequence associated with the probe

Cytoband:

a text for the cytoband associated with the probe

Details

This is a part of information obtained from the Agilent 244K design array probes description file.

References

F Sircoulomb, I Bekhouche, P Finetti, J Adelaide, AB Hamida, J Bonansea, S Raynaud, C Innocenti, E Charafe-Jauffret, C Tarpin, FB Ayed, P Viens, J Jacquemier, F Bertucci, D Birnbaum and M Chaffanet; Genome profiling of ERBB2-amplifief breast cancers. BMC Cancer, 2010, 10:539

Examples

   data(cghInfo)

   summary(cghInfo)

Using a C code for merging chromosome segments

Description

This is an internal function for using a C code while merging chromosome segments in the segmentation step.

Usage

chrMerge(nbSeg, idStart, idEnd, lBound, uBound, segVal, segProba,
                         fcall, L2R, nd, dm, sigma)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

#    data(fdata);

Plot a chromosome data

Description

This function generates a picture using a chromosome data.

Usage

chrPlot(chrData, thr = 0.05, deb = 100, fin = 1e10)

Arguments

Value

This function generates a picture on the screen

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

    # load CGH data and info files
    data(cghData)
    rownames(cghData) <- cghData[,1];
    data(cghInfo)
    noms <- colnames(cghData)
    m <- length(noms)
    samp  <- noms[2:m]

    # associate statistics with probes
    af <- fcrosMod(cghData, samp, log2.opt = 0, trim.opt = 0.25)

    chromosomes = c(7:9)
    alpha <- 0.05

    # summarize results for each chromosome
    xinfo2 <- dataSummary(af, cghInfo, chromosomes, alpha)

    # focused on chromosome 7 data
    idx <- which(xinfo2$xinfo.s$Chromosome == "7")
    chrData <- xinfo2$xinfo.s[idx, ]

    # Plot chromosome 7 data
    chrPlot(chrData, thr = alpha)

Plot a chromosome segmentation results

Description

This function generates a picture. It uses a chromosome data and the output results of the segmentation function chrSegment().

Usage

chrPlot2(chrData, chrSeg, deb = 100, fin = 1e10)

Arguments

Value

This function generates a picture on the screen

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

    # load CGH data and info files
    data(cghData)
    rownames(cghData) <- cghData[,1];
    data(cghInfo)
    noms <- colnames(cghData)
    m <- length(noms)
    samp  <- noms[2:m]

    # associate statistics with probes in the dataset
    af <- pfcoMod(cghData, samp, log2.opt = 0, trim.opt = 0.25)

    chromosomes <- c(7:9)
    alpha <- 0.05

    # summarize results for each chromosome
    xinfo2 <- dataSummary(af, cghInfo, chromosomes, alpha)

    # focused on chromosome 7 data
    idx <- which(xinfo2$xinfo.s$Chromosome == "7")
    chrData <- xinfo2$xinfo.s[idx, ]

    # segment chromosome 7 data
    chrSeg <- chrSegment(chrData, nd = 15)

    # plot chromosome 7 results
    op <- par(mfrow = c(2,1))
    chrPlot(chrData, thr = alpha, deb =-1, fin = 3.5e7)
    chrPlot2(chrData, chrSeg, -1, fin = 3.5e7)
    par(op)

Segmentation of a chromosome data

Description

This function allows to segment a chromosome data

Usage

chrSegment(chrData, nd = 10)

Arguments

Value

This function returns a data frame containing 6 information for each segment

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

    # load CGH data and info files
    data(cghData)
    rownames(cghData) <- cghData[,1]
    data(cghInfo)
    noms <- colnames(cghData)
    m <- length(noms)
    samp  <- noms[2:m]

    # associate statistics with probes in the dataset
    af <- pfcoMod(cghData, samp, log2.opt = 0, trim.opt = 0.25)

    chromosomes = c(7:9)
    alpha <- 0.05

    # summarize results for each chromosome
    xinfo2 <- dataSummary(af, cghInfo, chromosomes, alpha)

    # focused on chromosome 7 data
    idx <- which(xinfo2$xinfo.s$Chromosome == "7")
    chrData <- xinfo2$xinfo.s[idx, ]

    # segment chromosome 7 data
    chrSeg <- chrSegment(chrData, nd = 15)

    # show first 10 segment results
    chrSeg[1:10,]

Summarization of the detection results for a list of chromosomes

Description

From an outpout object of the function fcrosMod() or pfcoMod(), the chromosomes information object, the list of chromosomes and a threshold, this function creates two objects containing ordered chromosome data and summary results.

Usage

dataSummary(af, xinfo, chromosomes = c(1:22,"X","Y"), alpha = 0.05)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

    # load CGH data and info files
    data(cghData)
    rownames(cghData) <- cghData[,1]
    data(cghInfo)
    noms <- colnames(cghData)
    m <- length(noms)
    samp  <- noms[2:m]

    # associate statistics with probes in the dataset
    af <- pfcoMod(cghData, samp, log2.opt = 0, trim.opt = 0.25)

    chromosomes = c(7:9)
    alpha <- 0.05

    # summarize results for each chromosome
    xinfo2 <- dataSummary(af, cghInfo, chromosomes, alpha)

    # display the number of significant probes for each chromosome
    xinfo2$chrSumm

Calculation of fold change using pairwise comparison values

Description

This is an internal function for using a C code to calculate fold changes using pairwise comparison of samples.

Usage

fc2Calc(rvect, n, m, idx, m2)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

#    data(fdata);

Search for differentially expressed genes/probes

Description

Implementation of a method based on fold change rank ordering statistics for detecting differentially expressed genes in a dataset. This function should be used with two biological conditions dataset (microarray or RNA-seq, ...). Using pairwise combinations of samples from the two biological conditions, fold changes (FC) are calculated. For each combination, the FC obtained are sorted in increasing order and corresponding rank values are associated with genes. Then, a statistic is assigned to the robust average ordered rank values for each gene/probe.

Usage

fcros(xdata, cont, test, log2.opt = 0, trim.opt = 0.25)

Arguments

Details

Label names appearing in the parameters "cont" and "test" should match with some label names in the columns of the data matrix "xdata". It is not necessary to use all label names appearing in the columns of the dataset matrix.

Value

This function returns a data frame containing 9 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];
   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt, trim.opt);

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(0, 1);
   id.up <- matrix(0, 1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown + 1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup + 1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);


   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1,2:21], type = "l", col = "blue", xlim = c(1,20), 
        ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t,data.down[i,2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20), 
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Search for differentially expressed genes/probes

Description

Implementation of a method based on fold change or ratio rank ordering statistics for detecting differentially expressed genes. This function should be used with dataset in two separate tables and from two biological conditions datasets (microarray, RNA-seq, ...). Pairwise combinations of samples from the two biological conditions are used to obtain a matrix of fold changes. For each combination, the FCs obtained are sorted in an increasing order and the corresponding rank values are associated with genes/probes. Then, a statistic is associated with each gene/probe.

Usage

fcros2(xdata1, xdata2, cont, test, log2.opt = 0, trim.opt = 0.25)

Arguments

Details

The label names appearing in the parameters "cont" and "test" should match some label names in the columns of the data matrices "xdata1" and "xdata2". It is not necessary to use all column label names appearing in matrices "xdata1" and "xdata2". However, it is assumed that the same genes (same IDs or symbol) are used in xdata1 and xdata2.

Value

This function returns a data frame containing 9 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont02", "cont03", "cont04", "cont05",
             "cont06", "cont07", "cont08", "cont09", "cont10");
   test <- c("test01", "test02", "test03", "test04", "test05",
             "test06", "test07", "test08", "test09", "test10");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform fcros2()
   xdata1 <- fdata[,c(2:5, 12:17)];
   xdata2 <- fdata[,c(6:11, 18:21)];
   rownames(xdata1) <- fdata[,1];
   rownames(xdata2) <- fdata[,1];

   af2 <- fcros2(xdata1, xdata2, cont, test, log2.opt, trim.opt);

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af2, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(0,1);
   id.up <- matrix(0,1);
   n <- length(af2$FC);
   f.value <- af2$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown + 1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup + 1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);

   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1,2:21], type = "l", col = "blue", xlim = c(1,20),
        ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t,data.down[i,2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20),
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Calculation of a matrix of fold changes using pairwise comparisons

Description

This function is used internally by fcros2() to compute a matrix of fold changes using pairwise comparisons of a two biological conditions dataset.

Usage

fcrosFCmat(xdata, cont, test, log2.opt=0, trim.opt=0.25)

Arguments

Details

The label names appearing in the parameters "cont" and "test" should match some label names of the columns in the table "xdata". The dataset "xdata" can contain other label names not used.

Value

This function returns a data frame containing 3 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcrosFCmat()
   fc <- fcrosFCmat(fdata, cont, test, log2.opt);

   # plot histogram of the fold change (log2 scale) in dataset
   hist(log2(fc$FC),nclass = 50);

Search for differentially expressed genes or to detect recurrent copy number aberration probes

Description

Implementation of a method based on fold change rank ordering statistics to search for differentially expressed genes or to detect chromosomal recurrent copy number aberration probes. This function should be used with a matrix of fold changes or ratios from biological dataset (microarray, RNA-seq, ...). The function fcrosMod() is an extention of the function fcros() to a dataset which does not contain replicate samples or to a dataset with one biological condition dataset. Statistics are associated with genes/probes to characterize their change levels.

Usage

fcrosMod(fcMat, samp, log2.opt = 0, trim.opt = 0.25)

Arguments

Details

The label names appearing in the parameter "samp" should match some label names of the columns in the data matrix "xdata". It is not necessary to use all label names appearing in the columns of the dataset matrix.

Value

This function returns a data frame containing 8 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform fcrosMod()
   fc <- fcrosFCmat(fdata, cont, test, log2.opt, trim.opt);
   m <- ncol(fc$fcMat)
   samp <- paste("Col",as.character(1:m), sep = "");
   fc.val <- cbind(data.frame(fc$fcMat))
   colnames(fc.val) <- samp
   rownames(fc.val) <- fdata[,1]

   af <- fcrosMod(fc.val, samp, log2.opt, trim.opt);

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(0,1);
   id.up <- matrix(0,1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown + 1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup + 1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);


   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1, 2:21], type = "l", col = "blue", xlim = c(1,20),
        ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t,data.down[i, 2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20), 
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Read a tab delimited text file

Description

This function can be used to read a tab delimited text file to be used as input for the functions fcros(), fcros2(), fcrosMod() pfco() or pfcoMod(). You may also use other functions (read.csv(), read.csv2(), read.delim() or read.delim2()) to read your dataset.

Usage

fcrosRead(filename)

Arguments

Value

Output is a matrix or a table containing data.

Author(s)

Doulaye Dembele, doulaye@igbmc.fr

Examples

  # generate random dataset of size 100 x 5
  xdata <- matrix(c(rep(0,600)), ncol = 6);

  xdata[,2:6] <- matrix(rnorm(500,0,1), ncol = 5);

  x0 <- matrix(NA, 100, 0);

  for (i in 1:100) {
      x0[i] <- paste("Obs_",i, sep = "");
  }

  # set column names
  colnames(xdata) <- c("Index", "Col_1", "Col_2", "Col_3", "Col_4", "Col_5");

  # save data in a text file
  write.table(xdata, file = "test2delete.txt", quote = FALSE, sep = "\t",
         eol = "\n", col.names = TRUE, row.names = FALSE);

  # now used fcros.read() to open the file
  mydata <- fcrosRead(file = "test2delete.txt");

  summary(mydata)

Search for the top N changed genes or probes

Description

This function allows to seach for the top N differentially expressed genes or changed probes. It uses the output results obtained using one of the following functions fcros(), fcros2(), fcrosMod(), pfco() or pfcoMod().

Usage

fcrosTopN(af, topN)

Arguments

Value

This function returns a data frame containing 2 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt);
   
   # now select top 10 down and/or up regulated genes
   top10 <- fcrosTopN(af, 10);

   # display thresholds
   top10$alpha
   
   # display index of top10 genes
   fdata[top10$index, 1]
   
   # display fvalue of the top10 genes
   (af$f.value)[top10$index]

Student t-test for detecting differentially expressed genes

Description

The function uses the basic R t.test() function to perform the Student t-test. It should be used for two biological conditions dataset (microarray, or RNA-seq). The Fold changes, statistics and p-values are returned for each gene in the dataset.

Usage

fcrosTtest(xdata, cont, test, log2.opt = 0)

Arguments

Details

Label names appearing in the parameters "cont" and "test" should match column label names of the data matrix "xdata". It is not necessary to use all column label names of the dataset "xdata".

Value

Author(s)

Doulaye Dembele

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcrosTtest()
   at <- fcrosTtest(fdata, cont, test, log2.opt);
   
   # now select some differentially expressed genes
   id.de  <- matrix(0, 1);
   n <- length(at$FC);
   for (i in 1:n) {
       if ((at$p.value)[i] <= 0.0005) { id.de <- rbind(id.de, i); }
   }

   data.de <- fdata[id.de, ];
   nde <- nrow(data.de);

   # now plot the DE genes
   t <- 1:20;
   plot(t, data.de[1, 2:21], type = "l", col = "blue", xlim = c(1,20),
        ylim = c(0,18), main = "Down- and up-regulated genes");
   for (i in 2:nde) {
       lines(t, data.de[i,2:21], type = "l", col = "blue")
   }

Writing the fcros() or pfco() results in a tab delimited text file

Description

This function creates a tab-delimited text file with the results of the function fcros(), fcros2(), fcrosMod(), pfco() or pfcoMod(). The results are the values associated with the parameters "idnames", "ri", "FC", "FC2", "f.value" and "p.value".

Usage

fcrosWrite(af, file = "fcrosResults.txt", thr = 1)

Arguments

Value

This function creates and saves a tab-delimited text file on the disk.

Author(s)

Doulaye Dembele doulaye@igbmc.fr

Examples

    data(fdata);

    rownames(fdata) <- fdata[,1];
    cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
    test <- c("test01", "test02", "test08", "test09", "test05");
    log2.opt <- 0;
    trim.opt <- 0.25;
    af <- fcros(fdata, cont, test, log2.opt, trim.opt);
    af2 <- pfco(fdata, cont, test, log2.opt, trim.opt);

    fcrosWrite(af, file = "test2delete_values.txt");
    fcrosWrite(af2, file = "test2delete2_values.txt");

Example of dataset for the fcros package.

Description

This is a synthetic dataset generated using the "madsim" R package (available from the CRAN web site). Two biological conditions data matrix using 10,000 genes and 20 samples (10 control and 10 test samples) were simulated. 108 and 102 genes of this dataset are down- and up-regulated, respectively.

Usage

data(fdata)

Format

A data frame with 10,000 observations on the following 22 variables.

index:

a text for the genes, unique ID

cont01:

a numeric vector with log2 intensities for control sample 01

cont02:

a numeric vector with log2 intensities for control sample 02

cont03:

a numeric vector with log2 intensities for control sample 03

cont04:

a numeric vector with log2 intensities for control sample 04

cont05:

a numeric vector with log2 intensities for control sample 05

cont06:

a numeric vector with log2 intensities for control sample 06

cont07:

a numeric vector with log2 intensities for control sample 07

cont08:

a numeric vector with log2 intensities for control sample 08

cont09:

a numeric vector with log2 intensities for control sample 09

cont10:

a numeric vector with log2 intensities for control sample 10

test01:

a numeric vector with log2 intensities for test sample 01

test02:

a numeric vector with log2 intensities for test sample 02

test03:

a numeric vector with log2 intensities for test sample 03

test04:

a numeric vector with log2 intensities for test sample 04

test05:

a numeric vector with log2 intensities for test sample 05

test06:

a numeric vector with log2 intensities for test sample 06

test07:

a numeric vector with log2 intensities for test sample 07

test08:

a numeric vector with log2 intensities for test sample 08

test09:

a numeric vector with log2 intensities for test sample 09

test10:

a numeric vector with log2 intensities for test sample 10

DE_status:

a numeric vector with values -1, 0 and 1. Value 0 is used for no change genes, while -1 and 1 are used for down- and up-regulated genes, respectively.

Details

This dataset is obtained using the microarray data simulation model implemented in the package "madsim". A real microarray data, "madsim_test", was used as seed and the number of the control and the test samples were set to 10. The parameter "sdn" was set to 0.3 and all the other parameters in the madsim package were set to their default settings.

References

Dembele D, A flexible microarray data simulation model. Microarrays, 2013, v.2, n.2, pp.115-130

Examples

   data(fdata)

   rownames(fdata) <- fdata[,1];

   op <- par(mfrow = c(2,1));

   #  perform MA plot using samples "cont01" and "cont05"
   A1 <- 0.5*(fdata$cont01 + fdata$cont05);
   M1 <- fdata$cont05 - fdata$cont01;

   plot(A1, M1, col="red", xlim=c(2,18), ylim=c(-5,5),
            main="MA plot with two control samples");
   lines(x = c(2,18), y = c(0,0), col = "blue")
   lines(x = c(2,18), y = c(-1,-1), col = "blue")
   lines(x = c(2,18), y = c(1,1), col = "blue")

   #  perform MA plot using samples "cont01" and "test05"
   A2 <- 0.5*(fdata$cont01 + fdata$test05);
   M2 <- fdata$test05 - fdata$cont01;

   plot(A2, M2, col="red", xlim=c(2,18), ylim=c(-5,5),
            main="MA plot with one control and one test samples");
   lines(x = c(2,18), y = c(0,0), col = "blue")
   lines(x = c(2,18), y = c(-1,-1), col = "blue")
   lines(x = c(2,18), y = c(1,1), col = "blue")
   par(op)

Search for the top N changed genes or probes using f-values

Description

This function allows to seach for the top N differentially expressed genes or changed probes. It uses the f-values obtained using one of the following functions fcros(), fcros2(), fcrosMod(), pfco() or pfcoMod().

Usage

fvalTopN(fval, topN)

Arguments

Value

This function returns a data frame containing 2 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform pfco()
   af <- pfco(fdata, cont, test, log2.opt);
   
   # now select top 10 down and/or up regulated genes
   top10 <- fvalTopN(af$f.value, 10);

   # display thresholds
   top10$alpha
   
   # display index of top10 genes
   fdata[top10$index, 1]
   
   # display fvalue of the top10 genes
   (af$f.value)[top10$index]

Performs a volcano plot of the FCROS/PFCO statistics

Description

This function allows to have a volcano like plot using the output results of the function fcros(), fcros2(), fcrosMod(), pfco() or pfcoMod(): p-values versus robust fold changes (FC2). The p-value are transformed using -log10(), while FC2 are transformed using log2().

Usage

fvalVolcanoPlot(af, thr = 0.05)

Arguments

Value

This function displays on the screen a volcano like plot using the f-values and the robust fold changes (FC2).

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt);
   
   # Volcano plot
   fvalVolcanoPlot(af, thr = 0.01);

Histogram plot function of the fcros package results

Description

This function allows to have a histogram plot. It uses the statistics "ri" or "u1" obtained using one of the following functions: fcros(), fcros2(), fcrosMod(), pfco() or pfcoMod().

Usage

histoPlot(af, nbins = 50)

Arguments

Value

This function plots a histogram on the screen.

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcros() and pfco()
   af <- fcros(fdata, cont, test, log2.opt);
   af2 <- pfco(fdata, cont, test, log2.opt);

   # Histogram plots
   op <- par(mfrow = c(1,2))
      histoPlot(af);
      histoPlot(af2);
   par(op);

Calculation of the mean and the standard deviation of the full or reduced matrix of sorted ranks

Description

This is an internal function for using a C code in the calculation of the mean and the standard deviation of the full or reduced matrix with sorted rank values. The calculations are performed for each row.

Usage

moyStdCalc(rvect, n, m)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

#    data(fdata);

Searching for differentially expressed genes/probes using an approach based on the Perron-Frobenius theorem

Description

Implementation of a method based on fold change and the Perron theorem for detecting differentially expressed genes in a dataset. This function should be used with two biological conditions dataset (microarray or RNA-seq, ...). Using pairwise combinations of samples from the two biological conditions, fold changes (FC) are calculated. For each combination, the FC obtained are sorted in increasing order and corresponding rank values are associated with genes. Then, a statistic is assigned to the robust average ordered rank values for each gene/probe.

Usage

pfco(xdata, cont, test, log2.opt = 0, trim.opt = 0.25)

Arguments

Details

Label names appearing in the parameter "samp" should match with some label names in the columns of the data matrix "xdata". It is not necessary to use all label names appearing in the columns of the dataset matrix.

Value

This function returns a data frame containing 9 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform pfco()
   af <- pfco(fdata, cont, test, log2.opt, trim.opt);

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(0, 1);
   id.up <- matrix(0, 1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown + 1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup + 1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);


   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1,2:21], type = "l", col = "blue", xlim = c(1,20), 
        ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t,data.down[i,2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20), 
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Searching for differentially expressed genes or detecting recurrent copy number aberration probes using an approach based on the Perron-Frobenius theorem

Description

Implementation of a method based on fold change rank and the Perron theorem to search for differentially expressed genes or to detect chromosomal recurrent copy number aberration probes. This function should be used with a matrix of fold changes or ratios from biological dataset (microarray, RNA-seq, ...). The function pfcoMod() is an extention of the function pfco() to a dataset which does not contain replicate samples or to a dataset with one biological condition dataset. Statistics are associated with genes/probes to characterize their change levels.

Usage

pfcoMod(fcMat, samp, log2.opt = 0, trim.opt = 0.25)

Arguments

Details

The label names appearing in the parameter "samp" should match some label names of the columns in the data matrix "xdata". It is not necessary to use all label names appearing in the columns of the dataset matrix.

Value

This function returns a data frame containing 8 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

   data(fdata);
   rownames(fdata) <- fdata[,1];

   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;
   trim.opt <- 0.25;

   # perform pfcoMod()
   fc <- fcrosFCmat(fdata, cont, test, log2.opt, trim.opt);
   m <- ncol(fc$fcMat)
   samp <- paste("Col",as.character(1:m), sep = "");
   fc.val <- cbind(data.frame(fc$fcMat))
   colnames(fc.val) <- samp
   rownames(fc.val) <- fdata[,1]

   af <- pfcoMod(fc.val, samp, log2.opt, trim.opt);

   # now select top 20 down and/or up regulated genes
   top20 <- fcrosTopN(af, 20);
   alpha1 <- top20$alpha[1];
   alpha2 <- top20$alpha[2];
   id.down  <- matrix(0,1);
   id.up <- matrix(0,1);
   n <- length(af$FC);
   f.value <- af$f.value;

   idown <- 1;
   iup <- 1;
   for (i in 1:n) {
       if (f.value[i] <= alpha1) { id.down[idown] <- i; idown <- idown + 1; }
       if (f.value[i] >= alpha2) { id.up[iup] <- i; iup <- iup + 1; }
   }

   data.down <- fdata[id.down[1:(idown-1)], ];
   ndown <- nrow(data.down);
   data.up <- fdata[id.up[1:(iup-1)], ];
   nup <- nrow(data.up);


   # now plot down regulated genes
   t <- 1:20;
   op = par(mfrow = c(2,1));
   plot(t, data.down[1, 2:21], type = "l", col = "blue", xlim = c(1,20),
        ylim = c(0,18), main = "Top down-regulated genes");
   for (i in 2:ndown) {
       lines(t,data.down[i, 2:21], type = "l", col = "blue")
   }

   # now plot down and up regulated genes
   plot(t, data.up[1,2:21], type = "l", col = "red", xlim = c(1,20), 
       ylim = c(0,18), main = "Top up-regulated genes");
   for (i in 2:nup) {
       lines(t, data.up[i,2:21], type = "l", col = "red")
   }
   par(op)

Search for the top N changed genes or probes using p-values

Description

This function allows to seach for the top N differentially expressed genes or changed probes. It uses the p-value. These p-values can be obtained using one of the following functions fcros(), fcros2(), fcrosMod(), pfco(), pfcoMod() or another statistical method.

Usage

pvalTopN(pval, topN)

Arguments

Value

This function returns a data frame containing 2 components

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1]
   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt);
   
   # now select top 10 down and/or up regulated genes
   top10 <- pvalTopN(af$p.value, 12);

   # display thresholds
   top10$alpha
   
   # display index of top10 genes
   fdata[top10$index, 1]
   
   # display fvalue of the top10 genes
   (af$f.value)[top10$index]

Performs a volcano plot of the FCROS/PFCO statistics

Description

This function allows to have a volcano like plot using the output results of the function fcros(), fcros2(), fcrosMod(), pfco() or pfcomod(): p-values versus robust fold changes (FC2). The p-value are transformed using -log10(), while FC2 are transformed using log2().

Usage

pvalVolcanoPlot(af, thr = 0.05)

Arguments

Value

This function displays on the screen a volcano like plot using the p-values and the robust fold changes (FC2).

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kastner P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

   data(fdata);

   rownames(fdata) <- fdata[,1]
   cont <- c("cont01", "cont07", "cont03", "cont04", "cont08");
   test <- c("test01", "test02", "test08", "test09", "test05");
   log2.opt <- 0;

   # perform fcros()
   af <- fcros(fdata, cont, test, log2.opt);
   
   # Volcano plot
   pvalVolcanoPlot(af, thr = 1e-6);

This function computes a score to assess the significance of sequencing values

Description

Implementation of two methods based (1) on the coefficient of variation or (2) on the fold change rank ordering statistics for detecting genes with significant sequencing values (gwssv). A score is obtained for each gene and a threshold allows to select the number of gwssv.

Usage

rankReads(xdata, cont, test, meth=0, Ttimes=10, err=0.1, trim.opt=0,
                        rseed=60)

Arguments

Details

Label names appearing in the parameters "cont" and "test" should match with some label names in the columns of the data matrix "xdata". It is not necessary to use all label names appearing in the columns of the dataset matrix. For a general purpose dataset, one of these parametere can be empty.

Value

This function returns a data frame containing 10 components when meth=1 and 3 components when meth=0

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, manuscript under preparation

Examples

   data(bott);
   cont <- c("SRX033480", "SRX033488", "SRX033481");
   test <- c("SRX033493", "SRX033486", "SRX033494");
   n <- nrow(bott);

   x2 <- tcnReads(bott[,c(cont,test)])
   idx.ok <- (apply(x2, 1, sum) != 0)
   xdata <- x2[,c(cont,test)]
   rownames(xdata) <- bott[,1]
   idx.ok <- (apply(x2, 1, sum) != 0)
   tt2 <- sum(idx.ok)

   raf10.cv <- rankReads(xdata, cont, test, meth=0)
   raf10.pv <- rankReads(xdata, cont, test, meth=1)
   score.cv <- -log10(sort(raf10.cv$score))
   score.pv <- -log10(sort(raf10.pv$score))
   tmp <- scoreThr(score.cv, 2500, 3500)
   tmp

   tmp <- scoreThr(score.pv, 2500, 3500)
   tmp

   op <- par(mfrow = c(1,2))
   plot(score.cv, xlab = "index of genes",
      ylab = "-log10(sorted(score)", main = "rs.cv", type = "l",
      col = "blue", panel.first = grid())
   plot(score.pv, xlab = "index of genes",
      ylab = "-log10(sorted(score)", main = "rs.pv", type = "l",
      col = "blue", panel.first = grid())
   par(op)

Calculation of the FC matrix using pairwise comparisons

Description

This is an internal function for using a C code to calculate a vector form of the matrix of fold changes using pairwise comparison of data samples.

Usage

rmatCalc(fvect, n, m1, m2)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D and Kaster P, Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC bioinformatics, 2014, 15:14

Dembele D and Kastner P, Comment on: Fold change rank ordering statistics: a new method for detecting differentially expressed genes, BMC Bioinformatics, 2016, 17:462

Examples

#    data(fdata);

Calculation of the reduced matrix containing sorted rank values

Description

This is an internal function for using a C code for calculating the reduced matrix of sorted rank values.

Usage

rmatTrim(rvect, n, m, idx, m2)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

#    data(fdata);

Determine numerically a threshold for the ranking score

Description

Given a log 10 transformed of the sorted ranking score values (dscore), this function determines numerically a threshold (inflection point) for significance of sequencing value level. User should specify the lower and the upper bounds of the slope region containing the inflection point.

Usage

scoreThr(dscore, deb, fin)

Arguments

Value

This function returns two values: “pos” and “thr” which are the index or the number of genes with significant values and the score (-log10(score)) threshold

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, manuscript under preparation

Examples

#    data(fdata);

Performs a total count normalization of reads

Description

Given a data table x with count reads, one column for each sample, this function adjust values in such a way they become comparable between samples. User can specify the maximum value for total count reads to be used for each sample.

Usage

tcnReads(x, maxVal=0)

Arguments

Value

This function returns a data table of the same size as input

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Dembele D, manuscript under preparation

Examples

#    data(fdata);

Calculation of the Student one sample test probabilities

Description

This is an internal function for using a C code to perform a Student one sample test for each row of the full or reduced matrix with sorted rank values.

Usage

tprobaCalc(moy, std, n, dl, em)

Arguments

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, Analysis of high biological data using their rank values, Stat Methods Med Res, accepted for publication, 2018

Examples

#    data(fdata);

Compute variance of a beta distribution from data

Description

This is an internal function. Given a vector with values between 0 and 1 and assumed to come from a beta distribution, this function return the variance of the distribution. A trim parameter allow to have a robust value

Usage

varBeta(x, trim.opt)

Arguments

Value

This function returns one value equalt to the variance of the best beta distribution of entry x

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Dembele D, manuscript under preparation

Examples

#    data(fdata);

Transformation of read count values

Description

This function allows to transform the count values associated with Sequencing reads. The purpose of this transformation is to allow applying normal-based microarray-like statistical methods to RNA-seq read counts. Log2 values are returned.

Usage

voomReads(x, Rm=1e+06)

Arguments

Value

This function returns a data matrix of the same size as input matrix x. The values of this matrix are expressed in log2 scale.

Author(s)

Doulaye Dembele doulaye@igbmc.fr

References

Charity W Law, Yunshun Chen, Wei Shi and Gordon K Smyth, voom: precision weights unlock linear model analysis tools for RNA-seq read counts,Genome Biology, 2014, 15R29

Examples

#   data(fdata);