Title: FCROS for detecting differentially expressed genes
Description: A fold change ranks ordering statistics based method for
detecting differentially expressed genes in two biological
conditions transcriptomic data set. The variation in levels of
fold change calculated using several combinations on pairs from
biological conditions is used to associate a statistics to

Description: A fold change ranks ordering statistics based method
for detecting differentially expressed genes. The
variation in levels of fold change calculated using
several combinations on pairs from biological conditions
is used to associate statistics to genes.

Description: A fold change ranks ordering statistics based method for detecting differentially
expressed genes. The variation in levels of fold change or ratios calculated using
biological conditions is used to associate statistic to genes.

Title: FCROS to search for differentially expressed genes and to detect
recurrent chromosomal copy number aberrations.
Description: A fold change rank ordering statistics method is presented to search for
differentially expressed genes and to detect recurrent chromosomal copy number aberrations.
This method may be useful for high-throughput biological data (microarray, sequencing, ...).
Probabilities are associated with genes in the dataset and there is no problem of multiple

Title: A Method to Search for Differentially Expressed Genes and to
Detect Recurrent Chromosomal Copy Number Aberrations
Probabilities are associated with genes/probes in the dataset and there is no problem of
multiple tests when using this method. For aCGH data, segmentation results are obtained by
merging the significant probes detected.

Description: A fold change rank based method is presented to search for

Description: A fold change rank based method is presented to search for genes with changing
expression and to detect recurrent chromosomal copy number aberrations. This
method may be useful for high-throughput biological data (micro-array, sequencing, ...).
Probabilities are associated with genes or probes in the data set and there is no
problem of multiple tests when using this method. For array-based comparative genomic
hybridization data, segmentation results are obtained by merging the significant