README.md

In dncR/MLSeq: Machine Learning Interface for RNA-Seq Data

Introduction

MLSeq is an R/BIOCONDUCTOR package, which provides over 90 algorithms including support vector machines (SVM),random forest (RF), classification and regression trees (CART), Poisson and Negative Binomial Linear Discriminant Analysis (PLDA, NBLDA) and voom-based classifiers (voomDLDA, voomNSC, etc.) for the classification of sequencing data. MLSeq requires a count table as an input which contains the number of reads mapped to each transcript for each sample. This kind of count data can be obtained from RNA-Seq experiments, also from other sequencing experiments such as DNA or ChIP-sequencing. MLSeq includes both normalization (e.g deseq median ratio, trimmed mean of M values) and transformation (variance stabiliation transformation, regularized logarithmic transformation, etc.) techniques which can be performed through classification process. Although the main purpose of MLSeq is to classify samples using a count matrix from RNA-Sequencing data, some of the classifiers which are called sparse classifiers such as PLDA and voomNSC can be used to detect significant features.

To install the MLSeq package in R:

```{r, eval = FALSE, message=FALSE, warning=FALSE} if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")

BiocManager::install("MLSeq")

If you use MLSeq package in your research, please cite it as below:

> Goksuluk D, Zararsiz G, Korkmaz S, Eldem V, Zararsiz GE, Ozcetin E, Ozturk A, Karaagaoglu AE. MLSeq: Machine
  learning interface for RNA-sequencing data. Computer Methods and Programs in Biomedicine. 2019, 175:223-231.


To get BibTeX entry for LaTeX users, type the following:

```{r, eval = FALSE}
citation("MLSeq")

Please contact us, if you have any questions or suggestions:

[e-mail] [e-mail] [e-mail]

News:

Major changes in version 2.x.y

• Functions are reconstructed using S4 systems and new classes such as MLSeq, MLSeqMetaData and MLSeqModelInfo.
• New classifiers from caret package are now available for MLSeq. These functions can be used for transformed continuous data using one of transformation techniques which are provided by MLSeq's classification algorithms.
• A complete list of available classifiers can be viewed using availableMethods() and printAvailableMethods().
• New setter and getter functions are included.
• Predictions are now evaluated usin generic function predict(...). The older function predictClassify(...) can also be used for predictions.
• For more details see package manuals.

dncR/MLSeq documentation built on May 17, 2020, 6:45 p.m.