Title: Disease-Related Differential Proteins and Co-Expression Network
Description: Functions designed to connect disease-related differential proteins and
co-expression network. It provides the basic statics analysis included t test, ANOVA analysis.
The network construction is not offered by the package, you can used 'WGCNA' package which you
can learn in Peter et al. (2008) <doi:10.1186/1471-2105-9-559>. It also provides module analysis
included PCA analysis, two enrichment analysis, Planner maximally filtered graph extraction and
Imports: stats, ggplot2, ggalt, MEGENA, igraph, Hmisc, utils,
grDevices, plyr, scales, grid, VennDiagram
Suggests: WGCNA, Biostrings, impute, ggfortify

Title: Disease-Drived Differential Proteins Co-Expression Network

Title: Learning Principal Graphs with DDRTree
Author: Xiaojie Qiu, Cole Trapnell, Qi Mao, Li Wang
Description: Project data into a reduced dimensional space and construct a principal graph from the reduced dimension.

Description: Provides an implementation of the framework of reversed graph embedding (RGE) which projects data into a reduced dimensional space while constructs a principal tree which passes through the middle of the data simultaneously. DDRTree shows superiority to alternatives (Wishbone, DPT) for inferring the ordering as well as the intrinsic structure of the single cell genomics data. In general, it could be used to reconstruct the temporal progression as well as bifurcation structure of any datatype.

Title: Distance & Density-Based Outlier Detection
Author: Jacob H. Madsen <jacob.madsen1@mail.com>
Maintainer: Jacob H. Madsen <jacob.madsen1@mail.com>
Description: Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.