Courant Institute of Mathematical Sciences, New York University,
251 Mercer Street, New York, NY 10012.
Phone: (212) 998-3303, email: rangan (at) cims (dot) nyu (dot) edu
Office Hours Fall 2015: Thursday 1:30pm-3:25pm
Room: 1123 WWH
Research Interests: Applications of numerical-analysis and scientific-computing to the biological sciences.
(last updated 02/15/18)
List of publications
(last updated 02/15/18)
Recently I've been developing a 'loop-counting' algorithm for biclustering gene-expression data. I believe the results are rather promising.
Link to paper
(last updated 05/14/18)
Here is a tutorial which presents an example of this algorithm applied to a standard data set (last updated 07/22/15):
(tutorial in pdf)
,(tutorial in pptx)
A much more detailed explanation of this loop-counting algorithm is given in:
The files associated with the tutorial above are bundled into the following archives:
: This "level-0" archive contains all the basic matlab files needed to generate the output shown in the presentation. Each of the matlab files is documented internally (e.g., you can run '>> help tutorial_w1;' to see what the file tutorial_w1.m does). Note that this archive does not include the output itself. Thus, some runtime will be necessary if you want to bicluster everything yourself.
: Just like the "level-0" archive, this "level-1" archive contains the matlab files needed to generate the output shown in the presentation. In addition, the "level-1" archive also contains the output for the original data. This archive does not include all the label-shuffled trials used to generate p-values for the original data. Thus, some runtime will be necessary if you want to generate p-values yourself.
: As above, this "level-2" archive contains all files needed to generate the output. In addition, the "level-2" archive also contains all the output for both the original data and the label-shuffled permutations. If you download this archive you should be able to immediately generate the summary plots by running 'tutorial_summarize' or 'tutorial_plot'.
A significantly more efficient implementation of this algorithm (written in C) is available at:
(last updated 09/26/18)
lakcluster_ver18 on github
This implementation also includes several subroutines which perform binary vector-vector, matrix-vector and matrix-matrix operations.