Drag a rectangle around an area of interest to zoom in.
If anyone is interested in a more general version (to take in custom data), or would like me to correct something, leave a comment on the gist.
========================================== ========================================== | |
TMUX COMMAND WINDOW (TAB) | |
========================================== ========================================== | |
List tmux ls List ^b w | |
New -s <session> Create ^b c | |
Attach att -t <session> Rename ^b , <name> | |
Rename rename-session -t <old> <new> Last ^b l (lower-L) | |
Kill kill-session -t <session> Close ^b & |
## data input (number of reads mapped to each category) | |
total=100 | |
rRNA=5 # mapped to nuclear rRNA regions | |
mtRNA=7 # mapped to mitochondria genome | |
# for the rest of above, then we divide into different category, like http://www.biomedcentral.com/1741-7007/8/149 did. | |
intergenic=48 | |
introns=12 | |
exons=30 | |
upstream=3 | |
downstream=6 |
""" | |
Multiclass SVMs (Crammer-Singer formulation). | |
A pure Python re-implementation of: | |
Large-scale Multiclass Support Vector Machine Training via Euclidean Projection onto the Simplex. | |
Mathieu Blondel, Akinori Fujino, and Naonori Ueda. | |
ICPR 2014. | |
http://www.mblondel.org/publications/mblondel-icpr2014.pdf | |
""" |
#' When plotting multiple data series that share a common x axis but different y axes, | |
#' we can just plot each graph separately. This suffers from the drawback that the shared axis will typically | |
#' not align across graphs due to different plot margins. | |
#' One easy solution is to reshape2::melt() the data and use ggplot2's facet_grid() mapping. However, there is | |
#' no way to label individual y axes. | |
#' facet_grid() and facet_wrap() were designed to plot small multiples, where both x- and y-axis ranges are | |
#' shared acros all plots in the facetting. While the facet_ calls allow us to use different scales with | |
#' the \code{scales = "free"} argument, they should not be used this way. | |
#' A more robust approach is to the grid package grid.draw(), rbind() and ggplotGrob() to create a grid of | |
#' individual plots where the plot axes are properly aligned within the grid. |
human | mouse | |
---|---|---|
A1BG | A1bg | |
A1CF | A1cf | |
A2LD1 | A2ld1 | |
A2M | A2m | |
A4GALT | A4galt | |
A4GNT | A4gnt | |
AAAS | Aaas | |
AACS | Aacs | |
AADAC | Aadac |
Drag a rectangle around an area of interest to zoom in.
If anyone is interested in a more general version (to take in custom data), or would like me to correct something, leave a comment on the gist.
# slide 4 | |
curl https://s3.amazonaws.com/gemini-tutorials/trio.trim.vep.vcf.gz > trio.trim.vep.vcf.gz | |
curl https://s3.amazonaws.com/gemini-tutorials/recessive.ped > dominant.ped | |
gemini load --cores 4 \ | |
-v trio.trim.vep.vcf.gz \ | |
-t VEP \ | |
--skip-gene-tables \ | |
-p dominant.ped \ | |
trio.trim.vep.dominant.db |
#!/usr/bin/env python | |
import sys | |
import pprint | |
def make_grantham_dict(grantham_mat_file): | |
""" | |
Citation: http://www.ncbi.nlm.nih.gov/pubmed/4843792 | |
Provenance: http://www.genome.jp/dbget-bin/www_bget?aaindex:GRAR740104 |
dad | mom | kid | Inheritance description |
---|---|---|---|
HOM_REF | HOM_REF | HOM_REF | Expected |
HOM_REF | HOM_REF | HET | Mendelian violation (plausible de novo) |
HOM_REF | HOM_REF | HOM_ALT | Mendelian violation (implausible de novo) |
HOM_REF | HOM_ALT | HOM_REF | Mendelian violation (uniparental disomy) |
HOM_REF | HOM_ALT | HET | Expected |
HOM_REF | HOM_ALT | HOM_ALT | Mendelian violation (uniparental disomy) |
HOM_REF | HET | HOM_REF | Expected |
HOM_REF | HET | HET | Expected |