Sequencing data ====> Fancy figures in the paper.
The missing pieces? ๐ ๐งต
#rstats #computationalbiology
The missing pieces? ๐ ๐งต
#rstats #computationalbiology
1/ Quality control of the data. Any technical bias? sequencing depth difference?
2/ UNIX tools: alignment ---> quantification---> a table or a matrix
3/ downstream data analysis. First exploratory data analysis. PCA is always the first step to check batch effect and sample swaps.
4/ search bioconductor for a tool for your data. Almost every type of genomics data have a tool on bioconductor buff.ly/3yRtBm3
5/ learn tidyverse buff.ly/2PVNNeK and complexheatmap buff.ly/40nHNPv and you will be able to make most of the figures.
6/ try new analysis tools/ new analysis methods to try to reveal some biological information. Santiy check at every step. Talk to biology experts to see if the results make sense or not. Rinse and repeat.
7/ that's a wrap. If you enjoy this thread. retweet it to your audience. Join my newsletter to learn computational biology buff.ly/3l0kI6s
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