ಮಾಧ್ಯಮ
- ಟ್ವೀಟ್ಗಳು
- ಟ್ವೀಟ್ಗಳು & ಪ್ರತಿಕ್ರಿಯೆಗಳು
- ಮಾಧ್ಯಮ, ಪ್ರಸ್ತುತ ಪುಟ.
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Check out these sessions at
@AmstatNews Symposium on Data Science and Statistics (5/31/2019, Seattle)@TiffanyTimbers,@stephaniehicks, & me: Democratizing Data Science with Workflows@matthewnmccall &@LeviWaldron1: Interoperability: Your R Package Can Depend on Its Friendspic.twitter.com/DjktLNR2UC
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
Here's the teaser for what we build out from transcriptome checksums, so with no input from user https://www.youtube.com/watch?v=-_33LG2LY2o … (thx again to
@fiamh for contributing support letter for this work!) -
First paper from
@anqiz91 in the lab now published in Bioinformatics. Great job Anqi! Method is implemented in Bioconductor pkg apeglm (Approximate Posterior Estimation for GLM) for 1+ year. It can be used via lfcShrink() in DESeq2 https://doi.org/10.1093/bioinformatics/bty895 …pic.twitter.com/elaT8wiwdV
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
Finally we have a hex sticker, an evolution on tximport. Thanks to contributions to tximeta pkg from
@nomad421@CSoneson@PeteHaitch Bioc team (Lori Shepard, Martin Morgan) & Vince Careypic.twitter.com/4vkJg5nKFZ
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
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@Bioconductor 3.8 is released, which means so is tximeta! this idea came up more than 2 years ago, to auto-populate metadata for Salmon quant directories. the goal is no more guessing for the data you quantified earlier in a project, or from public archive. here's a demopic.twitter.com/6r1yoNcIyjಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
cool! this looks like a good idea (re: modeling disp ~ mu) and it's the direction we've moved in with modeling effect sizes as well (apeglm uses Student's t). Normal has too thin tails for (most) genomics data. Either a mixture prior as in ashr or something like Student's tpic.twitter.com/tHHefzPXTq
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Great statement from the Dean of Gillings School of Public Health https://cher.unc.edu/files/2018/08/Silent-Sam-Rimer-letter.pdf …pic.twitter.com/S0lakwvJIQ
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thanks for refs!! we'll be running addt'l methods for comparison. this was hardest part, but useful for others for our null (permuted) data, it didn't seem like outlier samples but high-dim overfitting (by inspection, the full data looks good, but it's finding patterns in noise)pic.twitter.com/ae6cPP5n4O
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Plot the contributions from each assay to the common variation / latent space, use data-splitting to assess stability. Outliers in this plot worth investigating: may be biological, or sample swaps. Drop in cor may indicate overfitting. Comparing Sparse multiCCA, JIVE, and MOFApic.twitter.com/nlGo74PTJm
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
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Now
@CSoneson on Junction Coverage Compatibility (JCC) score, diving into visuals of coverage of a gene with GTEx portal, asks, how can we automate this?#Bioc2018pic.twitter.com/yK8y5FIPxS
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
Elana Fertig
@FertigLab on using various matrix factorization techniques for ‘omics data#Bioc2018pic.twitter.com/FXNFlKPMsP
ಈ ಥ್ರೆಡ್ ತೋರಿಸಿ -
“Statistical Analysis and Comprehension of the Human Cell Atlas in R /
@Bioconductor ...” with@stephaniehicks and@drisso1893 begins at#Bioc2018pic.twitter.com/tTZcBxFftE
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Lelah Haghverdi from EMBL presenting a classic topic in high throughput data analysis
#CSAMA2018pic.twitter.com/DJSE7lE0Y4
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@BartDeplancke begins his lecture on regulatory variation through variable chromatin modules during “Statistical Methods for Functional Genomics”@cshlmeetingspic.twitter.com/6Qx3KofOjO
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@nomad421 describing concepts for transcript quantification during “Statistical Methods for Functional Genomics”@cshlmeetingspic.twitter.com/DJkx2KmID0
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@seandavis12 motivating use of the@Bioconductor project during “Statistical Methods for Functional Genomics”@cshlmeetingspic.twitter.com/GwmwRQsfy8
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