Editing
Ns126:Calendar/NOTES/2015-12-30
(section)
Jump to navigation
Jump to search
Warning:
You are not logged in. Your IP address will be publicly visible if you make any edits. If you
log in
or
create an account
, your edits will be attributed to your username, along with other benefits.
Anti-spam check. Do
not
fill this in!
===6, Tissue mapping algorithm for plasma cancer DNA=== In the present, RRBS were most widely applied in the methylation based biomarker identification. In order to identify most stable and high compatible cancer diagnosis biomarker and tissue mapping biomarker, hot covered methylation regions (HCMR) were defined to describe the genomic regions which were highly covered by GWBS and RRBS. The overlapped regions among the following three scenario were defined as HCMR in present study. 1,197,170 genomic regions covered by at least 50% RRBS data from Encode Project (101 samples). 6688 genomic regions covered by at least 50% RRBS data from Kunβs lab (108 samples)<span style="color:red">(WHY THERE IS SUCH A BIG DIFFERENCE BETWEEN ENCODE DATA AND OUR OWN DATA? RRBS SHOULD COVER THE SAME GENOMIC REGIONS NEAR MSPI CUTTING SITES, RIGHT?)</span>. 54209 genomic regions covered by at least 50% WGBS data from collected public dataset (61 samples as supplementary Table shown). Eventually, 3781 genomic regions were selected and these regions were considered as high frequent cover regions by RRBS and GWBS. <span style="color:blue">Response: yes, these regions would be high overlap, however, when you have large number of samples, some regions would be missing in more than 50% or more samples. these regions can't not put into the statistic model without doing imputation, here, I don't want to do imputation, therefore, I remove these regions having high missing probability in 50% samples. The distribution of the RRBS regions in all the samples can be found in our supplementary</span> 2111 Tissue specific MHL regions within HCMR were obtained by filtered with the GSI> 0.3 and within HCMR regions so that we could select high frequent biomarkers which can be detected in RRBS and GWBS. Two stage biomarker identification design were proposed in the present study. In the first stage, RRBS data from 10 colon cancer, 10 lung cancer, 10 pancreatic cancer and 20 normal individuals were collected. To keep the balance of the sample in different category. 20 samples were randomly separated into 2 groups and then random forest prediction model were built in the tissue mapping process. 225 MHL features positively selected in the random forest prediction model (see supplementary Table) <nowiki> Colon Lung Pancreas Normal2 Sensitivity Specificity Colon 6.89 2.55 0.46 0.1 68.90% 95.85% Lung 3.76 5.96 0.2 0.08 59.60% 96.15% Pancreas 1.06 0.46 8.25 0.23 82.50% 95.65% Normal 0.83 0.77 0.87 17.53 - 87.65% Random forest were conducted with 100 times to make sure the prediction model were with high reproducibility and the average prediction number were recorded in the tables. </nowiki> In the second stage, RRBS data from 20 colon cancer, 19 lung cancer were collected. 58 MHL features were positively selected in the random forest prediction model (see supplementary Table) <nowiki> Colon Lung True Positive Sensitivity Colon 19.05 0.95 95.96% 95.25% Lung 0.8 18.2 95.04% 95.79% Random forest were conducted with 100 times to make sure the prediction model were with high reproducibility and the average prediction number were recorded in the tables. </nowiki> When we merge the stage 1 and stage 2 samples together (pancreatic cancer plasma excluded since the sample size incomparable with other samples). <nowiki> Colon Lung Normal Sensitivity Specificity Colon(30) 21.48 1.42 7.1 71.60% 99.95% Lung(29) 2.85 19.07 7.08 68.86% 99.40% Normal(20) 0.01 0.12 19.87 - 99.35% </nowiki>
Summary:
Please note that all contributions to ZhangLabWiki may be edited, altered, or removed by other contributors. If you do not want your writing to be edited mercilessly, then do not submit it here.
You are also promising us that you wrote this yourself, or copied it from a public domain or similar free resource (see
ZhangLabWiki:Copyrights
for details).
Do not submit copyrighted work without permission!
Cancel
Editing help
(opens in new window)
Navigation menu
Personal tools
Not logged in
Talk
Contributions
Create account
Log in
Namespaces
Page
Discussion
English
Views
Read
Edit
View history
More
Search
Navigation
Main Page
Current events
Recent changes
Random page
Investigators
Matt Cai
Song Chen
Eric Chu
Dinh Diep
Elizabeth Duong
Shicheng Guo
Alan Fung
Daniel Jacobsen
Blue Lake
Huy Lam
Alice Li
Andrew Richards
Brandon Sos
Chris Wei
Yan Wu
Kun Zhang
Tools
What links here
Related changes
Special pages
Page information