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Comprehensive Meta-Analysis Basic analyses Correlations
 
38:14
Comprehensive Meta-Analysis Basic analyses Correlations
Views: 7559 Michael Borenstein
Comprehensive Meta-Analysis Basic data entry Correlations
 
12:57
Comprehensive Meta-Analysis Basic data entry Correlations Statistics.com
Views: 10838 Michael Borenstein
Jamovi Meta Analysis Module: Correlation Coefficients
 
03:05
Project link: https://github.com/kylehamilton/JamoviMeta
Views: 857 Kyle Hamilton
Conducting a meta-analysis with R
 
13:48
Meta-analysis synthesizes a body of research investigating a common research question. This video provides a practical and non-technical guide showing you how to perform a meta-analysis of correlational datasets. I use a supplementary R script to demonstrate each analytical step described in the paper, which is readily adaptable for people to use for their analyses. While the worked example is the analysis of a correlational dataset, the general meta-analytic process described in this paper is applicable for all types of effect sizes. The paper - http://journal.frontiersin.org/article/10.3389/fpsyg.2015.01549/abstract The script and datasets - https://github.com/dsquintana/corr_meta A podcast episode on meta-analysis issues https://soundcloud.com/everything-hertz/4-meta-analysis-or-mega-silliness
Views: 20229 Daniel Quintana
Comprehensive Meta-Analysis Subgroups
 
33:57
Comprehensive Meta-Analysis Subgroups
Views: 21179 Michael Borenstein
Meta Analysis Excel Correlation
 
03:33
Meta Analysis Excel Basic data entry Correlations With Random Effects Or Fixed Effects methods of Meta Analysis Excel Correlation. Guide Or Tutorial Meta Analysis Excel Correlation
Views: 38 Rus Sujilasari
Meta analysis in Stata
 
09:32
This lecture is part of the Systematic Reviews course that teaches undergraduate students, PhD students and researchers how to build a systematic review or meta-analysis. Don’t hesitate to contact me for help with your review, to conduct a systematic review or meta-analysis or to organise a course on your location. Enjoy the course! Maurice Zeegers (www.systematicreviews.nl)
Views: 2162 Maurice Zeegers
Rare Cancer Meta-Analysis, pt6.2: Importing our genes back into Correlation Engine
 
08:47
This entry is part six in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I use Illumina’s BaseSpace Correlation Engine (CE) to analyze the 130 genes over-expressed in more severe forms of pRCC. I demonstrate how to compare our gene list to the millions of comparisons contained in the ~22,000 curated studies in the CE database. I also go over using the Body Atlas tab to identify correlated tissues and cell lines based on our cluster profile. A text file containing the list of all 130 clustered genes, along with their normalized fold change scores for each comparison, can be downloaded at this Google Drive link: https://drive.google.com/open?id=1G3HLB7OBZJCLpruGU3Jiczs39TkB0_au Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 2018 (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 22 Michael Edwards
Rare Cancer Meta-Analysis, pt.2.1: Finding studies using BaseSpace Correlation Engine
 
06:55
This entry is part two in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I give a demonstration on collecting gene expression data from a single disease using Illumina’s BaseSpace Correlation Engine. I also describe the types of biological information we can obtain from this type of meta-analysis, as well as go over exporting the results to other statistical and visual programs for further work. A link to the actual meta-analysis used in the video in can be obtained in Correlation Engine (must have existing account) by typing in your working domain name into the following URL: https://”your domain name”.ussc.informatics.illumina.com/c/search/adv.nb?ids=853951,14029,79477,152971,56285,14033,451948,153346,14031,153313,451963,14030,14032,451966,451954,451951,13771 Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of Illumina. Any uses of Illumina’s products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from Illumina for these videos, but do receive reimbursement for travel when I speak at Illumina-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 77 Michael Edwards
Part 1 Assessing heterogeneity
 
09:04
Part 1 assessing heterogeneity
Views: 6130 Annette OConnor
Meta-Analysis with multiple outcomes
 
43:51
Comprehensive Meta-Analysis
Views: 15996 Michael Borenstein
Wisdom of Crowds in Oncology_Pt4_Compiling a meta-analysis using CE
 
12:18
This video series is the second part of Laboratory 4: Using Genomics and Bioinformatics in Cancer Research, given on the last day of the Molecular Biology in Clinical Oncology 2017 AACR Workshop. In this tutorial, the bioinformatics program Correlation Engine (BaseSpace/Illumina) is used to analyze 748 genes different in activated (ABC) vs. germinal (GBC) diffuse large B-cell lymphoma (this list was generated from an RNAseq study analyzed in the first section of the lab). We also use the Correlation Engine program to compile a meta-analysis using similar datasets contained in its database. A folder containing the 748 genes different in our original analysis, plus excel files containing a list of genes different in greater then 4, 5 and 6 out of 7 studies looking at activated vs. germinal B-cell lymphoma can be found at this link: https://drive.google.com/open?id=0B4kxx5VqjCBscU9UeHRUbzhHTnM I would like to thank my co-instructors for this course, Drs. Tzu Phang and Robert Stearman from the University of Colorado Denver and Indiana University School of Medicine, respectively. I would also like to thank Illumina Informatics (special thanks to Drs. Hinco Gierman, James Flynn and John Klejnot,) for donating the use of their platform during and after this course. I would also like to thank the American Association for Cancer Research (special thanks to Amy Baran) for once again putting on a fantastic workshop.
Views: 26 Michael Edwards
Running the Wilson Macros for Meta-Analysis in SPSS
 
24:47
How to run the Wilson macros for meta-analysis inside SPSS.
Views: 19629 Blair Johnson
Comprehensive Meta-Analysis Overview, using binary data
 
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Three-minute overview of Comprehensive Meta-Analysis software. This video uses binary data as an example.
Views: 3389 Michael Borenstein
Comprehensive Meta-Analysis Tutorial Means Basic
 
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Comprehensive Meta-Analysis Tutorial Means Basic www.Meta-Analysis.com
Views: 48325 Michael Borenstein
Assessing meta-analysis study quality using R
 
07:09
This video is the first in a series of non-technical guides showing you how to perform a meta-analysis in R. In this video, I'll be walking through the assessment of study quality as a potential moderator variable. We'll use a supplementary R script to demonstrate each analytical step described in the paper, which is readily adaptable for others to use for their analyses. The analysis in R begins at 3 minutes and 20 seconds if you would like to skip ahead, however, I believe the background to why you should look at study quality is important to hear. The script - https://gist.github.com/anonymous/7603301a9df81d8fa24c6f74dba31981 A podcast episode on meta-analysis issues https://soundcloud.com/everything-hertz/4-meta-analysis-or-mega-silliness Add me on Snapchat to see how I do science from day-to-day: ds.quintana 👻
Views: 766 Daniel Quintana
Correlation
 
02:07
Views: 6004 JMPSoftwareFromSAS
Comprehensive Meta-Analysis Basic Analysis Means
 
29:17
Comprehensive Meta-Analysis Basic Analysis Means
Views: 4902 Michael Borenstein
Rare Cancer Meta-Analysis, pt3.2: Importing and formatting the meta-data with Partek GS
 
07:39
This entry is part three in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In these installments, I describe the exported excel file from the meta-analysis in Correlation Engine (CE) and discuss formatting these data to combine into one analysis. I also demonstrate how we can upload this information into the statistical/graphical platform Partek Genomics Suite for further visualization and statistical work. A link to the actual excel file containing the papillary meta-analysis can be accessed at the following Google Drive link: https://drive.google.com/file/d/1FPdAl0UGfs8qf5X8JDeWNf3ghF3bnSX9/view?usp=sharing Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 38 Michael Edwards
Entering data into REVMAN.mp4
 
12:38
REVMAN
Views: 43519 Annette OConnor
Rare Cancer Meta-Analysis, pt.2.2: Setting up the meta-analysis and reviewing genes
 
06:29
This entry is part two in a series of instructional videos detailing a meta-analysis on 8 different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I give a demonstration on collecting gene expression data from a single disease using Illumina’s BaseSpace Correlation Engine. I also describe the types of biological information we can obtain from this type of meta-analysis, as well as go over exporting the results to other statistical and visual programs for further work. A link to the actual meta-analysis used in the video in can be obtained in Correlation Engine (must have existing account) by typing in your working domain name into the following URL: https://”your domain name”.ussc.informatics.illumina.com/c/search/adv.nb?ids=853951,14029,79477,152971,56285,14033,451948,153346,14031,153313,451963,14030,14032,451966,451954,451951,13771 Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of Illumina. Any uses of Illumina’s products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from Illumina for these videos, but do receive reimbursement for travel when I speak at Illumina-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 47 Michael Edwards
Rare Cancer Meta-Analysis, pt3.1: Describing the exported meta-data from CE
 
08:09
This entry is part three in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In these installments, I describe the exported excel file from the meta-analysis in Correlation Engine (CE) and discuss formatting these data to combine into one analysis. I also demonstrate how we can upload this information into the statistical/graphical platform Partek Genomics Suite for further visualization and statistical work. A link to the actual excel file containing the papillary meta-analysis can be accessed at the following Google Drive link: https://drive.google.com/file/d/1FPdAl0UGfs8qf5X8JDeWNf3ghF3bnSX9/view?usp=sharing Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 34 Michael Edwards
Comprehensive Meta-Analysis Basic data entry Means
 
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Comprehensive Meta-Analysis Basic data entry Means Statistics.com Week-1
Views: 14708 Michael Borenstein
Pearson's correlation coefficient in Stata®
 
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Explore how to estimate Pearson's Correlation Coefficient using Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.
Views: 111224 StataCorp LLC
Comprehensive Meta-Analysis (CMA): 入門教學 Meta regression
 
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Tutorial for Comprehensive Meta-Analysis (CMA): 入門教學 Meta regression Biostat, Inc 授權經銷商 SoftHome International ; Software for Science The best softwares reseller in Taiwan 13F, NO. 55, SEC.1, CHIEN KUO N-ROAD, TAIPEI, 10491,TAIWAN [email protected] www.softhome.com.tw 全傑科技股份有限公司 科學軟體世界 臺北市中山區建國北路一段五十五號十三樓 電話Tel: 02-25078298 傳真Fax: 02-25078303 本公司保證所銷售之軟體 皆為原版合法軟體 您可以傳一份,您想要分析的資料,給我們幫您試做看看 CMA Basic Customer0M.mp4
Views: 2492 全傑
11 Meta-analytic regression models explained
 
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What is a meta-analytic regression model, and which type should I use?
Views: 1485 MetaLab
Rare Cancer Meta-Analysis, pt6.3: Comparing our list to all other curated studies in CE
 
06:17
This entry is part six in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I use Illumina’s BaseSpace Correlation Engine (CE) to analyze the 130 genes over-expressed in more severe forms of pRCC. I demonstrate how to compare our gene list to the millions of comparisons contained in the ~22,000 curated studies in the CE database. I also go over using the Body Atlas tab to identify correlated tissues and cell lines based on our cluster profile. A text file containing the list of all 130 clustered genes, along with their normalized fold change scores for each comparison, can be downloaded at this Google Drive link: https://drive.google.com/open?id=1G3HLB7OBZJCLpruGU3Jiczs39TkB0_au Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 2018 (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 27 Michael Edwards
Rare Cancer Meta-Analysis, pt3.3: Visualizing the meta-data with a PCA plot
 
05:03
This entry is part three in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In these installments, I describe the exported excel file from the meta-analysis in Correlation Engine (CE) and discuss formatting these data to combine into one analysis. I also demonstrate how we can upload this information into the statistical/graphical platform Partek Genomics Suite for further visualization and statistical work. A link to the actual excel file containing the papillary meta-analysis can be accessed at the following Google Drive link: https://drive.google.com/file/d/1FPdAl0UGfs8qf5X8JDeWNf3ghF3bnSX9/view?usp=sharing Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 51 Michael Edwards
Correlation between test & simulation using photorealism in META
 
01:33
A video of the Straight front, 35 mph crash test of the VOLVO XC60 (Courtesy of VOLVO Safety Center) is compared side by side with the respective simulation model visualized in META using the advanced photo-realistic capabilities. Original footage used by permission of VOLVO Safety Center: https://www.youtube.com/watch?v=Qa7lzBYIkoA&t=3s
Views: 2512 BETA CAE Systems
B. Meta-Analysis - Dr. A.G. Picciano
 
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This video is an introduction to meta-analysis, a statistical procedure popular in social science and education research.
Views: 11837 Anthony Picciano
Jamovi Meta Analysis Module Example: Beta Version
 
02:49
https://github.com/kylehamilton/JamoviMeta
Views: 301 Kyle Hamilton
Primary & secondary data, meta analysis - Research Methods (7.24) Psychology AQA paper 2
 
03:25
7.24 Primary & secondary data, meta analysis - Research Methods - AQA spec Alevel Psychology, p2 in this video definitions and evaluations of Primary Data, Secondary Data, Meta-analysis If you are a student of A-level AQA psychology I have made these videos for you! They are a full set of videos for every part of the AQA specification from 2015 onwards. They are to be used in preparation for a flipped classroom, revision, self teaching or for anyone who is just interested in psychology in general. I have attempted to make them as simple, focused and accurate as possible, 6 key points for each sub topic, (to match the 6 A01/ knowledge points in the biggest essay you will get, a 16 marker) 2 pieces of evaluative research per sub-topic (with ways to expand these to gain the 10 A03/ Evaluation points available) The channel is an on-going project in my spare time, I'm a full time Psychology A-level teacher teaching over 125 students over A1 and A2. That being said, I'm not perfect, if you spot a mistake or omission, please let me know so I can adapt the next video!
Views: 620 Psych Boost
Jamovi Meta-Analysis Beta
 
00:57
https://github.com/kylehamilton/JamoviMeta
Views: 1100 Kyle Hamilton
Intraclass Correlations
 
07:31
In this tutorial, we go over the basics of the use of intraclass correlations (ICCs). We also show how to run ICCs in SPSS 18 for Mac and menu options are also explained.
Views: 68277 agneslystats
Rare Cancer Meta-Analysis, pt6.1: Creating a summation table of our genes using Partek GS
 
04:33
This entry is part six in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I use Illumina’s BaseSpace Correlation Engine (CE) to analyze the 130 genes over-expressed in more severe forms of pRCC. I demonstrate how to compare our gene list to the millions of comparisons contained in the ~22,000 curated studies in the CE database. I also go over using the Body Atlas tab to identify correlated tissues and cell lines based on our cluster profile. A text file containing the list of all 130 clustered genes, along with their normalized fold change scores for each comparison, can be downloaded at this Google Drive link: https://drive.google.com/open?id=1G3HLB7OBZJCLpruGU3Jiczs39TkB0_au Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 2018 (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 36 Michael Edwards
Introduction to Effect Size
 
02:49
Overview of effect size, the concepts behind it, and how to calculate it. Related blog post: http://www.andysbrainblog.blogspot.com/2013/02/the-will-to-fmri-power.html
Views: 28855 Andrew Jahn
Rare Cancer Meta-Analysis, pt6.4: Using Body Atlas to identify similar tissues and cell types
 
05:48
This entry is part six in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I use Illumina’s BaseSpace Correlation Engine (CE) to analyze the 130 genes over-expressed in more severe forms of pRCC. I demonstrate how to compare our gene list to the millions of comparisons contained in the ~22,000 curated studies in the CE database. I also go over using the Body Atlas tab to identify correlated tissues and cell lines based on our cluster profile. A text file containing the list of all 130 clustered genes, along with their normalized fold change scores for each comparison, can be downloaded at this Google Drive link: https://drive.google.com/open?id=1G3HLB7OBZJCLpruGU3Jiczs39TkB0_au Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May 2018 (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of the companies associated with the bioinformatics tools I use in these videos. Any uses of the products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from the platforms I demonstrate in these videos, but do receive reimbursement for travel when I speak at company-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 28 Michael Edwards
Trading Strategies: Forex Trading Correlations
 
35:36
FX Pairing Trading by Steve Ruffley of InterTrader http://www.financial-spread-betting.com/intertrader/intertrader.html Trading Strategies: Forex Trading Correlations One of, it not the most commonly traded market in the world with around $4 Trillion traded everyday. PLEASE LIKE AND SHARE so we can bring you more! There are two types of currencies 1) Floating 2) Pegged. The most common pairings are GBP/USD, EUR/GBP USD/JPY, USD/CHF EUR/USD/, USD/CAD Some currency pairs will move in the same direction and with same sentiment as others. Like in any other market they will also move in different directions and with a different sentiment to others.
Views: 6037 UKspreadbetting
Comparing Descriptive, Correlational, and Experimental Studies
 
10:45
What are descriptive studies? What are correlational studies? What are experimental studies? What are the similarities and differences between these studies types?
Views: 42315 Brooke Miller
Rare Cancer Meta-Analysis, pt2.3: Describing biogroups identified in the meta-analysis
 
07:23
This entry is part two in a series of instructional videos detailing a meta-analysis on eight different human gene expression studies looking at papillary renal cell carcinoma (pRCC). In this installment, I give a demonstration on collecting gene expression data from a single disease using Illumina’s BaseSpace Correlation Engine. I also describe the types of biological information we can obtain from this type of meta-analysis, as well as go over exporting the results to other statistical and visual programs for further work. A link to the actual meta-analysis used in the video in can be obtained in Correlation Engine (must have existing account) by typing in your working domain name into the following URL: https://”your domain name”.ussc.informatics.illumina.com/c/search/adv.nb?ids=853951,14029,79477,152971,56285,14033,451948,153346,14031,153313,451963,14030,14032,451966,451954,451951,13771 Inspiration for this meta-analysis on papillary kidney cancer came from an upcoming ‘Hackathon’ in May (https://sv.ai/papillary-renal-cell-carcinoma/) that brings together researchers, engineers and computer scientists to try to tackle challenging problems in life sciences. This year they are focusing on papillary renal-cell carcinoma type 1 (p1RCC), a disease that accounts for between 15 to 20% of all kidney cancers. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist. The opinions expressed during this video are mine and may not represent the opinions of Illumina. Any uses of Illumina’s products described in this demonstration may be uses that have not been cleared or approved by the FDA or any other applicable regulatory body. I do not get direct compensation from Illumina for these videos, but do receive reimbursement for travel when I speak at Illumina-sponsored events. Please subscribe to this YouTube channel or sign up to my blog (www.bioinfosolutions.com/blog/) to receive notifications on when the next video in the series is posted. Special thanks goes out to the biotech companies Illumina (Correlation Engine and Cohort Analyzer), Partek Inc. (Partek Genomics Suite) and Elsevier (Pathway Studio) for donating their platforms and providing technical assistance for this bioinformatics series.
Views: 34 Michael Edwards
Confidence in Network Meta-Analysis: How to evaluate study limitations (theory)
 
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Aim: This video explains how to evaluate the impact of study limitations (risk of bias) in the results of network meta-analysis. Details: There is a need to evaluate the credibility of network meta-analysis evidence in a systematic way. We previously developed a framework (CINeMA; Confidence in Network Meta-analysis) to judge the confidence that can be placed in results obtained from a network meta-analysis by adapting and extending the GRADE domains (study limitations, inconsistency, indirectness, imprecision and publication bias). The system is transparent and applicable to any network structure. We are develop a user-friendly web application (called CINeMA) to simplify and speed-up the process. These videos have been prepared by Adriani Nikolakopoulou, Theodore Papakonstantinou and Georgia Salanti
Views: 391 Georgia Salanti
Q/A: Correlation Vs. Causation - Examining TMAO
 
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If you wish to help me out, please donate to my paypal @ PayPal.Me/chrismorales121093 Donations to support this channel can be made here: https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=D3HRHLCAT4XJJ Follow me on IG: christopher_morales_121093 Back in 2011, researchers published a paper proposing that a naturally occurring compound called TMAO (trimethylamine N oxide), most commonly found in red meat, increases the risk of developing heart disease (1). If we use our deductive reasoning skills this means that if red meat consumption elevates TMAO, and elevated TMAO increase the risk of heart disease, we'd see higher rates of heart disease in people that eat more red meat. However the epidemiological evidence examining this question is mixed. For example there was a large meta-analysis study published in 2010 that covered over 1.2 million participants found the contrary. That is the consumption of non-processed red meat was not associated with increased risk of coronary heart disease, stoke, or diabetes. (2) On the other hand, a smaller prospective study including about 121,000 participants from the Nurses Health Study and Health Professionals Follow-up Study did find a positive association between red meat consumption (both fresh and processed) and total mortality, cardiovascular disease (CVD) and even cancer. (3) So for example, let's say a study shows that eating bacon increases your risk of heart disease. If we apply the patterns I described above, these people will tend to eat more processed foods, refined sugars with less fruits and vegetables. They also may drink and smoke more while exercising less. Some studies most mostly those looking at two variables with a large population set generally don't take these confounding factors into consideration. An elevated level of TMAO could reflect perhaps an over consumption of dietary trimethylamine (be it from red meat, or sea food); it could also reflect an impaired excretion of TMAO into the urine, or even an enhanced conversion of TMAO in the liver to undergo it's metabolic breakdown. These's an enzyme called Fmo3 which carries out it's conversion, and theres a number of genetic variants affecting the activity of this enzyme which can found only in certain ethnic groups....the enzyme may also be impaired by numerous types of drugs, or can be overexpressed my excess iron or salt. So as you can see, it's largely oversimplistic to suggest that eating red meat causes elevated TMAO. (4) If I were to propose a study, I'd take a population and split them in half: one group with TMAU (trimethlaminuria) which is a rare metabolic disorder that causes a defect in Fmo3; and the other group being "normal" metabolizers of TMAO. If those with TMAU are in general far healthier by numerous health factors (cancer/heart disease rates, cholesterol, CAD, etc.), then we should create a way to help for TMA oxidation to decrease in normal livers. References: (1) Wang, Z., Klipfell, E., Bennett, B. J., Koeth, R., Levison, B. S., DuGar, B., … Hazen, S. L. (2011). Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature, 472(7341), 57–63. http://doi.org/10.1038/nature0 9922 (2) Micha, R., Wallace, S. K., & Mozaffarian, D. (2010). Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes: A systematic review and meta analysis. Circulation, 121(21), 2271–2283. http://doi.org/10.1161/CIRCULATIONAHA.109.924977 (3) Pan, A., Sun, Q., Bernstein, A. M., Schulze, M. B., Manson, J. E., Stampfer, M. J., … Hu, F. B. (2012). Red Meat Consumption and Mortality: Results from Two Prospective Cohort Studies. Archives of Internal Medicine, 172(7), 555–563. http://doi.org/10.1001/archinternmed.2011.2287 (4)Motika, M.S., Zhang, J., & Cashman, J.R. (2007). Flavin containing monooxygenase 3 and human disease. Expert Opinion in Drug Metabolism Toxicology, 2(6):831-45. https://www.ncbi.nlm.nih.gov/pubmed/18028028 Pictures: TMAO Chemical Structure https://en.wikipedia.org/wiki/Trimethylamine_N-oxide#/media/File:Trimethylaminoxid.svg Artery http://media.renalandurologynews.com/images/2015/06/22/dysfunctionalhdlcvdriskckd_789184.jpg?format=jpg&zoom=1&quality=70&anchor=middlecenter&width=320&mode=pad Fish, Steak, Pork, Eggs http://assets.labroots.com/_public/_files/system/content-articles/images/profile/9302_855x575.jpg
Views: 434 Christopher Morales
Wisdom of Crowds in Oncology_Pt1_Introduction and background on meta-analysis
 
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This video series is the second part of Laboratory 4: Using Genomics and Bioinformatics in Cancer Research, given on the last day of the Molecular Biology in Clinical Oncology 2017 AACR Workshop. In this tutorial, the bioinformatics program Correlation Engine (BaseSpace/Illumina) is used to analyze 748 genes different in activated (ABC) vs. germinal (GCB) diffuse large B-cell lymphoma (this list was generated from an RNAseq study analyzed in the first section of the lab). We also use the Correlation Engine program to compile a meta-analysis using similar datasets contained in its database. A folder containing the 748 genes different in our original analysis, plus excel files containing a list of genes different in greater then 4, 5 and 6 out of 7 studies looking at activated vs. germinal B-cell lymphoma can be found at this link: https://drive.google.com/open?id=0B4kxx5VqjCBscU9UeHRUbzhHTnM I would like to thank my co-instructors for this course, Drs. Tzu Phang and Robert Stearman from the University of Colorado Denver and Indiana University School of Medicine, respectively. I would also like to thank Illumina Informatics (special thanks to Drs. Hinco Gierman, James Flynn and John Klejnot,) for donating the use of their platform during and after this course. I would also like to thank the American Association for Cancer Research (special thanks to Amy Baran) for once again putting on a fantastic workshop.
Views: 108 Michael Edwards

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