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Data-analysis strategies for image-based cell profiling
Juan C Caicedo,
Sam Cooper,
Florian Heigwer,
Scott Warchal,
Peng Qiu,
Csaba Molnar,
Aliaksei S Vasilevich,
Joseph D Barry,
Harmanjit Singh Bansal,
Oren Kraus,
Mathias Wawer,
Lassi Paavolainen,
Markus D Herrmann,
Mohammad Rohban,
Jane Hung,
Holger Hennig,
John Concannon,
Ian Smith,
Paul A Clemons,
Shantanu Singh,
Paul Rees 
,
Peter Horvath,
Roger G Linington,
Anne E Carpenter
,
Peter Horvath,
Roger G Linington,
Anne E Carpenter
Nature Methods, Volume: 14, Issue: 9, Pages: 849 - 863
Swansea University Author:
Paul Rees
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DOI (Published version): 10.1038/nmeth.4397
Abstract
Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involv...
| Published in: | Nature Methods |
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| ISSN: | 1548-7091 1548-7105 |
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2017
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa35840 |
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<?xml version="1.0"?><rfc1807><datestamp>2020-09-29T11:47:03.2021089</datestamp><bib-version>v2</bib-version><id>35840</id><entry>2017-09-29</entry><title>Data-analysis strategies for image-based cell profiling</title><swanseaauthors><author><sid>537a2fe031a796a3bde99679ee8c24f5</sid><ORCID>0000-0002-7715-6914</ORCID><firstname>Paul</firstname><surname>Rees</surname><name>Paul Rees</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2017-09-29</date><deptcode>MEDE</deptcode><abstract>Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.</abstract><type>Journal Article</type><journal>Nature Methods</journal><volume>14</volume><journalNumber>9</journalNumber><paginationStart>849</paginationStart><paginationEnd>863</paginationEnd><publisher/><issnPrint>1548-7091</issnPrint><issnElectronic>1548-7105</issnElectronic><keywords>Image processing, Machine learning</keywords><publishedDay>1</publishedDay><publishedMonth>9</publishedMonth><publishedYear>2017</publishedYear><publishedDate>2017-09-01</publishedDate><doi>10.1038/nmeth.4397</doi><url/><notes>2018: This work is the result of a collaboration between 19 laboratories worldwide at the forefront of cell image high-content analysis to develop a best practice protocol together with the open-access tools required to reliably measure cell phenotypes, signaling, cycle information etc. This is fast becoming the protocol which will be used as a standard that researchers will have to comply to in terms of quality control, data reporting and transparency to publish this type of analysis. The work is culmination of many years work with the Broad Institute of MIT and Harvard funded by several BBSRC, EPSRC and NSF grants.</notes><college>COLLEGE NANME</college><department>Biomedical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDE</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-09-29T11:47:03.2021089</lastEdited><Created>2017-09-29T12:58:13.3998084</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Biomedical Engineering</level></path><authors><author><firstname>Juan C</firstname><surname>Caicedo</surname><order>1</order></author><author><firstname>Sam</firstname><surname>Cooper</surname><order>2</order></author><author><firstname>Florian</firstname><surname>Heigwer</surname><order>3</order></author><author><firstname>Scott</firstname><surname>Warchal</surname><order>4</order></author><author><firstname>Peng</firstname><surname>Qiu</surname><order>5</order></author><author><firstname>Csaba</firstname><surname>Molnar</surname><order>6</order></author><author><firstname>Aliaksei S</firstname><surname>Vasilevich</surname><order>7</order></author><author><firstname>Joseph D</firstname><surname>Barry</surname><order>8</order></author><author><firstname>Harmanjit Singh</firstname><surname>Bansal</surname><order>9</order></author><author><firstname>Oren</firstname><surname>Kraus</surname><order>10</order></author><author><firstname>Mathias</firstname><surname>Wawer</surname><order>11</order></author><author><firstname>Lassi</firstname><surname>Paavolainen</surname><order>12</order></author><author><firstname>Markus D</firstname><surname>Herrmann</surname><order>13</order></author><author><firstname>Mohammad</firstname><surname>Rohban</surname><order>14</order></author><author><firstname>Jane</firstname><surname>Hung</surname><order>15</order></author><author><firstname>Holger</firstname><surname>Hennig</surname><order>16</order></author><author><firstname>John</firstname><surname>Concannon</surname><order>17</order></author><author><firstname>Ian</firstname><surname>Smith</surname><order>18</order></author><author><firstname>Paul A</firstname><surname>Clemons</surname><order>19</order></author><author><firstname>Shantanu</firstname><surname>Singh</surname><order>20</order></author><author><firstname>Paul</firstname><surname>Rees</surname><orcid>0000-0002-7715-6914</orcid><order>21</order></author><author><firstname>Peter</firstname><surname>Horvath</surname><order>22</order></author><author><firstname>Roger G</firstname><surname>Linington</surname><order>23</order></author><author><firstname>Anne E</firstname><surname>Carpenter</surname><order>24</order></author></authors><documents><document><filename>35840__17626__c2fc702ecf6e43f3bf5b0802520f7deb.pdf</filename><originalFilename>nmeth.4397.pdf</originalFilename><uploaded>2020-07-02T12:05:38.7523728</uploaded><type>Output</type><contentLength>1781125</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><embargoDate>2017-09-01T00:00:00.0000000</embargoDate><documentNotes>Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
| spelling |
2020-09-29T11:47:03.2021089 v2 35840 2017-09-29 Data-analysis strategies for image-based cell profiling 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2017-09-29 MEDE Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences. Journal Article Nature Methods 14 9 849 863 1548-7091 1548-7105 Image processing, Machine learning 1 9 2017 2017-09-01 10.1038/nmeth.4397 2018: This work is the result of a collaboration between 19 laboratories worldwide at the forefront of cell image high-content analysis to develop a best practice protocol together with the open-access tools required to reliably measure cell phenotypes, signaling, cycle information etc. This is fast becoming the protocol which will be used as a standard that researchers will have to comply to in terms of quality control, data reporting and transparency to publish this type of analysis. The work is culmination of many years work with the Broad Institute of MIT and Harvard funded by several BBSRC, EPSRC and NSF grants. COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2020-09-29T11:47:03.2021089 2017-09-29T12:58:13.3998084 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Juan C Caicedo 1 Sam Cooper 2 Florian Heigwer 3 Scott Warchal 4 Peng Qiu 5 Csaba Molnar 6 Aliaksei S Vasilevich 7 Joseph D Barry 8 Harmanjit Singh Bansal 9 Oren Kraus 10 Mathias Wawer 11 Lassi Paavolainen 12 Markus D Herrmann 13 Mohammad Rohban 14 Jane Hung 15 Holger Hennig 16 John Concannon 17 Ian Smith 18 Paul A Clemons 19 Shantanu Singh 20 Paul Rees 0000-0002-7715-6914 21 Peter Horvath 22 Roger G Linington 23 Anne E Carpenter 24 35840__17626__c2fc702ecf6e43f3bf5b0802520f7deb.pdf nmeth.4397.pdf 2020-07-02T12:05:38.7523728 Output 1781125 application/pdf Version of Record true 2017-09-01T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution 4.0 (CC-BY) Licence. true eng https://creativecommons.org/licenses/by/4.0/ |
| title |
Data-analysis strategies for image-based cell profiling |
| spellingShingle |
Data-analysis strategies for image-based cell profiling Paul Rees |
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Data-analysis strategies for image-based cell profiling |
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Data-analysis strategies for image-based cell profiling |
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Data-analysis strategies for image-based cell profiling |
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Data-analysis strategies for image-based cell profiling |
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Data-analysis strategies for image-based cell profiling |
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537a2fe031a796a3bde99679ee8c24f5 |
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537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees |
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Paul Rees |
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Juan C Caicedo Sam Cooper Florian Heigwer Scott Warchal Peng Qiu Csaba Molnar Aliaksei S Vasilevich Joseph D Barry Harmanjit Singh Bansal Oren Kraus Mathias Wawer Lassi Paavolainen Markus D Herrmann Mohammad Rohban Jane Hung Holger Hennig John Concannon Ian Smith Paul A Clemons Shantanu Singh Paul Rees Peter Horvath Roger G Linington Anne E Carpenter |
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Nature Methods |
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Swansea University |
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1548-7091 1548-7105 |
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10.1038/nmeth.4397 |
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Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences. |
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2017-09-01T03:44:45Z |
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1763752107259723776 |
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11.028886 |