Stochastic Regression and Peak Delineation with Flow Cytometry Data
This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty. By separating these two components, the signal and noise can be compared independently to evaluate measurement quality across different experimental conditions. The files contain experiments from a single stock of Escherichia coli cells that was diluted to different concentrations, stained with Hoechst33342, and acquired on a CytoFLEX LX under the same acquisition conditions. ?Control_Hoechst? is a biologic control sample stained only with Hoechst. ?RainbowBeads? is a control of hard-dyed fluorescent beads with 8 distinct peaks of known fluorescent intensities per manufacturer documentation. ?Test_double? indicates test samples with double fluorescent probe staining, the fractional number (e.g. 0.7) indicates the dilution factor from the stock, and the integer at the end represents the technical replicate.The downloaded Exp__20230921_1_Cyto-A-journal.zip file contains 14 files in .fcs format, which requires suitable software to read/analyze data (i.e., FCS Express).
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[
"006:55"
]
|
| contactPoint |
{
"fn": "Sandra M. Da Silva",
"hasEmail": "mailto:sandra.dasilva@nist.gov"
}
|
| description | This data repository contains original files (fcs) of flow cytometry experiments. The data was used to demonstrate the use of stochastic regression to quantify subpopulations of cells that have distinctly different genome copies per cell within a heterogenous population of Escherichia coli (E. coli) cells. This new approach gives estimates of signal and noise, the former of which is used for analysis, and the latter is used to quantify uncertainty. By separating these two components, the signal and noise can be compared independently to evaluate measurement quality across different experimental conditions. The files contain experiments from a single stock of Escherichia coli cells that was diluted to different concentrations, stained with Hoechst33342, and acquired on a CytoFLEX LX under the same acquisition conditions. ?Control_Hoechst? is a biologic control sample stained only with Hoechst. ?RainbowBeads? is a control of hard-dyed fluorescent beads with 8 distinct peaks of known fluorescent intensities per manufacturer documentation. ?Test_double? indicates test samples with double fluorescent probe staining, the fractional number (e.g. 0.7) indicates the dilution factor from the stock, and the integer at the end represents the technical replicate.The downloaded Exp__20230921_1_Cyto-A-journal.zip file contains 14 files in .fcs format, which requires suitable software to read/analyze data (i.e., FCS Express). |
| distribution |
[
{
"title": "Stochastic regression for flow cytometry data",
"format": "fcs file",
"mediaType": "application/x-zip-compressed",
"description": "E.coli stained with Hoechst 33342. All files called "test_double" are repeated experiment to test the reproducibility of the targeted measurement. RainbowBeads file tests the instrument response.",
"downloadURL": "https://data.nist.gov/od/ds/mds2-3119/Exp_20230921_1_Cyto-A-journal.zip"
}
]
|
| identifier | ark:/88434/mds2-3119 |
| issued | 2024-02-29 |
| keyword |
[
"E. coli",
"Flow cytometry",
"Hoechst 33342",
"genome copy",
"stochastic regression"
]
|
| landingPage | https://data.nist.gov/od/id/mds2-3119 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2022-06-01 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| theme |
[
"Bioscience",
"Metrology"
]
|
| title | Stochastic Regression and Peak Delineation with Flow Cytometry Data |
