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Status |
Public on Aug 10, 2016 |
Title |
Sample_76 CD_1_Terminal Ileum |
Sample type |
RNA |
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Source name |
Terminal Ileum
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Organism |
Homo sapiens |
Characteristics |
patient id: CD-954 disease: CD inflamed (0=no, 1=yes): 1 nanostring codeset: IBD2
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Treatment protocol |
At the time of endoscopy, mucosal biopsies were collected in RNA-later (Qiagen) and stored at -80°C ahead of downstream processing. If samples were collected at the time of surgery, mucosal tissue sample obtained from bowel resection specimens with colonoscopic biopsy forceps were processed immediately or stored at 4°C for processing within 12 hours. Intestinal source location and inflammation status was indicated as “uninflamed” or “inflamed” based on the endoscopic or gross appearance of the tissue by the endoscopist or investigator at time of surgical specimen collection.
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Extracted molecule |
polyA RNA |
Extraction protocol |
Using a combined Trizol (Life Technologies)-chloroform extraction and the RNeasy Midi Kit (Qiagen) for RNA extraction, total RNA was extracted by manufacturer’s protocol and RNA purity and quantity was measured by NanoDrop spectrophotometer (Promega).
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Label |
N/A
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Label protocol |
Extracted RNA (100ng) was used as input for expression profiling on the NanoString platform according to manufacturer's specifications.
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Hybridization protocol |
according to the manufacturer’s protocol
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Scan protocol |
according to the manufacturer’s protocol
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Description |
CD_1_Terminal Ileum
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Data processing |
Data from each version of probeset (lot) was first independently normalized and samples were analyzed for quality controls using following steps. Background noise was estimated using mean expression levels of spiked-in negative controls Estimated noise value was subtracted from raw counts data for each sample. Negative count values were reset to 1 Count values of spike-in positive controls were summed per sample and the average value was used to estimate a scaling factor for each sample. Expression value of each sample was adjusted using the sample specific normalization factor Samples with higher than acceptable background noise and positive control based scaling factor <0.3 or >3.0 were identified as outliers and removed from analysis Next, geometric mean of housekeeping genes was computed for each sample and the average value was used to compute the normalization factor. Data was adjusted in a similar fashion as was done previously with spiked-in positive controls Finally, samples with housekeeping-genes based normalization factor <0.2 or >5.0 were removed as outliers. Data Lots 2 & 3 were independently calibrated to Lot 1 using following steps. The average of medians of calibration samples, for a given pair of lots, was computed and used to estimate normalization factor for each sample. Each lot was scaled with the normalization factor computed in the previous step. Geometric means of counts per gene between repeated samples was computed within a lot and a calibration factor was estimated per gene. Calibration per gene was applied for the non-reference lot. All computations were performed in MATLAB. Nine hundred and eighty-nine samples passed data normalization and quality control.
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Submission date |
Sep 16, 2015 |
Last update date |
Aug 11, 2016 |
Contact name |
Ramnik J Xavier |
E-mail(s) |
xavier@molbio.mgh.harvard.edu
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Organization name |
Massachusetts General Hospital
|
Street address |
185 Cambridge Street
|
City |
Boston |
State/province |
MA |
ZIP/Postal code |
02114 |
Country |
USA |
|
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Platform ID |
GPL20926 |
Series (1) |
GSE73094 |
Genes in Inflammatory Bowel Disease-Associated Risk Loci Demonstrate Genotype-, Tissue-, and Inflammation-Specific Patterns of Expression in Terminal Ileum and Colon Mucosal Tissue |
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