BME100 s2016:Group 9 W1030AM L6

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Bayesian Statistics

Overview of the Original Diagnosis System

To test patients for the presence of the disease associated SNP, PCR analysis was divided up amongst the BME 100 groups. Seventeen teams of 6 people were given two patients DNA samples each, resulting in thirty-four patients total receiving a diagnosis. To prevent error, 3 concentrations were created of each groups DNA samples along with a positive and negative control sample to be put through the PCR process. After PCR was done, the groups again attempted to avoid error by taking three individual photos of each PCR sample drop when placed in the fluorimeter along with the biomarker sybr green. These images of the drops were then analyzed in image j, along with images of another DNA sample set with known concentrations, which would allow the group to make a calibration curve to help align the DNA of PCR analysis.
The image j analysis results were taken from each group, and pooled together in one master spreadsheet containing all the diagnoses of the patients along with a doctor's diagnoses for comparison. While most results were determined positive or negative, some groups did have to lab one or two tests of their PCR photos as "inconclusive". This could have arisen from issues within the software or data recording in transferring their results from image j to excel, or any other number of small issues. Group 9 ran into issues with its data analysis when analyzing the data in image j, where the group found that some of its photos were not of the best quality, and could not produce the best results through image j's analysis method. Furthermore, the group also lacked to be given one of the DNA sample concentrations that was used to create the calibration curve for the image j PCR, data, which could account for some erroneous readings.

What Bayes Statistics Imply about This Diagnostic Approach

Calculation 1&2

The Bayesian statistic calculations for calculations one and two provided results that were in the 80-90% range. This shows that the PCR tests were 80-90% reliable in creating a correct diagnoses for the disease SNP.

Calculation 3&4

The Bayesian statistic calculations for calculations 2 and 3 produced results that went from 50% for the positive results to 80%-100% for the negative results. This shows that when the PCR diagnoses performed but the BME 100 groups were measured in conjunction with the doctor's diagnosis, the positive results were only 50% reliable where as the results for the negative results were much more reliable.

Sources of Error

Three possible sources of error in the PCR detection process could have resulted from two categories of error in this experiment. For machine error, the first possible source of error in detection could have resulted from the reliance on the use of cell phone cameras to collect images for data analysis; the difference and reliability of each person's individual cell phone camera is so varied that results produced could be erroneous based solely on the difference in the images taken for analysis. The next machine error could have originated from the image j analysis software itself, as it can sometimes produce strange or erroneous results, and each time a measure is taken, no matter if its the same are in the same location of the same photo, it can provide inconsistent data measurements, showing that the software is not always 100% accurate. The final possible source of error, could come possible from human error, in which the group member perhaps did not interpret and copy data correctly between the image analysis software and the data analysis software (image j to excel). This would result in misreported results and as such the calculations would not be accurate.

Intro to Computer-Aided Design


Our Design

Feature 1: Consumables

Feature 2: Hardware - PCR Machine & Fluorimeter