Methods and systems for high confidence utilization of datasets

Abstract

Methods and systems for high-confidence utilization of datasets are disclosed. In one embodiment, the method includes selecting a metric for determining substantially optimal combination of true positives and false positives in a data set, applying an optimization technique, and obtaining, from the results of the optimization technique, a value for at least one optimization parameter, the value for at least one optimization parameter resulting in substantially optimal combination of true positives and false positives. A number of true positives and a number of false positives are a function of the one or more optimization parameters.

What is claimed is: 
     
         1 . A computer implemented method for devising measurements of fluorescence, wherein a signal is measured at different point along the spectrum of a fluorescent signal being measured, the method comprising the steps of:
 selecting a metric for determining substantially optimal combination of true positives and false positives in at least one data set;   applying an optimization technique; and   obtaining, from the results of the optimization technique, a value for at least one optimization parameter, said value for at least one optimization parameter resulting in substantially optimal combination of true positives and false positives; wherein the obtaining at least one optimization parameter comprises obtaining a value of a number of independent measures; wherein obtaining a value of a number of independent measures comprises obtaining at least one combination of a value of a number of independent measures and a value for a confidence measure; said independent measures comprising measures of a parameter of fluorescence obtained using different measurement criteria;   wherein a number of true positives and false positives are a function of at least one combination of the number of independent measures and the confidence measure; and   wherein—the steps of selecting a metric, applying an optimization technique, and obtaining, from the results of the optimization technique, a value are performed by means of a non-transitory computer usable medium having computer readable code that causes a processor to perform the steps;   whereby such measurement are used in systems used in applications including nucleic acid sequencing, high spatial density measurement of fluorescent based signals using scanners and cameras including for nucleic acid and protein measurements.   
     
     
         2 . The method of  claim 1  wherein the step of applying an optimization technique comprises the step of optimizing a cost function; said cost function being a function of the number of independent measures of fluorescence. 
     
     
         3 . The method of  claim 1  further comprising the steps of:
 a) selecting a predetermined initial value of the threshold for the value of the number of independent measures; 
 b) selecting one element of the data set; the data set comprising a plurality of elements; 
 c) determining at least one predetermined quantity for the selected one element; 
 d) determining whether said at least one predetermined quantity substantially satisfies a threshold criterion; 
 e) incrementing, if said at least one predetermined quantity satisfies the threshold criterion, a number of elements; 
 f) determining, after incrementing the number of elements, if the number of elements is more than the threshold for the value of the number of independent measures; 
 g) repeating steps b) through f) for each element from the plurality of elements; 
 h) determining, using step c), whether the threshold for the value of the number of independent measures results in a substantially optimal combination of true positives and false positives. 
 
     
     
         4 . The method of  claim 3  wherein the data set includes at least two parameters for at least one element; and the method further comprises the step of repeating steps d) and e) for each parameter before completing step f). 
     
     
         5 . The method of  claim 4  wherein the data set includes replicates; and the method further comprises the step of:
 i) selecting, before step b), a predetermined initial value of the confidence threshold measure; 
 j) calculating, after step d), if said at least one predetermined quantity satisfies the threshold criterion, a confidence measure for said one element; 
 k) determining whether the calculated confidence measure is greater than the confidence threshold measure; 
 l) proceeding to step e), for each element from the plurality of elements; 
 m) incrementing, after step h), the confidence threshold measure within a range of predetermined confidence thresholds; 
 and 
 wherein step d) further comprises repeating steps j) through l); and 
 wherein step h) further comprises selecting the confidence threshold measure that results in a substantially optimal combination of true positives and false positives. 
 
     
     
         6 . The method of  claim 3  wherein the data set includes at least two parameters for at least some elements; and the method further comprises the step of repeating steps d) and e) for each parameter before completing step f). 
     
     
         7 . The method of  claim 4  wherein thresholds for predetermined quantities are determined by the steps of:
 evaluating the predetermined quantities over at least a portion of the data set; 
 sorting the evaluated predetermined quantities in ascending order of value; and 
 selecting a predetermined percentile of the predetermined quantity as the threshold for the predetermined quantity. 
 
     
     
         8 . The method of  claim 7  wherein the predetermined quantity is a numerical difference between two elements of the data set. 
     
     
         9 . The method of  claim 7  wherein the predetermined quantity is the ratio between two elements of the data set. 
     
     
         10 . The method of  claim 7  wherein the step of evaluating the predetermined quantities over at least a portion of the data set comprises the steps of:
 selecting portion of the data set; and 
 evaluating the predetermined quantities over the selected portion of the data set; and 
 wherein the selected threshold for the predetermined quantity is utilized for the portion of the data set being evaluated. 
 
     
     
         11 . The method of  claim 10  wherein the predetermined quantities are obtained by interpolation and extrapolation based on consecutive portions of the data set. 
     
     
         12 . A system for devising measurements of fluorescence, wherein the signal is measured at different point along the spectrum of the fluorescent signal being measured, the system comprising:
 at least one processor; and   computer usable media having computer readable code embodied therein, the computer readable code causing said at least one processor to:
 select a metric for determining substantially optimal combination of true positives and false positives in at least one data set; 
 apply an optimization technique; and 
 obtain, from the results of the optimization technique, a value for at least one optimization parameter, said value for at least one optimization parameter resulting in substantially optimal combination of true positives and false positives; wherein the obtaining at least one optimization parameter comprises obtaining a value of a number of independent measures; wherein obtaining a value of a number of independent measurements comprises obtaining at least one combination of a value of a number of independent measures and a value for a confidence measure; 
 said independent measures comprising measures of a parameter of fluorescence obtained using different measurement criteria; 
   wherein a number of true positives and false positives are a function of at least one combination of the number of independent measures and the confidence measure;   whereby such measurement are used in systems used in applications including nucleic acid sequencing, high spatial density measurement of fluorescent based signals using scanners and cameras including for nucleic acid and protein measurements.   
     
     
         13 . The system of  claim 12  wherein the computer readable code in causing said at least one processor to apply an optimization technique further causes said at least one processor to optimize a cost function; said cost function being a function of the number of independent measures of the fluorescence. 
     
     
         14 . The system of  claim 12  wherein the computer readable code also causes said at least one processor to:
 a) select a predetermined initial value of the threshold for the value of the number of independent measures; 
 b) select one element of the data set; the data set comprising a plurality of elements; 
 c) determine at least one predetermined quantity for the selected one element; 
 d) determining whether said at least one predetermined quantity substantially satisfies a threshold criterion; 
 e) increment if said at least one predetermined quantity satisfies the threshold criterion, a number of elements; 
 f) determine, after incrementing the number of elements, if the number of elements is more than the threshold for the value of the number of independent measures; 
 g) repeat steps b) through f) for each element from the plurality of elements; 
 h) determine using step c), whether the threshold for the value of the number of independent measures results in a substantially optimal combination of true positives and false positives. 
 
     
     
         15 . The system of  claim 14  wherein the data set includes at least two parameters for at least some elements; and wherein the computer readable code also causes said at least one processor to: repeat steps d) and e) for each parameter before completing step f). 
     
     
         16 . A computer program product comprising a non-transitory computer usable medium having computer readable code embodied therein; said computer readable code causing a computer system to:
 select a metric for determining substantially optimal combination of true positives and false positives in at least one data set;   apply an optimization technique; and   obtain, from the results of the optimization technique, a value for at least one optimization parameter, said value for at least one optimization parameter resulting in substantially optimal combination of true positives and false positives; wherein the obtaining at least one optimization parameter comprises obtaining a value of a number of independent measures; wherein obtaining a value of a number of independent measurements comprises obtaining at least one combination of a value of a number of independent measures and a value for a confidence measure; said independent measures comprising measures of fluorescence obtained using different measurement criteria;   wherein a number of true positives and false positives are a function of at least one combination of the number of independent measures and the confidence measure.   
     
     
         17 . The computer program product of  claim 14  wherein the computer readable code in causing the computer system to apply an optimization technique further causes said at least one processor to optimize a cost function; said cost function being a function of the number of independent measures of fluorescence.