Methods and systems for facilitating analysis of feature extraction outputs

• US 20070255512A1
• Filed: 04/28/2006
• Published: 11/01/2007
• Est. Priority Date: 04/28/2006
• Status: Abandoned Application

• Current Assignee: Agilent Technologies, Inc.
• Original Assignee: Agilent Technologies, Inc.
• Inventors: Gordon, David, Corson, John, Delenstarr, Glenda, Troup, Charles, Payne, Andrew
• Application Number:
  US11/413,643
• Publication Number:
  US 20070255512A1
• Time in Patent Office: Days
• US Class Current: 702/35
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: G16B 50/10 Ontologies; Annotations
• CPC Class Codes: G16B 50/30 Data warehousing; Computing architectures

Abstract

Methods, systems and computer readable media for facilitating analysis of feature extraction outputs across multiple extractions. A feature extraction output of an extraction resulting from feature extraction of an array is inputted, and global statistics and array processing parameters are extracted from the feature extraction output. A table/file is populated with the extracted global statistics and array processing parameters of the extraction. The inputting, extracting and populating steps are repeated for at least one additional feature extraction output of another extraction, so that the table/file includes global statistics that can be readily cross-compared over multiple extractions with reference to a single table or file. Methods, systems and computer readable media are provided for setting threshold values for metrics that global statistics are provided for. An evaluation metric may be set by a user, based upon the threshold values set for the metrics. A metric set including the metrics and optionally one or more thresholds and optionally an evaluation metric may be stored and/or applied to additional global statistics for those metrics to evaluate the quality of one or more extractions. A set of reports are provided for facilitating analysis of feature extraction outputs across multiple extractions. A diagnostic tool is provided for identifying and diagnosing potential problems in feature extraction outputs.

Claims

1. A method of facilitating analysis of feature extraction outputs across multiple extractions, said method comprising:

inputting a feature extraction output of an extraction resulting from feature extraction of an array;

extracting global statistics and array processing parameters from the feature extraction output;

populating a table or file with the extracted global statistics and array processing parameters of the extraction; and

repeating said inputting, extracting and populating for at least one additional feature extraction output of another extraction, so that said table or file includes global statistics that can be readily cross-compared over multiple extractions with reference to a single table or file.

2. The method of claim 1, further comprising displaying the table on a user interface for comparisons of global statistics by a user.

3. The method of claim 1, further comprising plotting a chart of metric values for a metric in said table or file for a plurality of extractions, and displaying the chart on a user interface.

4. The method of claim 3, further comprising displaying a threshold value on the chart.

5. The method of claim 4, further comprising highlighting metric values that exceed the threshold value on the chart or highlighting metric values that are within limits of the threshold value.

6. The method of claim 3, wherein said metric values in said chart are dynamically linked to said table or file, and wherein clicking on a metric value in the displayed chart causes said table to be displayed so that a user can review all metric values for all metrics for the extraction from which the metric value clicked on was plotted.

7. The method of claim 1, wherein said inputting is directed manually by a user through a user interface.

8. The method of claim 1, wherein said inputting is performed automatically by integration with a feature extraction system.

9. The method of claim 1, further comprising storing the file in a database, and wherein subsequent executions of the method of claim 1 add the subsequent global statistics and array processing parameters of the subsequent extractions to the stored file, so that global statistics and array processing parameters of all extractions are stored in the file.

10. The method of claim 1, further comprising appending a user annotation to the array processing parameters and global statistics of an extraction in said file or table.

11. The method of claim 10, further comprising plotting a chart of metric values from at least one of the user annotations in said table or file for a plurality of extractions, and displaying the chart on a user interface.

12. The method of claim 10, wherein user annotation columns in said table can be created for any user annotation by a user through a user interface.

13. The method of claim 9, further comprising querying the file in the database to select a subset of the extractions for further processing.

14. The method of claim 13, further comprising querying the file in the database to select a subset of the extractions for further processing, wherein a query may include any metric field for which a particular global statistic is provided, user annotation field, array processing parameter field, specific extraction identification number, specific user annotation, specific array processing parameter, specific global statistic value, or any non-contradictory combination of these.

15. The method of claim 13, further comprising:

selecting a metric for which global statistics are reported in the subset of the extractions;

plotting a chart of the metric values from the global statistic values in the subset of extractions reported for the metric;

calculating summary statistics characterizing the distribution of the plotted metric values; and

setting a threshold value for the distribution.

16. The method of claim 13, further comprising:

selecting a metric for which a user annotation is reported in the subset of the extractions;

plotting a chart of the metric values from the user annotation values in the subset of extractions reported for the metric;

calculating summary statistics characterizing the distribution of the plotted metric values; and

setting a threshold value for the distribution.

17. The method of claim 15, wherein the threshold value is set based on at least one of the calculated summary statistics.

18. The method of claim 13, further comprising grouping or ordering the extractions in the subset by sorting the subset or re-querying the file in the database.

19. The method of claim 3 performed a plurality of times for a plurality of different metrics, wherein charts resulting therefrom are displayed in a vertical stacked formation in a QC chart.

20. The method of claim 13, further comprising saving the query in the database, wherein the query can be called later for re-use.

25. The method of claim 21, further comprising repeating a least the selecting, plotting, calculating and setting steps of claim 19 for at least one other metric.

26. The method of claim 25, further comprising defining an evaluation metric by a user, based on the thresholds that were set.

27. The method of claim 26, further comprising storing the metrics, thresholds, evaluation metric and extraction query used to obtain the extraction sets from which the metrics were selected, in the database as a metric set.

28. The method of claim 27, wherein each query stored is date-stamped with a date on which the query was performed.

29. The method of claim 27, further comprising comparing thresholds in said metric set against the global statistics provided in another set of extractions for the same metrics, and evaluating the quality of the arrays from which the extractions in said another set were produced, based on said evaluation metric.

30. The method of claim 29, wherein said comparison is performed automatically.

31. The method of claim 30, wherein said automatic comparison is done on the fly upon receiving feature extractions outputs from an integrated feature extraction system.

32. The method of claim 30, wherein said automatic comparison is done on the fly by a feature extraction tool in a feature extraction system, said metric set having been imported into said feature extraction system.

21. A method of facilitating analysis of feature extraction outputs across multiple extractions, said method comprising:

querying a file containing global statistics and array processing parameters for each of a plurality of extractions to select a subset of records, each record containing global statistics and array processing parameters for a different extraction;

selecting a metric for which global statistics are reported in the subset of the extractions;

plotting a chart of the metric values from the global statistic values in the subset of extractions reported for the metric;

calculating summary statistics characterizing the distribution of the plotted metric values; and

setting a threshold value for the distribution.

22. The method of claim 21, wherein the threshold value is set based on at least one of the calculated summary statistics.

23. The method of claim 22, wherein the threshold value setting is selected by a user.

24. The method of claim 21, wherein the threshold value is inputted manually by a user after viewing the chart.

33. A set of reports for facilitating analysis of feature extraction outputs across multiple extractions, said set comprising:

a statistics table containing global statistics for metrics, array processing parameters and user annotations for multiple extractions, each row of said table containing data for a single extraction, said data including at least global statistics and an array processing parameter, including a unique identifier for the extraction, wherein said table contains data for at least two different extractions; and

a QC chart displaying at least one plot of a metric values from global statistics for the metric across a plurality of said extractions.

35. The set of claim 33, further comprising a summary plot that graphically illustrates evaluation results of metric values plotted in said QC chart for at least one metric, each over a plurality of extractions.

36. The set of claim 35, wherein said summary plot is dynamically linked with said QC chart.

34. The set of claim 34, wherein said QC chart and said statistics table are dynamically linked.

37. A retrospective system for facilitating analysis of feature extraction outputs across multiple extractions, said system comprising:

a processor; and

a retrospective tool programmed to receive an input of a feature extraction output of an extraction resulting from feature extraction of an array, extract global statistics and array processing parameters from the feature extraction output, and populate a table or file with the extracted global statistics and array processing parameters of the extraction.

38. The retrospective system of claim 37, further comprising a database, wherein the file containing global statistics and array processing parameters is stored in said database.

39. The retrospective system of claim 37, further comprising a user interface, said system configured to display said table on said user interface.

40. The retrospective system of claim 39, wherein said retrospective tool is further programmed to plot at least one metric for which global statistics are contained in said table or file against a plurality of said extractions.

41. The retrospective system of claim 38, further comprising a user interface, said user interface being configured for user querying of said database to select a subset of the extractions and associated data contained in said file.

42. The retrospective system of claim 37 integrated into a feature extraction system.

43. The retrospective system of claim 41, wherein said retrospective tool is further programmed to plot at least one metric, selected by a user through said user interface, for which global statistics are contained in said table or file against the subset of extractions returned by said query.

44. The retrospective system of claim 43, wherein said user interface is configured for interactive user setting of a threshold value for a distribution of metric values from said global statistics plotted for the selected metric.

45. The retrospective system of claim 44, wherein said user interface is further configured for interactive user setting of an evaluation metric based on a plurality of thresholds set for a plurality of metrics.

46. The retrospective system of claim 42, wherein a plurality said inputs of feature extraction outputs from a plurality of extractions having been batch processed by the feature extraction system are received and processed automatically by said retrospective tool.

47. The retrospective system of claim 46, wherein said retrospective tool further automatically plots at least one metric for which global statistics are contained in said table or file against the batch of extractions received from the feature extraction system.

48. The retrospective system of claim 37, wherein said retrospective tool is configured to automatically compare thresholds stored in a metric set against the global statistics provided in a set of extractions received, in regard to the same metrics, and evaluate the quality of the arrays from which the extractions received were produced.

49. The retrospective system of claim 48, wherein said system automatically evaluates a quality of an extraction as acceptable or a status of hold for further evaluation, based upon an evaluation metric.

50. The retrospective system of claim 49, further comprising a database, wherein the system stores the quality evaluation results of the extractions in associations with the global statistics and array processing parameters for those extractions.

51. The retrospective system of claim 50, wherein the system calculates a statistic for a metric using the quality evaluation results of the extractions being analyzed against the metric.

52. The retrospective system of claim 49, wherein said system automatically transmits data from extractions evaluated as acceptable, to another system for further processing, and wherein extractions evaluated as hold for further evaluation are prevented from being sent until being reviewed further and manually approved.

62. The retrospective system of claim 37, further comprising:

a diagnostic tool including a set of diagnostic rules, a rules software language executable to execute said rules against at least one of feature extraction global statistics and feature extraction data, to determine whether logic provided in a rule is met or violated by the global statistic or feature data value compared, and programming for outputting potential problems identified by executing said rules against the data value.

63. The retrospective system of claim 62, wherein said diagnostic tool is configured to discover new rules by performing at least one of clustering and creating a decision tree based on at least one of available global statistics, array processing features and user annotations.

53. A feature extraction system for facilitating analysis of feature extraction outputs across multiple extractions, said system comprising:

a processor; and

a feature extraction tool programmed to extract global statistics and array processing parameters from a batch of feature extraction outputs produced by the feature extraction system, and populate a table or file on the fly with the extracted global statistics and array processing parameters extracted from the batch of feature extraction outputs.

54. A diagnostic tool for identifying and diagnosing potential problems in feature extraction outputs, said tool comprising:

a processor;

a set of diagnostic rules;

a rules software language executable by said processor to execute said rules against at least one of feature extraction global statistics and feature extraction data, to determine whether logic provided in a rule is met or violated by the global statistic data value or feature data value compared; and

programming for outputting potential problems identified by executing said rules against the at least one data value.

55. The tool of claim 54, wherein each rule in said rule set comprises a rule identifier, rule logic, severity element, diagnosis text and troubleshooting text.

56. The tool of claim 54, wherein said rule set is extensible, said rule set being configured to add, remove or modify rules.

57. The tool of claim 54 configured to update said rule said by downloading information over a network.

58. The tool of claim 54, wherein said tool further outputs at least one of a diagnosis for correction of a potential problem, and instructions where further information can be obtained to solve the potential problem.

59. The tool of claim 54 integrated into a retrospective system.

60. The tool of claim 54 integrated into a feature extraction system.

61. The tool of claim 54, further comprising a database in which said set of diagnostic rules are stored.

64. A method of evaluating the quality of feature extraction outputs from a plurality of arrays expected to produce the same results, said method comprising:

inputting feature extraction outputs of the extractions resulting from feature extraction of the arrays;

extracting global statistics and array processing parameters from the feature extraction outputs;

populating a table or file with the extracted global statistics and array processing parameters of the extractions;

plotting at least one metric against the global statistics reported for that metric for the extractions; and

analyzing the at least one plot to identify potential outliers.

65. A method of correlating a change in an array processing parameter for an extraction with changes in feature extraction outputs, said method comprising:

inputting feature extraction outputs of extractions resulting from feature extraction of arrays having a first set of array processing parameters;

extracting global statistics and array processing parameters from the feature extraction outputs;

inputting feature extraction outputs of extractions resulting from feature extraction of arrays having a second set of array processing parameters, wherein the second set is the same as the first set except for a change in one or a small percentage of the array processing parameters;

extracting global statistics and array processing parameters from the feature extraction outputs from the arrays having the second set of array processing parameters;

populating a table or file with all extracted global statistics and array processing parameters of the extractions;

plotting at least one metric against the global statistics reported for that metric for the extractions; and

comparing the values in the at least one plot to establish whether there is a significant difference between values from the arrays having the first set of array processing parameters versus values from the arrays having the second set of array processing parameters.

66. A method of developing a microarray product, said method comprising:

inputting feature extraction output of an extraction resulting from feature extraction of an existing array;

extracting global statistics and array processing parameters from the feature extraction output;

inputting feature extraction output of an extraction resulting from feature extraction of an array similar to the existing array, but in which at least one factor was changed;

extracting global statistics and array processing parameters from the feature extraction output from the array similar to the existing array;

populating a table or file with all extracted global statistics and array processing parameters of the extractions;

plotting at least one metric against the global statistics reported for that metric for the extractions; and

comparing the values in the at least one plot to establish whether the change of at least one factor had a positive, negative, or no impact on the feature extraction output as measured by the at least one metric.

67. The method of claim 66, wherein said factor comprises at least one of a change in hybridization conditions used to prepare the array, a change in scanner used to scan the array, a change in an array processing parameter, a change of at least one extraction algorithm used to perform the feature extraction, a change in a printer used to print the array, a change in a reagent used during processing of the array, and a change in a version of feature extraction software used to perform the extraction.

68. A method of diagnosis of potential errors in feature extraction outputs, said method comprising:

inputting a feature extraction output of an extraction resulting from feature extraction of an array;

extracting global statistics and array processing parameters from the feature extraction output;

populating a table or file with the extracted global statistics and array processing parameters of the extraction; and

repeating said inputting, extracting and populating for at least one additional feature extraction output of another extraction, so that said table or file includes global statistics that can be readily cross-compared over multiple extractions with reference to a single table or file;

plotting a chart of global statistic values for a metric in said table or file for a plurality of extractions;

evaluating the values in the chart to identify potential outliers; and

correlating one or more array processing parameters that are different between two sets of the global statistic values, one set predominantly containing the potential outliers and the other set containing predominantly non-outlier values; and

identifying the one or more array processing parameters as possibly causative of the potential errors.

69. The method of claim 68, further comprising appending a user annotation to a record in said file containing an extraction and associated global statistics and array processing parameters, wherein said correlating may further comprise correlating one or more user annotations that are different between the two sets of the global statistic values, and identifying the one or more user annotations as possibly causative of the potential errors.

70. A method of diagnosis of potential errors in feature extraction outputs, said method comprising:

executing a set of diagnostic rules against a global statistic or feature data value to determine whether the value complies with logic contained with the set of rules; and

outputting a warning and diagnosis of a potential error for an extraction when a rule is found to have been violated by not complying with the logic contained in a rule.

71. The method of claim 70, wherein each diagnostic rule in said rule set comprises a rule identifier, rule logic, severity element, diagnosis text and troubleshooting text.

72. The method of claim 70, wherein said executing a set of diagnostic rules comprises executing a set of diagnostic rules against feature data values from a plurality of replicate features to determine correlation among the feature data values.

73. The method of claim 70, wherein said executing a set of diagnostic rules comprises executing a set of diagnostic rules against feature data values from a set of common feature across a plurality of arrays in an array set to determine correlation among the feature data values.

74. A computer readable medium carrying one or more sequences of instructions for facilitating analysis of feature extraction outputs across multiple extractions, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

inputting a feature extraction output of an extraction resulting from feature extraction of an array;

extracting global statistics and array processing parameters from the feature extraction output;

populating a table or file with the extracted global statistics and array processing parameters of the extraction; and

repeating said inputting, extracting and populating for at least one additional feature extraction output of another extraction, so that said table or file includes global statistics that can be readily cross-compared over multiple extractions with reference to a single table or file.

75. A computer readable medium carrying one or more sequences of instructions for facilitating analysis of feature extraction outputs across multiple extractions, wherein execution of one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

querying a file containing global statistics and array processing parameters for each of a plurality of extractions to select a subset of records, each record containing global statistics and array processing parameters for a different extraction;

selecting a metric for which global statistics are reported in the subset of the extractions;

plotting a chart of the metric values from global statistic values in the subset of extractions reported for the metric;

calculating statistics characterizing the distribution of the plotted metric values; and

setting a threshold value for the distribution.

76. The computer readable medium of claim 75, wherein the following further steps are performed:

repeating a least the selecting, plotting, calculating and setting steps for at least one other metric.

77. The computer readable medium of claim 75, wherein the following further step is performed:

defining an evaluation metric interactively by a user, based on the thresholds that were set.

78. The computer readable medium of claim 77, wherein the following further steps is performed:

storing the metrics, thresholds, evaluation metric and queries used to obtain the extraction sets from which the metrics were selected, in the database as a metric set.

79. The computer readable medium of claim 77, wherein the following further step is performed:

comparing thresholds in said metric set against the global statistics provided in another set of extractions for the same metrics, and evaluating the quality of the arrays from which the extractions in said another set were produced, based on said evaluation metric.