How to use SanXoT - Revision history

Mtrevisan: /* Building the workflows */

2018-08-13T09:14:00Z

‎Building the workflows

Mtrevisan: /* Interpreting the results of GIA integrations */

2018-08-13T09:12:16Z

‎Interpreting the results of GIA integrations

Mtrevisan: /* Performing integrations and removing outliers */

2018-08-13T09:03:54Z

‎Performing integrations and removing outliers

Mtrevisan: /* Performing integrations and removing outliers */

2018-08-13T08:53:22Z

‎Performing integrations and removing outliers

Mtrevisan: /* Performing integrations and removing outliers */

2018-08-13T08:52:21Z

‎Performing integrations and removing outliers

Mtrevisan: /* Calibrating the data */

2018-08-13T08:51:09Z

‎Calibrating the data

Mtrevisan: /* Calibrating the data */

2018-08-13T08:50:25Z

‎Calibrating the data

Mtrevisan: /* Calibrating the data */

2018-08-13T08:49:38Z

‎Calibrating the data

Mtrevisan: /* Calibrating the data */

2018-08-13T08:49:09Z

‎Calibrating the data

Mtrevisan: /* Preparing the data */

2018-08-13T08:47:53Z

‎Preparing the data

@@ Line 76: / Line 76: @@
 # A list of concatenated (or branched) GIA integrations. As explained above, each GIA is performed by running sequentially '''[[SanXoT]]''' (to calculate the general variance), '''[[SanXoTSieve]]''' (to tag outliers) and SanXoT (to integrate without outliers)
-Note that some specific GIA integrations need the use of other modules. These include experiment merging and Systems biology analysis. See advanced applications and exploring SanXoT features.
+Note that some specific GIA integrations need the use of other modules. These include experiment merging and Systems biology analysis. See '''[[How to use SanXoT#advanced applications|advanced applications]]''' and '''[[exploring SanXoT features]]'''.
 == Advanced applications ==

@@ Line 65: / Line 65: @@
 == Interpreting the results of GIA integrations ==
-GIA integrations basically generate the quantitative data and their associated variances at the upper level, and detect and eliminate outliers that can affect the values at the upper level. However, each integration also generates various information that may be very useful to interpret the results. The most important is the general variance of the integration.
+GIA integrations basically generate the quantitative data and their associated variances at the upper level. However, each integration also generates various information that may be very useful to interpret the results. The most important is the general variance of the integration.
-The general variance is an estimate of the error introduced when the elements of the lower level are experimentally generated from those of the upper level. This parameter may have different meaning depending on the nature of the data.
+The general variance is an estimate of the error introduced when the elements of the lower level are experimentally generated from those of the upper level. This parameter may have different meaning depending on the nature of the data. For instance, when spectra are integrated into peptides, the general variance estimates the error associated to the measurement (independently of peptide and protein manipulation), and hence depends on the LC-MS setup. When peptides are integrated into proteins, the variance estimates the error produced at the time of peptide generation from their proteins (protein digestion, peptide desalting,...), independently of LC-MS analysis. When proteins are integrated into the grand mean, the variance mainly estimates the error produced during protein manipulation (extraction,…). Similarly, integrating technical and biological replicates allow to estimate separately the different error sources at the protein level.
 == Building the workflows ==

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 In addition to the quantitative data, each GIA integration may generate several additional files which contain information about the integration. More detailed information may be found in '''[[exploring SanXoT features]]'''.
-One of the advantages of the WSPP model is that it provides a straightforward method to eliminate outliers<ref name=navarro2014/> in each integration step. This is done by calculating standardized log2-ratios (z-values), which allows to estimate the probability that each element of the lower level is a significant outlier of the z distribution (e.g., N(0,1)) and calculating the associated FDR. The most extreme outliers may be removed sequentially and the integration repeated, until all outliers below a user-defined FDR-level are removed.
+One of the advantages of the WSPP model is that it provides a straightforward method to eliminate outliers<ref name=navarro2014/> in each integration step. This is done by calculating standardized log2-ratios (z-values), which allow to estimate the probability that each element of the lower level is a significant outlier of the z distribution (e.g., N(0,1)) and also to obtain the associated FDR. The most extreme outliers may be removed sequentially and the integration repeated, until all outliers below a user-defined FDR-level are removed.
 Outlier removal is performed by the '''[[SanXoTSieve]]''' module by tagging the outlier elements in the relations table.

@@ Line 53: / Line 53: @@
 In addition to the quantitative data, each GIA integration may generate several additional files which contain information about the integration. More detailed information may be found in '''[[exploring SanXoT features]]'''.
-One of the advantages of the WSPP model is that it provides a straightforward method to eliminate outliers<ref name=navarro2014/> in each integration step. This is done by calculating standardized log2-ratios (z-values), calculating the probability that each element of the lower level is a significant outlier of the z distribution (e.g., N(0,1)) and calculating the associated FDR. The most extreme outliers may be removed sequentially and the integration repeated, until all outliers below a user-defined FDR-level are removed.
+One of the advantages of the WSPP model is that it provides a straightforward method to eliminate outliers<ref name=navarro2014/> in each integration step. This is done by calculating standardized log2-ratios (z-values), which allows to estimate the probability that each element of the lower level is a significant outlier of the z distribution (e.g., N(0,1)) and calculating the associated FDR. The most extreme outliers may be removed sequentially and the integration repeated, until all outliers below a user-defined FDR-level are removed.
 Outlier removal is performed by the '''[[SanXoTSieve]]''' module by tagging the outlier elements in the relations table.

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 Each GIA integration takes the input data file from the lower level and the relations table, calculates the general variance of the integration  (using a robust iterative method) and generates as output a data file containing the integrated quantifications of the upper level. The integration is done using the '''[[SanXoT]]''' module.
-In addition to the quantitative data, each GIA integration may generate several additional files which contain information about the integration. More detailed information may be found in exploring SanXoT features.
+In addition to the quantitative data, each GIA integration may generate several additional files which contain information about the integration. More detailed information may be found in '''[[exploring SanXoT features]]'''.
 One of the advantages of the WSPP model is that it provides a straightforward method to eliminate outliers<ref name=navarro2014/> in each integration step. This is done by calculating standardized log2-ratios (z-values), calculating the probability that each element of the lower level is a significant outlier of the z distribution (e.g., N(0,1)) and calculating the associated FDR. The most extreme outliers may be removed sequentially and the integration repeated, until all outliers below a user-defined FDR-level are removed.

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 Calibration is the process where the quality parameter (the parameter which is used to rank quantitative values according to their variance) is transformed into the prior weight, a parameter with full statistical meaning defined as the inverse of the local variance of the quantitative value in log2-ratio scale. This process is done using the '''[[Klibrate]]''' module and needs to be performed only in the first step of each workflow. For more information about calibration, see '''[[exploring SanXoT features]]'''.
-Using prior weights is essential in GIA integrations, since this parameter not only reflects the local variances of the measurement, but are also used as statistical weights to calculate the weighted averages. Besides, GIA integrations follow error propagation theory, so that the output datafile of each integration contain true prior weights and do not need to be recalibrated.
+Using prior weights is essential in GIA integrations, since this parameter not only reflects the local variances of the measurement, but is also used as statistical weight to calculate the weighted averages. Besides, GIA integrations follow error propagation theory, so that the output datafile of each integration contains true prior weights and does not need to be recalibrated.
 == Performing integrations and removing outliers ==

@@ Line 44: / Line 44: @@
 == Calibrating the data ==
-Calibration is the process where the quality parameter (the parameter which is used to rank quantitative values according to their variance) is transformed into the prior weight, a parameter with full statistical meaning defined as the inverse of the local variance of the quantitative value in log2-ratio scale. This process is done using the '''[[Klibrate]]''' module and needs to be performed only in the first step of each workflow. For more information about calibration, see exploring SanXoT features.
+Calibration is the process where the quality parameter (the parameter which is used to rank quantitative values according to their variance) is transformed into the prior weight, a parameter with full statistical meaning defined as the inverse of the local variance of the quantitative value in log2-ratio scale. This process is done using the '''[[Klibrate]]''' module and needs to be performed only in the first step of each workflow. For more information about calibration, see '''[[exploring SanXoT features]]'''.
 Using prior weights is essential in GIA integrations, since these parameters not only reflect the local variances of the measurement, but are also used as statistical weights to calculate the weighted averages. Besides, GIA integrations follow error propagation theory, so that the output datafile of each integration contain true prior weights and do not need to be recalibrated.

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 The data obtained by the software used to obtain the quantitative values must be converted to generate:
-# '''an uncalibrated data file''' containing three columns, identifier, quantitative values (2-base logarithm of the ratios of the two quantitative measurements) and quality parameter. The quality parameter is a number that is used to rank the quantitative values according to their quality (in terms of variance, higher quality meaning a lower variance). In the case of isobaric labeling the third column contains the intensity of the most intense of the two reporter ions used to calculate the ratio; other kind of quantifications may need to use other parameters to account for the quality of quantitative values<ref name=navarro2014/>.
+# '''an uncalibrated data file''' containing three columns, identifier, quantitative values (2-base logarithm of the ratios of the two quantitative measurements) and quality parameter. The quality parameter is a number that is used to rank the quantitative values according to their quality (in terms of variance, higher quality meaning a lower variance). In the case of isobaric labeling the third column contains the maximum intensity of the two reporter ions used to calculate the ratio; other kind of quantifications may need to use other parameters to account for the quality of quantitative values<ref name=navarro2014/>.
 # '''the relations tables''' of all the GIA integrations planned in the workflow. In the fundamental workflow these tables describe which scans will be integrated into each peptide and which peptides will be integrated into each protein.
 These tables are easy to construct, due to their simple plain-text format. If the software used for quantification generates tab-separated text tables, one of the SanXoT modules ('''[[Aljamia]]'''), may be used for this task. In the unit tests for SanXoT we include a workflow that uses Aljamia to generate all the files required from the output of Proteome Discoverer 2.1. Quantitative values produced by the software package in other formats may require other simple scripts to generate the files required by SanXoT.