determining whether an instrument is operating 
 within acceptable 
 performance metrics is an essential step during scientific investigation .
a variety of methods are commonly used in liquid chromatography 
 mass spectrometry ( lc  ms ) , with 
 many groups routinely utilizing a common sample which is regularly 
 analyzed , for example , weekly .
samples range from simple proteins 
 and mixtures to whole cell - lysates . in 
 addition to regularly running a quality control ( qc ) sample , recent 
 research has investigated analysis methods and metrics for assessing 
 data quality .
rudnick et al . defined 46  nist metrics  
 to quantify the performance of various lc  ms aspects , such 
 as chromatography , ion source , dynamic sampling , and peptide identifications .
these metrics are largely dependent on ms / ms data and their subsequent 
 peptide identifications .
the quameter software package proposed an 
 additional set of metrics , which are not dependent on ms / ms identifications .
the nist and quameter efforts focused on 
 generating a comprehensive 
 set of metrics for monitoring the various aspects of system performance . 
 when employing the metrics to monitor the day - to - day operation of 
 an lc  ms system , it is important to note that some metrics 
 are heavily dependent on the specific instrument and configuration . 

what is an acceptable value for one setup may be wholly unacceptable 
 for another .
thus , a critical shortcoming of current metric sets is 
 that they produce an array of values and leave interpretation to the 
 user .
specifically , there is no guidance as to when the performance 
 of the lc 
given the 
 large number of metrics available , routine interpretation of the metrics 
 and quality assurance is typically determined via a subjective weighting 
 of a subset of the metrics .
the specific subset of metrics , and associated 
 acceptable values , varies from lab to lab and among individual operators . as pointed out by tabb
decision 
 support tools for the interpretation of metrics derived from data  . a variety of statistical methods may be used 
 to explore and model how the multivariate metrics predict data quality , 
 including principal component analysis , classification and regression and model 
 selection techniques . leveraging a large corpus of 
 manually curated qc data
, we developed a lasso logistic regression 
 classifier ( llrc ) that predicts , with high sensitivity and specificity , 
 whether a qc data set is in or out of control .
this signature , which 
 is a composite of lc  ms performance metrics , is more robust 
 than any single metric itself .
as performance can be viewed as a continuum , 
 the llrc model natively computes a quality score within the range 
 01 .
the trained model then identifies a cutoff for the dichotomous 
 classification that achieves the highest sensitivity and specificity . 

an important feature of the llrc is that it differentiates between 
 false positive and false negative errors .
therefore , the 
 penalties associated with these errors are separately defined , which 
 allows the classifier to be more responsive in the balance between 
 sensitivity and specificity .
finally , 
 we present the software used to create the classifier , so that other 
 laboratories can create a classifier specific to their qc regimen , 
 instrumentation , and needs .
a shewanella oneidensis mr-1 lysate digest is used 
 as a quality control sample in our laboratory 
 to provide sufficient proteomic complexity to assess both lc and ms 
 performance .
the cultures ( 7 l ) were grown in fed - batch mode using 
 a bioflow 3000 model fermentor ( new brunswick , inc . ) and allowed to 
 achieve steady state before sampling and harvesting .
the medium was 
 hba mr-1 with 0.5 ml / l of 100 mm ferric nta , 1 ml / l of 1 mm na2seo4 , and 1 ml / l of 3 m mgcl26h2o as well as vitamins , minerals , and amino acids . the cultures 
 used o2 as the terminal electron acceptor from a house 
 air source ( 5 l / min flow rate ) and were maintained at 20% dissolved 
 oxygen ( do ) .
other monitored parameters included maintaining a ph 
 of 7.00 ( 0.03 ) , a constant agitation of the culture at 5000 
 rpm , and temperature of 30 c .
these samples were pelleted ( 11900 g for 8 min at 4 c ) and frozen at 80 c 
 until processing for proteomic analysis .
enough material was prepared 
 in this manner to provide qc samples for the last four years . the 
 majority of the culture is still waiting to be used .
cells were 
 then lysed by homogenizing the cells with 0.1 mm zirconia / silica beads 
 in the bullet blender ( next advance , averill park ny ) speed 8 for 
 3 min .
samples were then immediately placed on ice to inhibit proteolysis 
 and then transferred , and the beads were rinsed with 100 l 
 of 100 mm na4hco3 and 1 ml of 100 mm ammonium 
 bicarbonate . a bca protein assay ( thermo scientific , san jose ca ) 
 was performed to determine concentration .
samples were denatured by 
 adding urea to 7 m and reduced by dithiothreitol ( dtt ) to a concentration 
 of 5 mm .
the samples were then incubated for 30 min at 60 c , 
 and then diluted 10-fold with 100 mm na4hco3 .
next , trypsin 
 ( affymetrix , santa clara ca ) was added in a ratio 1:50 trypsin / protein 
 and incubated at 37 c for 3 h. samples were desalted by 100 
 mg dsc-18 columns ( sigma aldrich ) .
each column was conditioned with 
 3 ml of meoh and rinsed with 2 ml of 0.1% tfa in h2o .
peptides 
 were then loaded on the resin and washed with 4 ml of 95:5 h2o / acn with 0.1% tfa .
collected sample was concentrated via speedvac 
 ( thermo scientific , san jose ca ) , and then the samples were transferred 
 to ultracentrifuge tubes and ultracentrifuged at 100  000 rpm 
 for 10 min at 4 c , and the supernatant was drawn off and pooled . 

final concentration was determined by peptide bca ( thermo scientific , 
 san jose ca ) .
samples were brought to a concentration of 0.5 g/l 
 with h2o ( millipore , billerica ma ) and divided into aliquots 
 for injections on the hplc .
the hplcs used to run the samples 
 were built in - house utilizing 
 various commercial pumps , valves , and auto samplers , all of which 
 were coordinated by a custom software packaged called lcmsnet .
the 
 data sets analyzed for this paper were run using lc columns that were 
 75 m inner diameter , and either 30 or 65 cm in length .
these 
 lc columns were packed in house with phenomenex jupiter c18 3 m 
 porous beads .
both 60 and 100 min acquisitions 
 were used . mobile phase a is 0.1% formic acid in h2o and 
 mobile phase b is 0.1% formic acid in acetonitrile .
the 100 min gradient 
 was delivered by starting at 5% mobile phase b and advancing to 8% , 
 12% , 35% , 60% , and 75% at times ( in minutes ) 2 , 20 , 75 , 97 , 100 respectively . 

typically 2.5 g of shewanella digest was loaded 
 to the head of the column or to a trapping column . although operating 
 conditions varied by capabilities of each instrument , typical conditions 
 for each are as follows .
the ltq was run in data - dependent ms - ms mode , 
 selecting the top 10 parent ions from each survey scan .
the exactive 
 runs were high resolution ms only with the target resolution set to 
 100  000 .
the ltq - orbitrap and the velos - orbitrap instruments 
 were typically set to have a high resolution survey scan of 60  000 
 resolution followed by the top 6 or 10 data - dependent ms 
ms 
 scans , respectively . because of the diversity of data sets used in 
 this study , this is not a comprehensive list of conditions .
data are 
 presented from four classes of instruments from thermo scientific : 
 the ltq linear ion trap , exactive , ltq - orbitrap , and velos orbitrap 
 platforms .
there are 1150 testing and training data sets : 224 on ltq 
 instruments , 85 on exactive instruments , 380 on ltq - orbitrap instruments , 
 and 461 on velos - orbitrap instruments ( see table 1 ) . for each qc data set
, the 
 nist and quameter metrics were calculated 
 exactly as described in the original publications .
the 
 quameter metrics were calculated using the software from the tabb 
 group ( http://fenchurch.mc.vanderbilt.edu/software.php ) ; 
 the nist metrics were calculated using in - house software , smaqc , which 
 is posted on our github repository ( https://github.com/pnnl-comp-mass-spec ) . for data from the exactive instrument class ,
no ms - ms data were 
 collected , and therefore any metrics relating to ms / ms data were omitted . 
 peptide identifications were performed with msgf+ ( version v9593 , 
 05/06/2013 ) .
the protein sequence database was the shewanella 
 oneidensis mr1 proteome supplemented with trypsin and keratin 
 sequences .
relevant search parameters were tryptic specificity ( semi 
 tryptic allowed ) , no static modifications , dynamic methionine oxidation . 

high resolution instruments used a 20 ppm tolerance , low resolution 
 data used 2.5 da .
all of the data has been deposited to the proteomexchange 
 consortium ( http://proteomecentral.proteomexchange.org ) 
 via the pride partner repository with the data set identifiers pxd000320 , 
 pxd000321 , pxd000322 , pxd000323 , and pxd000324 .
this includes the 
 instrument .raw files , the ms - gf+ peptide identifications in .mzidentml 
 format , .mgf converted spectra files , 
 and the spreadsheet containing the metrics for every data set .
the data sets were manually reviewed 
 by three expert instrument operators ( 30 + years of combined lc 
this 
 viewer contained the base peak chromatogram , total ion current chromatogram , 
 plots of both the top 50  000 and top 500  000 lc 
ms 
 detected features , and the number of peptides identified . in the first 
 round , 1150 data sets were manually curated as  good  , 
  okay  , or  poor  and used to develop 
 the classifier . in cases where the assessors disagreed ( 510% ) , 

moreover , the 
  okay  value was used to denote the wide range of performance , 
 which , although not optimal , was still acceptable . for the validation , 
 an additional 1321 data sets classified with the statistical model 
 from which a subset of 100 data sets were chosen for manual curation 
 ( supplemental files 1 and 2 , supporting information ) .
data quality assessment requires knowledge of the conditions 
 under which the qc was run to properly account for the variety of 
 run parameters , for example , high resolution ms only , high resolution 
 ms low resolution ms / ms , high resolution ms high resolution ms / ms , 
 and low resolution ms low resolution ms / ms .
data - dependent acquisition 
 regimes varied from ms / ms of top 3 , top 6 , and top 10 most intense 
 peaks from the survey ms scan .
fragmentation methods included high 
 energy collisional dissociation ( hcd ) and collision induced dissociation 
 ( cid ) .
resolution ranged from 1000 to 100  000 based on instrument 
 capabilities and settings to meet the needs of ongoing experiments . 

run times were either 60 or 100 min , and lc column lengths varied 
 accordingly .
each of these criteria is considered to determine what 
 constitutes an acceptable ( in control ) data set .
for example , under 
 identical mass spectrometer conditions , an acceptable 60 min hplc 
 run would provide results that would be unacceptable ( out of control ) 
 for a 100 min run .
multivariate 
 statistical 
 techniques were applied to the data to 
 identify which metrics might be useful to understand the quality of 
 a given data set and to develop a model to predict the quality of 
 future data sets . principal component analysis ( pca )
was applied on the nist and quameter metrics , 87 in all , 
 using the pca package in the r statistical programming language .
we applied a few of them , including classification and regression 
 tree algorithms ( cart ) , linear discriminant analysis ( lda ) , and logistic 
 regression augmented by a classification threshold .
the most effective 
 was logistic regression , an approach for 
 predicting a binary response using a linear combination of continuous 
 and/or categorical predictor variables .
the goal for the classifier 
 is to identify data sets that are out of control . to that end ,
the 
 binary response was coded as a  0  for a data set that 
 was in control ( annotated as  good  or
 1  for a data set that was out of control ( annotated 
 as  poor  )
. the linear component of the logistic regression 
 model can be expressed aswhere i , ( i = 1 , ... , n )
indexes the data sets , 0 is the intercept , 1 is a vector of coefficients ( one for each of 
 the nist and quameter quality metrics included in the model ) , and xi is a vector representing the quality metrics included in the model . 

the probability of the binomial response is modeled by the logistic 
 function : where  ( xi ) estimates the 
 probability of data 
 set i being out of control .
an important step 
 in any regression model is determining the set of variables that best 
 predict the response .
we used the lasso approach , a model selection technique 
 that accounts for collinearity among the predictors while selecting 
 a subset of the predictor variables that results in the  best  
 regression model . in this case , 
best  is defined as 
 the subset of variables whose coefficients maximize a penalized log - likelihood 
 function ( see appendix ) .
the inputs 
 to the lasso logistic regression model include the complete 
 set of possible quality metrics , the binary expert annotations ( 0 
 =  good  or 
, 1 =  poor  ) , 
 and  , a regularization parameter that simultaneously restricts 
 the size of model coefficients and the number of quality metrics included 
 in the model .
we developed a separate lasso logistic regression model 
 for each of the four instruments types .
we used a threshold parameter , 
  , to make a definitive prediction as to whether a data set 
 was good or poor .
specifically , we classify data set i as out of control if (xi ) >  and in control 
 otherwise . 
 we will refer to the combination of the logistic regression and the 
 threshold ,  , as the lasso logistic regression 
 classifier ( llrc ) .
when predicting the quality of a data set , 
 the llrc will either 
 predict a data set correctly , or will make either a false positive 
 or false negative error .
a false positive error occurs when the llrc 
 predicts the data set to be out of control , when it was annotated 
 as in control .
the false negative error occurs when the llrc predicts 
 the data set as being in control when it was annotated as out of control . 

the rates of these errors depend on the threshold criteria and are 
 inversely related ( as one goes up , the other generally goes down ) . 
 to this end , a loss function is constructed that reflects the consequences 
 we attribute to the two types of errors .
when the llrc predicts a 
 data set correctly , zero loss occurs . when the llrc produces a false 
 positive , a loss of 1 occurs , and a false negative receives a loss 
 of   1 .
hence , the consequences ( or cost ) of a false 
 negative are  times greater than those of a false positive . cross validation was employed to determine the optimal  
 and  that minimize the expected loss .
cross validation was 
 performed on the 1150 testing / training data sets by randomly dividing 
 the data set into five equal parts .
four parts were then combined 
 to train the llrc which was subsequently used to predict the  held - out  
 part .
this continues such that each part was predicted from a model 
 trained by the other four parts .
the r package used in the creation 
 of the llrc , and a tutorial 
 for its use is available for download at http://omics.pnl.gov/software/msdataqualitysignatures.php .
one critical setting where subjective or time - consuming 
 manual 
 data analysis can lead to systemic problems is in the evaluation of 
 quality control ( qc ) data sets . in an effort to automate the assessment 
 of qc data sets , we selected a testing / training sample of 1150 data 
 sets ( see table 1 ) and manually annotated them 
 as being 
these data sets originated from four types of mass spectrometers and 
 are all replicate analyses of the same whole - cell lysate of s. oneidensis ( see experimental procedures ) .
in addition to the manual rating , all computational metrics from 
 nist and quameter were calculated for each data set .
initial analyses of the 1150 data sets using pca showed a separation 
 by quality rating and also by instrument type ( figure 1 ) .
the separation by quality rating indicates 
 that statistical classification approaches that rely on multiple variables 
 are likely to be successful , to some degree , in predicting data quality 
 ( supplemental figure 1 , supporting information ) .
each data point on 
 the plot represents a single qc data set and is identified by its 
 instrument class ( the symbol ) and its manually curated quality ( the 
 color ) .
the first two principal components explain about 40% of the 
 variability in the original data .
only the quameter data quality metrics 
 could be calculated for exactive data , as it lacks ms2 fragmentation 
 scans .
the primary goal was to build 
 a classifier that accurately predicts 
 the data set quality , specifically when a data set is out of control 
 ( poor ) .
we utilized a lasso logistic regression classifier ( llrc ) 
 which simultaneously restricts the size of model coefficients and 
 the number of quality metrics included the model ( see experimental procedures ) .
importantly , the model does not 
 treat false negatives and false positives as equally deleterious events . 
 the different impact of each error type is reflected in the practical 
 implication of day - to - day instrument operation .
while false positives 
 incur extra work and inconvenience for operators to review and overturn 
 the decision , the false - negative error is of much greater consequence ; 
 if the classifier fails to predict an out of control data set , a problem 
 in the lc 
ms system may not be discovered and poor quality 
 data will continue to be produced .
optimal parameter values were obtained 
 by using cross validation to minimize a loss function that treated 
 false negatives as five times worse than false positives .
the resulting 
 llrcs for each of the four instrument types are shown in table 2 .
this reflects the continuum of performance of 
 real lc  ms systems in day - to - day operation .
converting this 
 value into the binary classification is accomplished by applying a 
 threshold cutoff .
figure 2 shows the sensitivity 
 and specificity as the threshold value varies for each instrument 
 platform .
the dashed gray line on each plot in figure 2 indicates the optimal threshold that minimizes the loss function
. 
 mathematical and algorithmic details for obtaining the llrc are provided 
 in the appendix . sensitivity and specificity 
 trade - off .
the 1150 curated data sets 
 are shown according to their classification from the cross - validation 
 results .
we define sensitivity as the probability 
 of correctly classifying an out of control ( or poor ) data set , equal 
 to 1 minus the false negative rate .
specificity is the proportion 
 of good data sets that are correctly classified and is equal to 1 
 minus the false positive rate ( see experimental procedures ) .
the dotted vertical line indicates the optimal threshold ,  , 
 which balances the cost of a false positive or false negative classification 
 error .
the final step was to develop an 
 llrc for predicting the quality 
 of future data sets on each platform .
these classifiers were created 
 by training on all the available data using the optimal parameter 
 values previously obtained . in developing the llrc , the lasso method 
 selected between 2 and 12 quality metrics to be included in the llrc 
 for each of the four instrument types .
the llrc utilized a different number 
 of metrics to classify data from each of the different instrument 
 types .
the number of metrics used seems to correlate with how well 
 separated the data sets appeared in the pca analysis ( supplemental figure 1 , supporting information ) .
exactive data , which required only two metrics , was the most distinctly 
 separated by the first two principle components .
in contrast , orbitrap 
 data required 12 metrics and was visibly less clearly separated in 
 the pca analysis .
it is important to note that many of the nist and 
 quameter quality metrics are highly correlated with each other .
although 
 lasso selected a certain subset of these metrics , it is likely there 
 are other subsets that would have performed similarly had they been 
 selected .
consequently , one can not directly compare the subset of 
 metrics chosen for one instrument class to another , and we do not 
 claim that the metrics in table 3 constitute 
 the best set .
table 2 shows the sensitivity 
 and specificity 
 at the optimal threshold for each instrument platform .
the sensitivity 
 for each instrument category ranged from 100% for exactive to 89.4% 
 for ltq - orbitrap .
the specificity ranged from 97% for exactive to 
 84.6% for ltq ion trap ( which had a very high sensitivity of 97% ) . 

a value of  that performs well with both sensitivity and specificity 
 is preferable .
however , if one measure is more important in a specific 
 cost / benefit scenario , the threshold will adjust to reflect the trade - offs 
 captured in the loss function .
sensitivity was more desirable , 
 and , therefore , resulted in a threshold with a larger discrepancy 
 between sensitivity and specificity .
moreover , the expected rates 
 of false positives and false negative are implicitly considered as 
 part of the optimization .
the llrc is 
 a composite classifier , in that it relies on multiple metrics to determine 
 the quality of each data set .
some instrument operators may rely only 
 on a single metric to assess data quality .
commonly used single metrics 
 include : total tryptic peptide count ( p_2a ) , distinct tryptic peptide 
 count ( p_2c ) , and the absolute intensities of peaks in the tic ( ms1_2b ) . 
 in table 4 , we present a comparison of the 
 llrc to these single measures of quality for velos - orbitrap data sets . 
 the table illustrates the proportion of false positives and false 
 negatives that would occur when using the llrc , as well as the four 
 aforementioned metrics , each applied individually with their own threshold . 
 as before ,
the loss function used to optimize the llrc parameters 
 assumed that false negatives are 5 times worse than false positives , 
 resulting in only 7.1% ( 24 ) false positives and 6.6% ( 8) false negatives 
 ( see table 4 ) . to create an equitable comparison , 

the threshold for each individual metric was selected to achieve a 
 false negative rate of 0.066 ( equal to that of the llrc ) .
the llrc 
 had a false positive rate of 7.1% , while the single metrics resulted 
 in considerably worse false positive rates ranging between 32.3% and 
 73.5% ( see table 4 ) .
the llrc kept the false 
 negative rate low ( i.e. , high sensitivity to detecting out - of - control 
 data sets ) while also controlling the false positive rate . to frame 
 this comparison in real - world terms ,
let us compare llrc to the p_2c 
 single metric classifier in terms of the total number of data sets 
 that would require validation .
our comparison depends on two assumptions : 
 both classifiers have the same sensitivity ( 0.934 ) , and if a classifier 
 predicts a data set is poor it will be curated .
consequently , if we 
 use the p_2c classifier instead of the llrc , we would expect to have 
 to validate an additional ( 0.3230.071 )    
 100% of the data sets . here ,  is the unknown fraction of data 
 sets that truly are out of control .
so , in a group of 1000 data sets , 
 if  were 0.15 , we would expect to have to curate 38 additional 
 data sets .
although the time spent in curating a single data set is 
 typically small ( 12 min ) , there is a distinct advantage for 
 automating curation and avoiding manual revalidation .
individually , 
 the 38 additional manual analyses may not take much time , but the 
 repetitive monotony increases the propensity for operator error and 
 drifting subjectivity .
the sensitivity is held constant 
 at 0.934 to show the sensitivity for any single metric compared to 
 the llrc . from rudnick et al .
p_2c is 
 the total unique tryptic peptide identifications ; p_2a is the total 
 spectrum identifications ; ms1_2b is the median tic .
to further 
 validate the llrc , 
 we predicted the data quality of an additional 1321 noncurated velos - orbitrap 
 data sets using the optimal llrc trained on all the data ( denoted f * in the appendix ) .
we randomly 
 ( but systematically ) selected 100 of these classified data sets such 
 that the probabilities estimated by the logistic regression were evenly 
 spaced across the range of predicted probabilities .
the sensitivity 
 was quite similar to the cross validation estimates ( shown in table 2 ) , but the specificity was 11% lower than what was 
 observed in cross validation .
notwithstanding this reduction in specificity , 
 it is noteworthy that sensitivity was preserved , especially because 
 sensitivity is more important than specificity ; that is , a false negative 
 error is considerably worse than the false positive error .
the primary purpose 
 for which we use the data is to build a classifier for automatically 
 detecting out - of - control data sets and therefore poor instrument performance . 

we envision , however , that this corpus could be used for a myriad 
 of other bioinformatics and biostatistics applications . with data 
 presented on four different classes of instruments ,
an obvious application 
 would be the development of ms / ms scoring functions , using this data 
 to understand how fragmentation differs between instrument types . 

a second type of analysis could be to understand the qualitative presence / absence 
 of various peptides observed across a large number of replicates . 

the missing data problem is an important issue for quantitative proteomics , 
 and this corpus would allow for rigorous understanding of the scope 
 of the problem and potentially causes for sporadic peptide observation . 

another application that we envision is the investigation of unidentified 
 or unattributed spectra . with thousands of lc 
ms / ms data sets , 
 there are a very large number of fragmentation spectra for which there 
 is not a confident identification . of those unidentified species , 

many are fragmented in multiple data sets ; spectrum averaging or other 
 methods could be utilized to obtain a confident identification . for 
 these and
doubtless many other bioinformatics explorations , we have 
 posted the data to public repositories and encourage researchers to 
 use them as a resource .
we note that the original intent of our research 
 ( classifying bad data sets ) necessitated the publication of lc 
we openly acknowledge that 
 some of the data are of low quality , as was required for our goals . 

therefore , in secondary analyses of these data , we encourage users 
 to carefully consider whether such data are appropriate for their 
 application , and consult supplementary files 1 
 and 2 , supporting information for a list of data that is considered 
 out - of - control . the presented approach demonstrates how the 
 lasso logistic regression 
 classifier ( llrc ) approach can leverage a collection lc 
we note that our classifier models may not be directly 
 usable by those whose lc 
however , the described methodology and accompanying 
 software can be applied by others who want to develop an automated 
 qc analysis .
given the diverse qc samples , instruments and even goals 
 of various research laboratories and core facilities , we strongly 
 recommend that each group train their own classifier .
our research 
 shows that a statistically trained composite metric is dramatically 
 more effective than any single metric .
thus , this work describes an 
 automated process that will greatly improve the use of quality metrics .
while the approach produces a dichotomous prediction ( i.e. , in 
 or out of control ) for each data set , operators may find it more useful 
 to omit the final classification step and base their assessments on 
 the probability scores alone .
these scores , which range in the unit 
 interval , may be useful in making decisions about borderline cases . 
 similarly , adding an explicit borderline class would be a valuable 
 future direction .
the llrc may also provide the underpinnings for 
 a more comprehensive statistical model designed to identify the onset 
 of operational drift .
such a methodology would alert the instrument 
 operator to more closely monitor the system . a final extension
understanding the particular subsets of the quality metrics 
 that correspond to out - of - control conditions in lc or ms would assist 
 operators in diagnosing malfunctions .
moreover , new and more specific 
 metrics could be created to specifically target different types of 
 malfunction .
such classifications would provide immense utility for 
 day - to - day instrument operation .
all data used for 
 this project are available at http://omics.pnl.gov and 
 have been deposited to the proteomexchange consortium ( http://proteomecentral.proteomexchange.org ) via the pride partner repository with 
 the data set identifiers pxd000320 , pxd000321 , pxd000322 , pxd000323 , 
 and pxd000324 .
this includes representative instrument raw files and 
 the complete list of qc metrics for all data sets ( supplemental files 1 and 2 , supporting information ) .
 ensuring data quality and proper 
 instrument functionality is a 
 prerequisite for scientific investigation . 
 manual quality assurance 
 is time - consuming and subjective . 
 metrics for describing liquid chromatography 
 mass spectrometry ( lc  ms ) data have been developed ; however , 
 the wide variety of lc  ms instruments and configurations precludes 
 applying a simple cutoff . using 1150 manually classified quality control 
 ( qc ) data sets , we trained logistic regression classification models 
 to predict whether a data set is in or out of control . 
 model parameters 
 were optimized by minimizing a loss function that accounts for the 
 trade - off between false positive and false negative errors . 
 the classifier 
 models detected bad data sets with high sensitivity while maintaining 
 high specificity . 
 moreover , the composite classifier was dramatically 
 more specific than single metrics . 
 finally , we evaluated the performance 
 of the classifier on a separate validation set where it performed 
 comparably to the results for the testing / training data sets . by presenting 
 the methods and software used to create the classifier 
 , other groups 
 can create a classifier for their specific qc regimen , which is highly 
 variable lab - to - lab . in total , this manuscript presents 3400 lc  
 ms 
 data sets for the same qc sample ( whole cell lysate of shewanella 
 oneidensis ) , deposited to the proteomexchange with identifiers 
 pxd000320pxd000324 . 
Introduction
Experimental Procedures
Results
Discussion