A method for detecting and correcting feature misidentification on expression microarrays
- I-Ping Tu†^{1, 6}Email author,
- Marci Schaner^{2},
- Maximilian Diehn^{2},
- Branimir I Sikic^{3},
- Patrick O Brown^{2, 5},
- David Botstein^{4} and
- Michael J Fero†^{1, 2}Email author
https://doi.org/10.1186/1471-2164-5-64
© Tu et al; licensee BioMed Central Ltd. 2004
Received: 06 July 2004
Accepted: 09 September 2004
Published: 09 September 2004
Abstract
Background
Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors.
Results
To date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10–50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them.
Conclusion
Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays.
Background
Process steps The process fail points for cDNA based microarray production. Steps shown in italics are accessible to the testing methods outlined here. Earlier steps may require sequencing to test. The process ID is used to identify steps where the problems, if any, are found.
Process ID | Problem Type | Note |
---|---|---|
-999 | Unidentified | |
0.0 | Source.General | persists across all arrays at clone, 96 level |
0.1 | Source.Contamination | |
0.2 | Source.MisID | |
1.0 | Prep.General | persists across all arrays at clone, 96 level |
1.1 | Prep.Contamination | |
1.2 | Prep.MisID | |
1.2.1 | Prep.MisID.OrderError | |
1.2.2 | Prep.MisID.Rot | |
1.3 | Prep.Fail | |
2.0 | PCR.General | persists for 1 PCR round at 96 level |
2.1 | PCR.Contamination | |
2.2 | PCR.MisID | |
2.3 | PCR.Fail | |
2.4 | PCR.Cleanup | |
3.0 | Replate.General | persists for 1 PCR round at 96 level |
3.1 | Replate.Contamination | |
3.2 | Replate.MisID | |
3.2.1 | Replate.MisID.OrderError | |
3.2.2 | Replate.MisID.96Rot | |
3.2.3 | Replate.MisID.384Rot | |
4.0 | Print.General | persists for 1 print run batch at 384 level |
4.1 | Print.Contamination | |
4.2 | Print.MisID | |
4.2.1 | Print.MisID.OrderError | |
4.2.2 | Print.MisID.Rot | |
4.3 | Print.Fail | dried out plate, too concentrated, too weak etc. |
5.0 | Scan.General | anything after array production |
Results
Finding misidentified data
Test for 96 well plate handling error. a) In this table we see that the middle bank of comparisons indicates a discrepancy between data from the first PCR run and the second, at the 96-well plate level. b) A check of the distance matrix results show that the swapped plates are h and i in one case, and n and o in the other. Bold indicates P-value = 0.3, italic indicates P-values between 1.0E-03 and 1.0E-04, while regular font indicates P-values < 1.0E-04.
a) | R_{m, m} | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PCR Round 1 vs 1 | PCR Round 1 vs 2 | PCR Round 2 vs 2 | ||||||||||
Plates | A1 v A2 | A2 v A3 | A3 v A4 | A4 v A5 | A2 v B1 | A3 v B2 | A4 v B3 | A5 v B4 | B1 v B2 | B5 v B3 | B1 v B4 | B5 v B4 |
{a, a} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 |
{b, b} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 3 | 1 |
{c, c} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{d, d} | 2 | 1 | 1 | 1 | 1 | 1 | 5 | 9 | 1 | 1 | 1 | 1 |
{e, e} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 |
{f, f} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{g, g} | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 |
{h, h} | 1 | 1 | 1 | 1 | 343 | 322 | 408 | 294 | 1 | 1 | 1 | 1 |
{i, i} | 2 | 1 | 1 | 1 | 421 | 290 | 402 | 359 | 1 | 1 | 1 | 1 |
{j, j} | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 4 | 1 |
{k, k} | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{l, l} | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 |
{m, m} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{n, n} | 1 | 1 | 1 | 1 | 255 | 141 | 20 | 167 | 1 | 1 | 1 | 1 |
{o, o} | 1 | 1 | 1 | 1 | 330 | 248 | 357 | 277 | 3 | 1 | 5 | 6 |
{p, p} | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 2 | 1 | 1 |
{q, q} | 1 | 1 | 1 | 1 | 4 | 3 | 3 | 2 | 1 | 1 | 1 | 1 |
{r, r} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 |
{s, s} | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{t, t} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{u, u} | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
{v, v} | 1 | 1 | 1 | 1 | 1 | 1 | 7 | 1 | 1 | 1 | 1 | 1 |
{w, w} | 2 | 1 | 1 | 1 | 1 | 16 | 1 | 2 | 1 | 1 | 1 | 1 |
b) | A2 v B1 | A3 v B2 | A4 v B3 | A5 v B4 | ||||||||
D_{mm} | D_{mn} | D_{mm} | D_{mn} | D_{mm} | D_{mn} | D_{mm} | D_{mn} | |||||
MEAN | 0.33 | 0.97 | 0.34 | 0.89 | 0.44 | 0.98 | 0.37 | 0.96 | ||||
SD | 0.2 | 0.11 | 0.2 | 0.13 | 0.21 | 0.11 | 0.21 | 0.12 | ||||
{h, h} | 0.99 | 1.02 | 1.07 | 1 | ||||||||
{i, i} | 1.09 | 1 | 1.07 | 1.06 | ||||||||
{h, i} | 0.22 | 0.09 | 0.33 | 0.31 | ||||||||
{i, h} | 0.28 | 0.07 | 0.11 | 0.14 | ||||||||
{n, n} | 0.98 | 0.91 | 0.85 | 0.92 | ||||||||
{o, o} | 1.01 | 1.02 | 1.07 | 1.02 | ||||||||
{n, o} | 0.1 | 0.51 | 0.45 | 0.52 | ||||||||
{o, n} | 0.04 | 0.17 | 0.11 | 0.05 |
Finding a 384-well plate rotation
Test for rotated plates. a) Plate rotation results from the rank matrices R and R' . The flagged comparisons indicate a plate rotation for plate p. b) Plate rotation distance matrix comparison. The data meet the criteria for a plate rotation.
a) | A1 v B | A2 v C | A3 v D | A4 v E | ||||
---|---|---|---|---|---|---|---|---|
Plates | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} |
{i, i} | 1 | 102 | 1 | 95 | 1 | 82 | 1 | 93 |
{j, j} | 1 | 18 | 1 | 102 | 1 | 16 | 1 | 64 |
{k, k} | 1 | 52 | 1 | 99 | 1 | 102 | 1 | 41 |
{l, l} | 6 | 108 | 3 | 35 | 9 | 76 | 1 | 22 |
{m, m} | 1 | 51 | 1 | 79 | 1 | 85 | 1 | 68 |
{n, n} | 1 | 14 | 1 | 35 | 1 | 41 | 1 | 93 |
{o, o} | 1 | 92 | 1 | 92 | 1 | 102 | 1 | 76 |
{p, p} | 78 | 1 | 48 | 1 | 46 | 1 | 65 | 1 |
{q, q} | 1 | 117 | 1 | 96 | 1 | 87 | 1 | 111 |
{r, r} | 1 | 118 | 1 | 110 | 1 | 115 | 1 | 112 |
{s, s} | 1 | 116 | 1 | 108 | 1 | 109 | 1 | 113 |
{t, t} | 1 | 69 | 1 | 93 | 3 | 20 | 1 | 103 |
{u, u} | 1 | 116 | 1 | 103 | 1 | 72 | 1 | 84 |
{v, v} | 1 | 83 | 1 | 104 | 1 | 85 | 1 | 64 |
b) | A1 v B | A2 v C | A3 v D | A4 v E | ||||
D_{mm} | D'_{mm} | D_{mm} | D'_{mm} | D_{mm} | D'_{mm} | D_{mm} | D'_{mm} | |
MEAN | 0.26 | 0.96 | 0.23 | 0.88 | 0.19 | 0.81 | 0.21 | 0.88 |
SD | 0.14 | 0.1 | 0.14 | 0.14 | 0.1 | 0.17 | 0.13 | 0.14 |
{pp} | 0.95 | 0.84 | 0.85 | 0.97 | ||||
{pp} | 0.21 | 0.14 | 0.23 | 0.24 | ||||
P-value | 4.10E-07 | 3.20E-14 | 6.60E-06 | 6.30E-08 | 2.10E-11 | 3.20E-04 | 2.50E-09 | 2.40E-06 |
Finding a misidentified 384-well plate
Test for swapped plates. a) Plate r is seen here to have a problem, but from the table we see that it is most certainly not a plate rotation. b) A check of the distance matrix shows the {r, q} comparison to be quite good and satisfies the criteria for a match, indicating that plates r and q have been accidentally swapped.
a) | A1 v B | A2 v C | A3 v D | A4 v E | ||||
---|---|---|---|---|---|---|---|---|
Plates | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} | R_{mm} | R'_{mm} |
{i, i} | 1 | 82 | 1 | 48 | 1 | 107 | 1 | 76 |
{j, j} | 1 | 45 | 1 | 87 | 1 | 87 | 1 | 89 |
{k, k} | 1 | 18 | 1 | 45 | 1 | 63 | 1 | 15 |
{l, l} | 1 | 85 | 1 | 106 | 1 | 103 | 1 | 93 |
{m, m} | 1 | 30 | 1 | 49 | 1 | 53 | 1 | 37 |
{n, n} | 1 | 115 | 1 | 113 | 1 | 37 | 1 | 102 |
{o, o} | 1 | 24 | 1 | 64 | 1 | 82 | 1 | 100 |
{p, p} | 1 | 110 | 1 | 102 | 1 | 104 | 1 | 93 |
{q, q} | 1 | 118 | 1 | 111 | 1 | 110 | 1 | 91 |
{r, r} | 77 | 7 | 48 | 12 | 51 | 22 | 16 | 23 |
{s, s} | 1 | 55 | 1 | 44 | 1 | 90 | 1 | 88 |
{t, t} | 1 | 115 | 1 | 101 | 1 | 110 | 1 | 99 |
{u, u} | 1 | 118 | 1 | 93 | 1 | 74 | 1 | 95 |
{v, v} | 1 | 15 | 1 | 13 | 1 | 29 | 1 | 71 |
b) | A1 v B | A2 v C | A3 v D | A4 v E | ||||
D_{mm} | D_{mn} | D_{mm} | D_{mn} | D_{mm} | D_{mn} | D_{mm} | D_{mn} | |
MEAN | 0.19 | 0.93 | 0.25 | 0.94 | 0.28 | 0.95 | 0.2 | 0.81 |
SD | 0.14 | 0.07 | 0.19 | 0.07 | 0.17 | 0.07 | 0.16 | 0.1 |
{rq} | 0.3 | 0.26 | 0.25 | 0.34 | ||||
{rr} | 0.97 | 0.95 | 0.98 | 0.72 | ||||
P-value | 1.30E-08 | 0 | 1.10E-04 | 0 | 1.90E-05 | 0 | 5.80E-04 | 1.30E-06 |
Discussion
Out of the 265 million features checked in our database using MuFu, problems were detected and corrected on 1.3 million of them. That we were able to find and correct both previously known and unknown problems gives us confidence in the algorithm. That so few problems existed overall (0.5%) reassures us as to the robustness of our process in general.
Microarray data, by its sheer volume, presents interesting laboratory information management challenges. The data arrive at the investigators desk after a significant number of steps. We have found that the better you track production, quality control, and experimental steps, the better chance you have of uncovering the reasons behind discrepancies that may appear in the data. Often, statistical analyses look only at the data presented as final expression values or ratios, without taking into account relevant quality control data. In our effort to understand our errors and the source of large systematic discrepancies in our data we have found the MuFu algorithm a useful test of certain classes of process errors, and as a check for general problems with specific process steps or microtiter plates. We use MuFu to find, verify and fix problems that can be attributed to an error in plate processing. We also flag plates for which we can find no specific problem but we see yield inconsistent results. These may be plates that, at some point in the process, had a problem (cross contamination, a PCR problem) that was not detected while the process step was being carried out. The fact that we can test the data, using our standard quality control hybridizations, for these types of quality issues is reassuring and has helped us gain confidence in our data.
Obviously, there are many other classes of error that creep into microarray data. Aside from the gross process errors that are amenable to detection, as we have described here, there is also a large class of more subtle systematic errors that can contribute to the overall systematic error on the expression ratio measurement. Isolating the source of these individual errors is sometimes quite difficult. Properties of the microarray feature such as spot size, shape and uniformity can contribute, but the majority of systematic errors are introduced at the time the experiment is performed. Slide post processing, RNA quality, labelling, hybridization and washing all lend the possibility for introducing systematic errors. Improvements in protocols and hybridization apparatus have helped reduce these errors and should continue to do so in the future. As these sources of error are identified and eliminated, expression microarrays will continue to provide progressively more sensitive measures of gene expression.
Conclusions
Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We have shown the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays. We generalized our procedure using a simple heuristic, which found and fixed several problems with the proper assignment of gene identifier with physical microarray feature. We found thirteen print runs (9K arrays) that had four plates mistracked, six print runs with single 384 plate rotations, and one instance of a plate rotation at the 96 well plate level. One skipped plate was detected, as well as one plate printed twice. Out of the 265 million features checked in our database using MuFu, problems were detected and corrected on 1.3 million of them. That we were able to find and correct both previously known and unknown problems gives us confidence in the algorithm. That so few problems existed overall (0.5%) reassures us as to the robustness of our process in general. A list of corrected arrays can be found at http://www.microarray.org/mufu.
Methods
We follow the simple heuristic outlined here. The flowchart for the program is shown in Figure 1. In the figure we do not include additional loops needed to repartition the data in different ways for different scenarios.
Data partitioning
1. Begin by partitioning the gene expression data on an array into subsets according to plate. Other partitions can also be made but the plate level partition is the most useful for our purposes. Let A _{ ij }be the intensity measurements of array A, subset i and gene index j. The measurements are usually of the channel (Cy3 or Cy5) used as a common reference. For example, if we partition the data by the 384-well plate each feature once occupied at some point in its process history, A is the array id, i is the plate id, and j is the well index between 1–384. Let A _{ i }be the vector (A _{ i 1},...,A _{ i 384}). In our vernacular this is the 384-well plate expression vector for plate i. Let B _{ ij }andB _{ i }be the similar definitions for array B. We also reverse the data vectors from array A and label it A _{ i }' . In our notation, A _{ i }' = (A _{ i 384},...,A _{ i 1}) is the expression vector for a plate rotated by 180°.
2. Generate a distance matrix {D _{ mn }, 1 ≤ m, n ≤ N} in which each element D _{ mn }= 1 - corr(A _{ m }, B _{ n }). For the 384-well plate example, A _{ m }is the 384-well expression vector for plate m on array A, B _{ n }is the 384-well expression vector for plate n on array B, and D _{ mn }is the distance between the two vectors in this 384 dimensional space. We also generate the corresponding rotated-distance matrix {D' _{ mn }, 1 ≤ m, n ≤ N} for the plate rotation case, in which each element D' _{ mn }= 1 - corr(A' _{ m }, B _{ n }). We tried several correlation functions including Euclidean and Pearson but found the Pearson to be best suited to this task.
3. Generate a rank matrix {R _{ mn }, 1 ≤ m, n ≤ N} by converting the distances to ranks such that the row m in the rank matrix is the order statistic of the row m of the distance matrix. We also generate the corresponding rotated-rank matrix {R' _{ mn }} for {D' _{ mn }}. Ideally, for the case where we are comparing identical subsets from two different arrays we expect the diagonal elements of the rank matrix to all be equal to one, which means that each subset of genes from the first array matches its corresponding subset in the second array the best. In general, due to the statistical variation in array data, the diagonal elements are not all equal to one, even if there are no misidentification errors. The examples show that this does not hinder us from making a clear distinction between real problems in the data and statistical fluctuations.
Identification of rotated plates
A plate rotation may have an affect on a single microarray batch if it occurs during array printing or may persist across many print batches if it happens during a 96-well (PCR) process step. In any case, the mismatch will persist across many array comparisons. To check for plate rotations in a print batch, we compare an array sample (A1, A2, A3, A4 in the example) from the print batch in question to a control sample of arrays (B, C, D, E in the example) selected from several distinct print batches. By comparing the rank R, and rotated-rank R', matrix assignments for comparisons across array batch boundaries we can quickly flag possible rotated plates. A visualization of this test is shown in Table 3a. In the table we have flagged the top 5% of all ranks in the rotated column and the bottom 95% in the unrotated column. If the flags agree across all comparisons, we have a strong indication that a plate rotation has occurred. If we see a flag raised in this test, but we cannot attribute it to a plate rotation, this may indicate a different class of process error. In particular, if the flag is raised for all pairwise comparisons of the batch being tested (in this case batch A) against all of the control batches (in this case batches B, C, D and E) in the non-rotated case, we conclude that the corresponding flagged plate or partition from batch A may be misidentified. Note that in the limit that the partitions of the array are all uniform in expression ratios there is a 5% probability of a spurious flag. For this reason it is better to use high quality, highly variegated control arrays for such tests. Next, to better resolve ambiguous cases and to check our rank matrix determination we use the distance rather than rank matrix. If, for example, a plate x is to be considered a rotated plate, the following three criteria must be met.
1. D' _{ xx }<D _{ xx }. The rotated-distance must be less than the non-rotated distance.
2. D' _{ xx }is close to the mean of the distribution, {D _{ mm }, 1 ≤ m ≤ N}, and D_{ xx }is close to the mean of the distribution {D' _{ mm }, 1 ≤ m ≤ N}.
3. D' _{ xx }is an outlier of the distribution {D' _{ mm }, 1 ≤ m ≤ N}, and D _{ xx }is an outlier of the distribution {D _{ mm }, 1 ≤ m ≤ N}.
The second and third criteria above are valid if the distributions of {D' _{ mm }, 1 ≤ m ≤ N} and {D _{ mm }, 1 ≤ m ≤ N} separate well. In Figure 5 we showed that our data support this model.
Finding misidentified plates
- 1.
D _{ xy }<D _{ xx }. The mismatch distance is shorter than the match distance.
- 2.
D _{ xy }is close to the mean of the distribution {D _{ mm }, 1 ≤ m ≤ N}, and D _{ xx }is close to the mean of the distribution {D _{ mn }, 1 ≤ m ≠ n ≤ N}.
- 3.
D _{ xx }is an outlier of the distribution {D _{ mm }, 1 ≤ m ≤ N}, and D _{ xy }is an outlier of the distribution {D _{ mn }, 1 ≤ m ≠ n ≤ N}.
Again, the second and third criteria above are valid if the distributions of {D _{ mm }, 1 ≤ m ≤ N} and {D _{ mn }, 1 ≤ m ≠ n ≤ N} are well resolved.
Notes
Declarations
Acknowledgements
We thank members of the Brown and Botstein labs for encouragement and support. This work was supported in part by a grant from the National Cancer Institute.
Authors’ Affiliations
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