Hello everyone,

Recently I have been analyzing my protein microarray data. I choose vsn method to process the raw data as many relative researches did. My data looks like as below:

 There are two groups of samples and 190 kinds of proteins, and T represents tumor group(n=87), N represents normal groups(n=62). The last row is the control spot with 'No DNA'.

Firstly, I use my data to construct expression set. Then I can call justvsn to normalize the whole data just as the 'Introduction to robust calibration and variance stablisation with VSN  Wolfgang Huber  April 16,2015' says, as in content 3 "Running VSN on data from mutiple arrays(single colour normalisation)".

Now I want to normalize the data like Suman Sundaresh et al did in their article " From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis". In "Data preprocessing and normalization" section of the article says "Since the dataset contains expression profiles of 244 of the 1741 F.tularensis antigens that generated some immune response, only the seven known true-negative intra-array control signals (cell-free expression reactions lacking template gene) are used as ‘house-keeping’ probes to obtain the scale and offset parameters. The transformation function‘vsn’ is then applied to the whole dataset using these parameters. This method calibrates the measurements and renders the variance relatively independent of the mean signal."

So I want to do the same to use negative controls to obtain these parameters then to normalize the whole data. But I don't know how to write the R command.Hope anyone can tell me how to do it. Thanks a lot. 

Dear Guojie

does Chapter 7 in the vsn vignette, Normalisation with ’spike-in’ probes, address your question?

Using only 7 probes as input to fit the vsn model is pushing the limits of identifiability though (i.e. the result could be rather noisy). My recommendation would be to try use more probes (say, at least 40), even for the price of some small bias. You could do some experimentation with these choices and hopefully it doesn't make a big difference.

Kind regards

Wolfgang

Dear Dr. Huber,

Thank you for your reply.

I thought Chapter 7 was all about positive controls or features which were differentially expressed.Now I get it that it can also apply to negative controls. But the example in the chapter says that taking features 100 to 200 as spike-in controls then to normalize the whole dataset. My understanding of features means the proteins in my data. However, my dataset has only one "No DNA" probe but with 149 samples(87T+62N), that means there is only one feature. I'll show my data in R:

>library(vsn)

> exprs=as.matrix(read.table('clipboard',sep='\t',header=T,row.names=1))

> dim(exprs)
[1]  96 149

> min= new("ExpressionSet", exprs=exprs)
> dim(min)
Features  Samples 
      96      149 

> head(exprs(min))
               T18    T20    T21    T23    T24    T25    T28    T29    T30
Rv-BILF2    4071.0 2839.0 2907.5 2458.0 1951.0 2042.5 2091.5 2481.5 2591.5
Rv-LMP2-E6  2745.0 2468.0 2186.5 2241.0 1847.0 1809.0 1756.5 2051.5 2110.5
Rv-BZLF1-E1 4089.5 3780.0 3473.5 3533.5 2858.5 2598.0 2290.0 2825.0 2907.5
Rv-BZLF2    3815.0 3624.0 3243.0 3376.5 2969.5 2743.5 2426.5 2851.5 2796.5
Rv-EBNA-LP  3442.0 3405.0 3191.5 3392.5 2875.0 2780.0 1602.5 1558.0 1884.0
Rv-BVRF2    3394.0 7064.5 3011.5 4015.0 6506.0 3027.5 1758.0 6891.0 2408.0
               T31    T33    T35    T36     T37    T42    T43    T44    T46
Rv-BILF2    3178.0 2056.5 2067.5 2071.5  2310.0 1440.0 2184.0 2143.5 2235.5
Rv-LMP2-E6  2544.5 1733.5 1870.5 1901.0  2200.0 1464.0 2131.5 2141.0 2255.0
Rv-BZLF1-E1 3103.5 2258.0 2428.0 2694.5  2976.0 2171.5 5366.5 3013.0 2968.5
Rv-BZLF2    3262.0 2345.5 2681.5 3341.5  3051.5 2366.0 2994.0 3083.5 3336.5
Rv-EBNA-LP  2186.0 1598.5 2490.0 2541.5  2825.5 2176.0 2754.5 2945.5 3218.0
Rv-BVRF2    2547.5 1618.5 2734.0 3974.5 14521.5 2538.5 6704.0 2830.0 4969.5
               T47    T48  T49    T50    T52    T53    T54    T55     T56
Rv-BILF2    2171.0 2691.5 2086 1182.0 3536.5 5663.0 3865.0 4393.0  3492.5
Rv-LMP2-E6  2169.0 2599.5 2133 1213.0 2629.0 6586.0 2966.0 3542.5  2762.0
Rv-BZLF1-E1 2974.5 3287.0 2914 1633.5 3804.0 7165.5 4274.5 4924.0  4046.5
Rv-BZLF2    3197.5 3693.0 3072 1880.5 2789.0 5733.0 4609.0 4411.0  4350.5
Rv-EBNA-LP  3090.5 3351.5 2885 1742.5 2337.0 4355.0 2673.0 3117.5  2542.0
Rv-BVRF2    3081.0 3916.0 2619 1491.0 7124.5 4499.5 2765.5 3805.0 10212.5
               T57    T58    T59    T60    T61    T62    T63    T64    T65
Rv-BILF2    3433.0 2345.5 3287.5 2820.5 2862.5 3387.5 3639.0 2913.5 2545.5
Rv-LMP2-E6  2621.5 2149.0 2838.5 2204.0 2409.0 2549.5 2685.0 2403.0 2118.5
Rv-BZLF1-E1 3585.0 2920.0 4146.5 3385.5 3473.5 3786.0 3936.5 3637.0 3210.5
Rv-BZLF2    3711.0 2746.5 4260.5 3732.0 3603.0 3797.0 4187.5 3969.0 3089.5
Rv-EBNA-LP  2532.5 1884.0 2611.5 2060.5 2086.0 2355.5 2565.5 2064.5 1956.5
Rv-BVRF2    4030.5 1759.0 2533.0 2683.0 2860.5 2266.5 2784.5 8781.0 2781.0
               T67    T68    T70    T73    T74    T76    T77    T78    T79
Rv-BILF2    2997.0 2945.5 3374.0 2259.0 3939.0 3912.0 3576.5 2783.0 4715.5
Rv-LMP2-E6  2123.5 2282.0 2485.0 2505.5 5645.5 5749.0 4789.0 3394.0 7064.0
Rv-BZLF1-E1 3209.0 3284.5 3285.5 2225.5 3119.5 2933.0 3056.0 2582.5 3391.0
Rv-BZLF2    3595.0 3390.0 3755.0 2470.0 5299.0 5014.0 4507.0 3270.0 9299.5
Rv-EBNA-LP  1850.5 1900.0 2154.0 2318.0 3039.5 3078.0 2974.0 2551.0 3059.0
Rv-BVRF2    2036.0 2083.5 2689.5 3034.0 3408.5 3675.5 3266.5 2558.5 4012.0
               T80    T81    T82    T83    T84    T86     T87    T88    T89
Rv-BILF2    1953.5 3874.5 4069.5 2675.0 4435.5 4002.0  5218.0 4088.0 4203.0
Rv-LMP2-E6  2481.5 6554.5 4474.0 3106.5 4543.5 4190.5  5260.0 4366.5 4520.5
Rv-BZLF1-E1 1940.5 2707.0 3987.5 2845.0 4049.0 3572.5  5086.5 3783.5 3875.0
Rv-BZLF2    2652.5 5526.5 4349.5 3123.0 4408.0 3962.5  5168.0 4222.5 4392.0
Rv-EBNA-LP  1788.5 2311.0 4280.0 3020.0 4314.0 3842.5  4641.0 3902.5 4253.0
Rv-BVRF2    2248.5 3876.0 3903.0 2801.0 7052.0 3942.5 19028.5 4526.5 4061.0
               T91    T92    T93    T94    T95    T96    T97    T98    T104
Rv-BILF2    4097.5 3438.5 2464.5 2647.0 2522.5 2702.0 2518.5 1771.0  6246.0
Rv-LMP2-E6  4413.0 3865.5 2832.5 2713.5 2743.0 2863.5 2677.5 1894.0 11212.5
Rv-BZLF1-E1 3923.0 3417.0 4919.5 2445.5 2495.5 2513.0 2400.0 1789.5  4074.5
Rv-BZLF2    4272.0 3877.5 2679.5 2659.0 2628.5 2788.5 2679.5 2156.5  9517.0
Rv-EBNA-LP  4054.0 3757.0 2913.0 2552.0 2604.0 2751.5 2663.0 2067.0  3641.0
Rv-BVRF2    5742.0 3366.0 2571.5 2598.5 2584.0 2541.0 2426.0 1821.0  7249.0
              T109   T121    T127    T129   T131    T132   T176    T178
Rv-BILF2    5692.0 3163.5  5471.0  5791.5 5184.5  2804.5 2363.5  6366.5
Rv-LMP2-E6  9880.0 4672.5  8544.0  9834.0 9549.0  4786.5 2976.0 10958.5
Rv-BZLF1-E1 3459.0 2857.0  4515.5  4797.0 4167.0  3430.0 2354.0  4454.0
Rv-BZLF2    8946.5 4471.5  9417.0 10220.0 8594.0  5464.5 3215.5 10994.5
Rv-EBNA-LP  3032.0 2411.0  3899.5  4087.5 3598.0  2503.0 2244.0  3852.5
Rv-BVRF2    5428.5 2768.5 26026.5 27066.5 4791.0 16614.0 2159.0 24884.5
              T181   T185   T192    T201   T204   T208   T217   T224   T231
Rv-BILF2    4244.0 3132.5 3065.0  6843.0 2983.5 4124.0 2319.5 3681.0 3426.0
Rv-LMP2-E6  4176.0 4574.5 3901.0 10421.5 4696.5 5666.0 2911.5 5455.5 4984.5
Rv-BZLF1-E1 2821.0 2449.5 2725.5  4122.0 2165.0 3309.0 2088.0 2587.0 2832.0
Rv-BZLF2    4548.0 4944.0 4153.5 10567.0 5293.5 6000.5 2955.0 5398.0 5036.0
Rv-EBNA-LP  2453.0 2213.5 2486.5  3578.5 2084.0 2874.0 1865.5 2149.0 2274.0
Rv-BVRF2    6945.5 2944.5 3323.0 20266.5 2563.0 6881.0 2020.0 3412.5 2814.0
              T293   T295    T301   T302   T304   T305   T311     N1     N2
Rv-BILF2    3923.0 4019.0  2849.5 3122.0 2526.0 2823.5 2533.0 1631.5 1672.5
Rv-LMP2-E6  3199.0 3669.0  2206.5 2487.5 2160.5 2575.0 2107.0 1606.0 1649.0
Rv-BZLF1-E1 4245.0 4484.0  3057.5 3208.0 2840.0 2864.0 3066.5 2296.5 2195.0
Rv-BZLF2    4967.5 3896.5  3110.5 3089.5 3015.0 2928.0 3061.0 2416.0 2397.5
Rv-EBNA-LP  2876.5 2345.0  1942.0 2724.5 2298.0 2547.0 2238.0 2407.0 2300.0
Rv-BVRF2    3953.0 2986.0 44120.5 3816.0 3755.0 2641.0 9006.5 2225.0 2204.5
                N3     N4     N6     N7     N8     N9    N10    N13    N14
Rv-BILF2    1474.5 1806.5 2036.0 2023.0 1657.5 1365.5 2198.5 2585.0 1990.5
Rv-LMP2-E6  1470.5 1782.0 2030.5 1894.5 1642.5 1279.5 2197.0 2809.5 1999.5
Rv-BZLF1-E1 2139.5 2484.5 2768.0 2557.5 2184.0 1797.5 3069.0 4226.5 2810.5
Rv-BZLF2    2163.0 2630.0 2916.0 2746.0 2461.5 2229.5 3343.0 4812.5 3006.5
Rv-EBNA-LP  2059.0 2486.0 2638.5 2593.5 2332.5 2044.0 3089.5 4234.5 2759.0
Rv-BVRF2    2113.0 2563.5 2836.0 2470.0 2184.0 1842.5 2849.0 3386.0 2689.5
               N15    N16    N18    N19    N21    N23    N24    N25  N26
Rv-BILF2    2382.5 2723.0 2761.5 2024.5 1493.0 2051.0 1683.5 1518.5 1604
Rv-LMP2-E6  2446.0 2753.0 2720.5 1943.5 1377.5 1932.5 1572.5 1512.0 1634
Rv-BZLF1-E1 3223.5 4742.5 3381.0 2720.5 2016.5 2478.0 2017.0 1960.5 2141
Rv-BZLF2    3453.5 4733.0 3441.5 3018.0 2256.5 2431.5 2492.0 2162.5 2382
Rv-EBNA-LP  3093.5 4364.0 3282.5 2739.5 2103.5 2278.0 2056.5 1972.0 2112
Rv-BVRF2    3077.5 3868.5 3192.0 2447.0 1865.5 2201.0 2060.0 1882.5 1859
               N27    N28    N30    N31    N32    N33    N34    N35    N36
Rv-BILF2    1920.5 1757.5 1231.5 2960.0 2554.0 3123.5 3084.5 3573.0 3283.0
Rv-LMP2-E6  1812.5 1656.5 1168.5 2844.0 2532.5 3170.5 3099.0 3583.0 3462.5
Rv-BZLF1-E1 2327.5 2208.0 1706.0 3715.5 3351.0 4123.5 4099.0 4600.5 4441.0
Rv-BZLF2    2467.0 2470.0 1801.5 4091.5 3801.5 4587.5 4737.5 5301.5 5165.0
Rv-EBNA-LP  2316.5 2338.0 1648.0 3812.0 3485.0 4139.0 4174.5 4739.0 4578.0
Rv-BVRF2    2211.5 2065.0 1625.0 3424.0 3219.0 3856.5 3794.5 4346.5 4048.5
               N37    N39    N40    N41    N42    N43    N46    N47    N48
Rv-BILF2    3011.5 1883.5 3070.5 2806.5 2590.0 2111.0 3278.5 3258.0 3669.0
Rv-LMP2-E6  3309.5 2091.0 2544.0 2481.0 2217.5 1904.0 2749.5 2692.5 3019.0
Rv-BZLF1-E1 4188.5 2649.0 3228.0 3195.0 2986.5 2414.0 3555.5 3372.5 3839.0
Rv-BZLF2    4629.0 2992.5 3296.0 3196.5 3222.5 2489.5 3770.5 3420.0 4027.0
Rv-EBNA-LP  4358.5 2753.5 2603.0 2591.0 2433.0 2233.5 2912.0 2810.0 3187.5
Rv-BVRF2    3761.0 2287.0 2712.0 2352.5 2104.0 2062.5 3171.0 2895.5 3285.0
               N50    N52    N54    N55    N57    N58    N59    N61    N62
Rv-BILF2    4285.0 4261.0 5337.0 3638.0 2725.0 4211.5 4359.0 4086.0 4405.5
Rv-LMP2-E6  3686.5 3709.5 4954.5 3270.0 2568.0 3404.5 3686.0 3440.0 3922.5
Rv-BZLF1-E1 4558.0 4535.5 5998.5 4018.5 3213.5 4460.5 4580.5 4359.0 4875.5
Rv-BZLF2    5026.5 4764.5 6035.0 4995.0 3546.5 5027.0 4770.0 4494.5 5175.0
Rv-EBNA-LP  3867.0 3636.5 5038.5 3501.0 2801.5 3716.5 3965.5 3668.5 4182.5
Rv-BVRF2    4404.0 4308.5 4929.0 3214.5 2548.5 7666.0 3821.0 3705.0 3918.5
               N63    N64    N65    N66    N67    N68    N69    N70    N71
Rv-BILF2    4118.5 4002.5 4448.0 3453.0 4040.0 4256.0 4573.0 4972.0 5333.0
Rv-LMP2-E6  3672.0 3591.0 3876.5 3120.5 3558.0 3737.0 4061.0 4698.0 4792.5
Rv-BZLF1-E1 4665.0 4463.5 4992.5 4053.5 4531.0 4536.5 5077.5 5472.5 5676.5
Rv-BZLF2    4717.5 4604.0 5101.0 4068.0 4646.0 4656.0 5314.0 5779.0 5809.5
Rv-EBNA-LP  3778.0 3847.0 4151.0 3188.5 3730.0 3968.0 4498.0 4868.0 5125.5
Rv-BVRF2    3736.0 3482.0 4246.0 2992.0 3539.5 3749.0 4231.5 4690.5 4724.5
               N72    N73    N75    N78    N79    N80
Rv-BILF2    4888.5 4606.0 2859.5 4613.0 2135.0 1643.0
Rv-LMP2-E6  4409.5 4059.5 2533.5 7081.5 2453.5 2159.5
Rv-BZLF1-E1 5214.5 4865.0 2805.5 3697.5 2118.5 1693.0
Rv-BZLF2    5373.5 5208.0 3204.0 7612.0 2488.0 2143.5
Rv-EBNA-LP  4571.5 4185.5 2874.5 2986.0 1845.5 1409.5
Rv-BVRF2    4158.0 3974.0 2626.0 4461.0 1930.5 1507.0

> tail(exprs(min))
                    T18    T20    T21    T23    T24    T25    T28    T29
Rv-EBNA3B-E2     4242.5 4157.0 3790.5 3841.5 3063.0 2993.5 2659.5 3373.5
Rv-BLLF1-C       3493.5 3768.5 2913.5 3554.5 2689.0 2379.5 3142.0 5489.5
Rv-BLLF1-N       4212.0 4112.5 3701.5 3815.0 3407.0 2809.0 2584.5 3109.0
Rv-BNRF1-C       5223.5 4670.5 4242.5 4265.0 3526.0 3000.0 2705.5 3464.0
Rv-BNRF1-N       5952.0 5590.0 5054.5 5344.0 4492.0 3993.5 2959.0 3312.0
mean-Rv-Negative 3154.5 1297.0 1293.5 1437.0  567.5  744.0  592.0  759.5
                    T30    T31    T33    T35    T36    T37    T42    T43
Rv-EBNA3B-E2     3450.0 3748.5 2732.5 3490.5 3567.0 4247.0 2734.0 5166.5
Rv-BLLF1-C       2740.5 4299.0 2160.5 2608.5 2833.5 3114.5 2055.5 3772.0
Rv-BLLF1-N       2769.5 3387.5 2254.5 2883.5 2965.5 3310.5 2422.0 3975.0
Rv-BNRF1-C       3651.5 3740.0 2965.5 3684.5 3883.5 4432.0 2697.5 4757.0
Rv-BNRF1-N       3612.0 4028.5 2835.0 4870.0 4892.0 5742.0 3933.5 6220.5
mean-Rv-Negative 1048.0 1169.0  736.5  575.5  968.0 1193.0  326.0 1023.0
                    T44     T46    T47    T48    T49     T50    T52    T53
Rv-EBNA3B-E2     5613.5  5458.5 5199.0 5557.5 4224.0  2278.0 3960.5 6516.5
Rv-BLLF1-C       3686.0 13239.0 3668.5 4495.5 7508.5 10021.0 4893.0 6911.0
Rv-BLLF1-N       4226.0  4464.5 4351.5 4563.0 4549.0  2587.5 3851.5 6791.5
Rv-BNRF1-C       4760.0  5435.5 5356.0 5555.5 4699.5  2769.5 4762.0 7255.5
Rv-BNRF1-N       6053.0  6826.0 7044.0 7389.0 5809.5  3462.5 4059.5 7009.0
mean-Rv-Negative 1055.5  1068.0  947.5 1097.5  989.5   421.5 1243.0 3756.5
                    T54    T55    T56    T57    T58    T59    T60     T61
Rv-EBNA3B-E2     4153.0 4378.0 3726.0 3613.0 3060.0 4167.0 3832.0  3705.5
Rv-BLLF1-C       4050.5 4440.0 4733.0 3452.5 3176.0 3915.0 3186.0 25406.0
Rv-BLLF1-N       4356.0 4219.0 4849.0 3665.5 2835.5 4067.0 3228.5  3672.5
Rv-BNRF1-C       5575.5 5449.5 4932.0 4131.5 3560.5 5340.5 4237.0  4647.5
Rv-BNRF1-N       4520.5 4539.5 4066.5 3987.0 2822.0 4301.5 3744.0  4048.0
mean-Rv-Negative 1987.0 2089.5 1474.5 1191.5 1366.5 1421.5 1043.0  1126.0
                    T62    T63    T64    T65    T67    T68    T70    T73
Rv-EBNA3B-E2     3868.0 3659.0 3663.0 3121.5 4411.5 4285.0 4615.0 2611.5
Rv-BLLF1-C       3618.0 3703.5 3376.5 3175.0 3424.0 3301.0 3537.5 2943.0
Rv-BLLF1-N       3608.5 3956.5 3706.0 3039.0 3357.5 3345.5 4363.5 2908.0
Rv-BNRF1-C       4757.5 5512.5 4199.5 3602.5 4532.5 4686.5 5270.0 2348.0
Rv-BNRF1-N       4230.5 4563.5 4122.0 3582.0 4523.5 4574.5 5152.5 2294.5
mean-Rv-Negative 1227.0 1015.0  737.0  747.5  650.5  724.0  967.5 1274.0
                    T74    T76    T77    T78    T79    T80    T81    T82
Rv-EBNA3B-E2     3347.5 3435.5 3374.5 2948.5 5953.0 4375.0 3358.5 4672.5
Rv-BLLF1-C       3747.5 5824.5 3646.0 2719.5 5742.0 2258.5 4684.5 5060.5
Rv-BLLF1-N       4396.5 4658.0 3917.5 5349.0 5248.5 2464.5 5088.5 5389.5
Rv-BNRF1-C       3292.5 3302.5 3044.0 2592.5 3161.5 1875.0 2685.0 4846.5
Rv-BNRF1-N       3346.5 3094.5 2881.5 2673.5 2961.0 1818.5 2371.5 4746.0
mean-Rv-Negative 1374.0 1519.0 1542.0 1213.0 1142.0  726.5 1075.0 2666.5
                    T83     T84    T86    T87     T88    T89    T91     T92
Rv-EBNA3B-E2     3078.0  4636.0 4411.0 6017.0  4938.5 4762.0 4388.5  3692.0
Rv-BLLF1-C       2854.5 26797.0 3943.0 7158.5 17698.5 4134.5 4119.0 17998.5
Rv-BLLF1-N       3843.0  5493.5 4938.5 8341.0  7198.0 5227.0 5434.5  4912.0
Rv-BNRF1-C       3381.5  5068.0 4788.0 6329.0  5184.5 3394.5 5338.0  4499.5
Rv-BNRF1-N       3277.5  4981.5 4648.0 6228.0  4996.5 5307.5 5228.0  4348.5
mean-Rv-Negative 1177.0  1754.0 3433.5 2110.0  1657.0 1570.0 1529.0  1337.0
                    T93    T94    T95    T96    T97    T98   T104  T109
Rv-EBNA3B-E2     2820.0 3659.0 3683.0 3721.0 3434.5 2642.5 4709.5  6686
Rv-BLLF1-C       2498.5 3041.5 2718.5 2718.0 2668.0 3465.5 4574.5 13769
Rv-BLLF1-N       3614.0 3742.5 3826.5 3723.5 3661.5 2979.5 9698.5  6535
Rv-BNRF1-C       3176.0 3866.5 3725.0 3926.5 3707.5 2764.0 4206.0  3725
Rv-BNRF1-N       2904.5 3774.0 3701.0 3769.5 3607.0 2903.0 4129.5  3628
mean-Rv-Negative  913.0 3406.0 3427.5 3137.0 3193.5 2316.5 2172.0  2032
                   T121   T127   T129   T131   T132   T176   T178    T181
Rv-EBNA3B-E2     3530.5 6214.0 6713.0 4857.5 5607.0 2970.0 5150.5  3527.5
Rv-BLLF1-C       2746.5 7520.0 8204.5 4456.5 4504.5 2361.0 9747.0 23913.5
Rv-BLLF1-N       4087.0 7664.0 7922.0 6774.5 6080.0 3054.0 8238.0 33319.0
Rv-BNRF1-C       3175.0 4855.0 5178.5 4479.5 3093.5 2806.5 5366.5  3607.5
Rv-BNRF1-N       3044.5 5336.5 5706.0 4709.5 3094.0 2698.5 4766.5  3449.5
mean-Rv-Negative 1521.0 2123.0 2367.0 1782.5  904.5  974.0 1464.0  1484.5
                   T185   T192    T201   T204   T208   T217   T224   T231
Rv-EBNA3B-E2     3083.0 3328.5  4679.0 2323.5 3940.0 2554.5 4237.0 2954.0
Rv-BLLF1-C       3677.0 2801.5  9132.0 3374.0 3775.0 2189.0 4272.0 3845.0
Rv-BLLF1-N       3803.5 3655.0 13416.0 3553.0 4741.0 2776.5 4692.0 3777.5
Rv-BNRF1-C       3212.5 3545.5  5017.0 2693.5 3900.0 2653.5 3321.0 3495.5
Rv-BNRF1-N       3047.0 3583.0  5113.5 2661.0 3841.5 2527.5 3389.0 3160.0
mean-Rv-Negative 1203.5 1243.0  2685.0  959.0 1696.0 1028.5 1239.5  970.5
                   T293   T295   T301   T302   T304   T305   T311     N1
Rv-EBNA3B-E2     5127.0 3939.5 3184.5 4138.0 3464.5 3837.5 3295.5 2693.0
Rv-BLLF1-C       4425.0 3992.0 3265.0 4420.0 2946.0 3267.0 4455.0 2108.0
Rv-BLLF1-N       4499.5 3888.5 5765.0 3779.0 3197.0 3329.0 4748.0 2604.0
Rv-BNRF1-C       5957.5 5212.5 3727.0 3676.0 3553.0 3792.0 3443.0 2961.0
Rv-BNRF1-N       5735.0 4948.5 3702.5 4306.5 4116.5 4159.5 4174.0 3562.5
mean-Rv-Negative 2152.5 1364.5 1315.0 2633.0 2853.0 4049.5 2235.5  727.5
                     N2     N3     N4     N6     N7     N8     N9    N10
Rv-EBNA3B-E2     2588.0 2536.5 2873.5 3361.5 3085.5 2660.5 2186.0 4052.5
Rv-BLLF1-C       2181.5 1893.5 2485.5 2697.0 2383.5 1982.5 1514.5 2581.0
Rv-BLLF1-N       2710.0 2405.0 2880.0 3132.0 2815.5 2483.5 2091.5 3052.5
Rv-BNRF1-C       3120.0 2825.5 3424.0 3642.5 3365.0 3044.0 2524.0 4314.0
Rv-BNRF1-N       3550.0 3491.0 4202.5 4302.0 4214.5 3820.0 3051.0 4154.5
mean-Rv-Negative  709.0  613.5 1183.5 1162.5  908.5  632.5  576.0  733.0
                  N13    N14    N15    N16    N18    N19    N21    N23
Rv-EBNA3B-E2     4809 4084.0 4777.5 6073.0 5266.5 3913.5 2899.0 2849.0
Rv-BLLF1-C       2871 2222.5 2660.5 2906.5 2941.5 2113.0 1514.5 2244.0
Rv-BLLF1-N       4070 2800.5 3374.0 3857.5 3540.5 2724.0 1986.0 2363.5
Rv-BNRF1-C       4564 3820.0 4691.5 5619.5 4859.5 3595.5 2814.0 2929.5
Rv-BNRF1-N       4927 3952.5 4850.0 5721.5 5098.5 3880.5 3198.5 3398.0
mean-Rv-Negative 1115  675.0  812.0  684.5 1199.5  542.5  399.5 1804.0
                    N24    N25    N26    N27    N28    N30    N31    N32
Rv-EBNA3B-E2     3211.5 2955.5 2669.0 3124.0 3005.0 2008.5 6182.0 5317.0
Rv-BLLF1-C       2111.0 1949.0 2051.0 2481.0 2177.5 1562.0 4084.0 3753.5
Rv-BLLF1-N       2251.5 2217.5 2307.0 2602.5 2430.5 1896.5 4596.0 4367.0
Rv-BNRF1-C       2775.5 2729.5 2709.0 3090.5 2832.5 2207.0 5724.5 5237.5
Rv-BNRF1-N       3300.5 3334.5 3312.5 3620.0 3310.5 2696.5 7970.0 6992.5
mean-Rv-Negative  936.0  808.0 1105.5 1458.0 1337.0  560.5 1378.0  847.0
                    N33    N34    N35    N36    N37    N39    N40    N41
Rv-EBNA3B-E2     6530.5 7054.5 8151.0 6684.5 5813.5 3350.5 3937.5 3989.0
Rv-BLLF1-C       4702.5 4495.5 5144.5 5163.0 4889.0 3079.5 3097.0 3018.0
Rv-BLLF1-N       5402.5 5268.5 5679.0 5762.5 5673.5 3567.0 3442.5 3427.0
Rv-BNRF1-C       6175.0 6381.5 6993.5 6601.5 6385.5 3910.0 3933.5 4044.0
Rv-BNRF1-N       8479.0 8764.0 9317.0 8607.5 7742.0 4914.5 4627.5 4597.5
mean-Rv-Negative 1723.5  980.5 1742.0 1564.0 1719.0  722.0 1824.5 2570.0
                    N42    N43    N46    N47    N48    N50    N52    N54
Rv-EBNA3B-E2     3598.5 2981.0 4557.5 4274.5 4471.0 5711.5 5032.5 6924.0
Rv-BLLF1-C       2733.5 2239.5 3570.5 3352.0 3869.0 4583.0 4620.0 6040.5
Rv-BLLF1-N       3087.5 2564.5 3876.5 3502.5 3933.0 4653.5 4573.5 6217.0
Rv-BNRF1-C       3562.0 2955.5 4694.5 4344.5 4744.5 5738.0 5235.5 6550.5
Rv-BNRF1-N       4745.5 3897.5 6222.5 5824.0 6273.5 7657.5 7143.5 8536.5
mean-Rv-Negative 1436.0 1452.5 2115.0 1504.0 1927.0 2976.5 2080.5 5332.0
                    N55    N57    N58    N59    N61    N62    N63    N64
Rv-EBNA3B-E2     4232.0 2922.5 4683.5 5005.5 4780.0 6269.0 4926.0 4642.5
Rv-BLLF1-C       3759.5 3261.5 4479.0 6075.5 4485.0 4964.0 4721.5 4479.5
Rv-BLLF1-N       4177.0 3554.5 4708.5 4801.5 4716.5 5445.5 5407.0 4819.0
Rv-BNRF1-C       4539.5 3400.5 4984.0 5048.5 4877.5 5712.5 5209.5 5175.5
Rv-BNRF1-N       6322.5 4786.0 6826.0 6738.5 6311.0 7517.0 6748.0 6886.0
mean-Rv-Negative 1736.0  983.0 1580.0 2179.0 1150.0 2388.0 1352.5 1243.0
                    N65    N66    N67    N68    N69    N70    N71    N72
Rv-EBNA3B-E2     4964.5 4118.0 5161.0 5302.5 6019.5 6567.5 6534.0 6885.5
Rv-BLLF1-C       4806.0 3791.0 4502.5 4598.5 5120.5 5681.5 5855.5 5468.5
Rv-BLLF1-N       5335.5 4505.5 5385.0 5255.0 5988.0 6459.5 6499.0 6406.5
Rv-BNRF1-C       5724.0 4336.5 5145.5 5803.0 6133.5 6569.5 6833.0 6114.0
Rv-BNRF1-N       6844.0 5307.5 6407.0 6557.5 7063.0 7811.5 7159.5 7048.5
mean-Rv-Negative 4813.0 1504.0  431.0 1358.0 1861.5 2267.5 2070.5 1377.5
                    N73    N75    N78    N79    N80
Rv-EBNA3B-E2     5426.0 3533.5 4178.0 2299.5 1484.0
Rv-BLLF1-C       5186.0 3134.5 3643.5 2206.5 1702.0
Rv-BLLF1-N       5973.0 3434.0 5121.0 2473.0 1981.5
Rv-BNRF1-C       5976.5 3462.0 4507.5 2801.0 1995.5
Rv-BNRF1-N       6023.5 3749.5 4971.0 2567.0 1910.5
mean-Rv-Negative 1073.0  392.0 1348.0  824.5  546.5

As you can see in my dataset,there is one probe with 149 samples,then how can I do this normalization with "spike-in" probes? Looking for your reply.

Best regards.

Guojie

No, you cannot fit the vsn parameters based on data from one probe only.

I recommend calling vsn in the standard way, and visualising the values of the "mean-Rv-Negative" probes  to see whether there are, e.g. any remaining large trends, or indvidual outliers (i.e. use the probe for quality assessment),.

Best-

Wolfgang

so you recommend I treated the "mean-Rv-Negative" as another protein, say my dataset includes 96 kinds of protein(in fact 95 kinds of proteins expressed from 85 ORFs plus one "no DNA" control) and use justvsn method to transform the all 96 proteins data instead of leave the "no DNA" control out? I'm not sure the vsn method can transform the real probes including the "no DNA" control? I always think the method can only treat the real probes with express proteins. I tried the two ways and used the meanSdPlot() to verify the transformation. Transformation the 95 kinds of probes shows better variance-stabilization than the 96 kinds of probes transformation.

Hope you can enlighten me about this point.

Best regards.

Guojie  

One single row of your matrix min should not much affect the result of justvsn, as the fitting is done using a robust method. Can you post the meanSdPlot outputs for the alternative calls:

   m1 <- justvsn(min)

   m2 <- justvsn(min[ rownames(min) != "mean-Rv-Negative", ])

   k <- some value between 1 and 149

   plot(m1[,k], m2[,k]); abline(a = 0, b = 1, col = "orange")

Thanks again for your great patience. I did as you suggested. Please check it out as below.

>  m1 <- justvsn(min)
vsn2: 96 x 149 matrix (1 stratum). Please use 'meanSdPlot' to verify the fit.
> m2 <- justvsn(min[ rownames(min) != "mean-Rv-Negative", ])
vsn2: 95 x 149 matrix (1 stratum). Please use 'meanSdPlot' to verify the fit.
> k <-2

> c<-exprs(m1)[,k]
> d<-exprs(m2)[,k]

> plot(c[1:95],d);abline(a = 0, b = 1, col = "orange")

The image is here: http://pan.baidu.com/s/1eQDDfNk

when k=149, the plot is like this: http://pan.baidu.com/s/1o6BuWf4

> meanSdPlot(m1)

The image is here: http://pan.baidu.com/s/1nty5Klb

> meanSdPlot(m2)

The image is here: http://pan.baidu.com/s/1bne7xtd

> meanSdPlot(m1[1:95])

The image is here: http://pan.baidu.com/s/1gdGsQt1

Does it suggest m1 and m2 are not so much different? I can't tell whether the difference between the m1 and m2 is big or not. 

Best regards.