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Question: Using Limma for proteomics (2D DIGE) datasets
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gravatar for Tom Wenseleers
8.1 years ago by
Tom Wenseleers60 wrote:
Dear all, I am interested in using Limma/LimmaGUI for the analysis of proteomics (2D DIGE) datasets. I have had a try with LimmaGUI, however, I seem to keep on getting the message "limmaGUI was unable to read in the gene list from the raw (image analysis) files." (although the Cy3 and Cy5 data are read in fine) - any idea what I could be doing wrong? Main thing is I don't know how I should call the gene (well protein) list column... I attach a couple of the .spot files and the targets file for your info. I chose File...New...selected my targets file, then Type of Image processing file...Other Red foreground: Cy5, Red background: Cy5_b (I just put in zeros - hope that is OK?), Green foreground: Cy3, Green background: Cy3_b. I have the latest Windows version of R and all the bioconductor packages installed. (Incidentally, during installation it complained about the sma package not being there - that appears to be no longer supported - but is still used in e.g. Limma, so this I guess needs sorting out - I installed an older archived version). In proteomics we also have the Cy2 channel which is an internal control based on a pooled sample that is identical for all gels - but I think I do not have to use that since I am interested in the Cy3/Cy5 Log ratios and (Cy3/Cy2) / (Cy5/Cy2)=Cy3/Cy5, ie I think the Cy2 channel would only introduce additional noise. Anyway if any of you would have experience in using Limma for the analysis of proteomics datasets, please let me know... Right now in the proteomics community people are mostly using simple t-tests etc, but using moderated t statistics would obviously be much better... Maybe there would be some scope for writing a dedicated Bioconductor package for the analysis of proteomics 2D DIGE data, based on Limma or LimmaGUI, I don't know... I think most of the code would hardly need any changing, only the input of the data would need changing and maybe dealing with missing values could be better too (which is more of an issue in proteomics than in microarrays) (eg using a few preprocessing and analysis options, such as to leave them out, substitute by 0 or impute missing values using k nearest neighbours). I think this could greatly benefit the proteomics community. cheers, Tom Wenseleers Dr. T. Wenseleers Dept. of Biology Zoological Institute K.U.Leuven Naamsestraat 59 B-3000 Leuven Belgium tel. +32 (0)16 32 39 64 mobile +32 (0)472 40 45 96 e-mail tom.wenseleers at bio.kuleuven.be web http://bio.kuleuven.be/ento/wenseleers/twenseleers.htm -------------- next part -------------- SlideNumber Name FileName Cy3 Cy5 1 56_g1 brain_56_g1.spot V O 2 56_g2 brain_56_g2.spot V O -------------- next part -------------- row.names genes Cy3 Cy5 Cy3_b Cy5_b 1 p1 0 0 2 p2 0 0 3 p3 0 0 4 p4 0 0 5 p5 0 0 8 p8 0 0 10 p10 0 0 11 p11 0 0 12 p12 0 0 13 p13 0 0 16 p16 0 0 18 p18 0 0 19 p19 0 0 22 p22 0 0 23 p23 0 0 24 p24 0 0 25 p25 0 0 26 p26 0 0 27 p27 0 0 28 p28 0 0 29 p29 0 0 30 p30 0 0 33 p33 0 0 34 p34 0 0 36 p36 0 0 38 p38 0 0 39 p39 0 0 40 p40 0 0 41 p41 0 0 42 p42 0 0 43 p43 0 0 45 p45 0 0 46 p46 0 0 47 p47 0 0 48 p48 0 0 51 p51 0 0 53 p53 0 0 55 p55 0 0 57 p57 0 0 58 p58 0 0 59 p59 0 0 60 p60 0 0 61 p61 0 0 62 p62 0 0 63 p63 0 0 64 p64 0 0 66 p66 0 0 67 p67 0 0 68 p68 0 0 69 p69 0 0 70 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480933 0 0 2876 p2876 0 0 2877 p2877 0 0 2878 p2878 0 0 2879 p2879 0 0 2880 p2880 0 0 2881 p2881 0 0 2882 p2882 0 0 2883 p2883 66628 76797 0 0 2884 p2884 0 0 2885 p2885 677320 716371 0 0 2886 p2886 0 0 2887 p2887 576973 907372 0 0 2888 p2888 0 0 2889 p2889 16794 3482 0 0 2890 p2890 45165 42190 0 0 2891 p2891 0 0 2892 p2892 24243 25501 0 0 2893 p2893 10247 2704 0 0 2894 p2894 0 0 2895 p2895 22892 9567 0 0 2896 p2896 102900 94918 0 0 2897 p2897 49450 22820 0 0 2898 p2898 9163 1734 0 0 2899 p2899 591782 586125 0 0 2900 p2900 69730 72938 0 0 2901 p2901 0 0 2902 p2902 21009 10779 0 0 2903 p2903 0 0 2904 p2904 0 0 2905 p2905 0 0 2906 p2906 11431 2608 0 0 2907 p2907 7612 1028 0 0 2908 p2908 376408 404253 0 0 2909 p2909 41857 50106 0 0 2910 p2910 168234 163929 0 0 2911 p2911 23459 46007 0 0 2912 p2912 0 0 2913 p2913 0 0 2914 p2914 76153 61548 0 0 2915 p2915 219687 183194 0 0 2916 p2916 13931 4097 0 0 2917 p2917 28806 27058 0 0 2918 p2918 0 0 2919 p2919 83586 120411 0 0 2920 p2920 21421 27184 0 0 2921 p2921 28577 26392 0 0 2922 p2922 0 0 2923 p2923 0 0 2924 p2924 104530 171736 0 0 2925 p2925 1124809 1707078 0 0 2926 p2926 0 0 2927 p2927 0 0 2928 p2928 541756 533909 0 0 2929 p2929 23215 21179 0 0 2930 p2930 0 0 2931 p2931 0 0 2932 p2932 0 0 2933 p2933 0 0 2934 p2934 31832 52988 0 0 2935 p2935 19921 3546 0 0 2936 p2936 0 0 2937 p2937 0 0 2938 p2938 29580 67243 0 0 2939 p2939 280448 502249 0 0 2940 p2940 0 0 2941 p2941 264569 287757 0 0 2942 p2942 2870905 5431844 0 0 2943 p2943 19680 19139 0 0 2944 p2944 0 0 2945 p2945 0 0 2946 p2946 0 0 2947 p2947 0 0 2949 p2949 0 0 2950 p2950 68664 73191 0 0 2951 p2951 107600 121738 0 0 2952 p2952 0 0 2953 p2953 72324 94373 0 0 2954 p2954 0 0 2955 p2955 11318 3109 0 0 2956 p2956 0 0 2957 p2957 0 0 2958 p2958 0 0 2959 p2959 618386 718605 0 0 2960 p2960 11290 2501 0 0 2961 p2961 31630 36312 0 0 2962 p2962 0 0 2963 p2963 24952 26787 0 0 2964 p2964 0 0 2965 p2965 0 0 2966 p2966 48218 50073 0 0 2967 p2967 0 0 2968 p2968 0 0 2969 p2969 867026 827662 0 0 2970 p2970 0 0 2971 p2971 195524 158471 0 0 2972 p2972 0 0 2973 p2973 0 0 2974 p2974 0 0 2975 p2975 24971 21529 0 0 2976 p2976 0 0 2977 p2977 0 0 2978 p2978 29503 29566 0 0 2979 p2979 0 0 2980 p2980 41125 8003 0 0 2981 p2981 0 0 2982 p2982 0 0 2983 p2983 0 0 2984 p2984 0 0 2985 p2985 34299 30468 0 0 2986 p2986 0 0 2987 p2987 9353 2848 0 0 2988 p2988 0 0 2989 p2989 67832 41978 0 0 2990 p2990 81936 82181 0 0 2991 p2991 63073 62080 0 0 2992 p2992 0 0 2993 p2993 0 0 2994 p2994 1587420 1268834 0 0 2995 p2995 98246 91288 0 0 2996 p2996 42988 44990 0 0 2997 p2997 0 0 2998 p2998 33321 17112 0 0 2999 p2999 0 0 3000 p3000 0 0 3001 p3001 0 0 3002 p3002 737525 756315 0 0 3003 p3003 0 0 3004 p3004 55975 56761 0 0 3005 p3005 137653 113154 0 0 3006 p3006 0 0 3007 p3007 34803 13978 0 0 3008 p3008 241401 267818 0 0 3009 p3009 0 0 3010 p3010 0 0 3011 p3011 0 0 3012 p3012 99160 120573 0 0 3013 p3013 267971 297309 0 0 3014 p3014 440891 513574 0 0 3015 p3015 39579 32088 0 0 3016 p3016 608149 706453 0 0 3017 p3017 0 0 3018 p3018 32866 20333 0 0 3019 p3019 12333 2624 0 0 3020 p3020 10013 2345 0 0 3021 p3021 52637 73148 0 0 3022 p3022 0 0 3023 p3023 37269 29983 0 0 3024 p3024 0 0 3025 p3025 0 0 3026 p3026 16754 24338 0 0 3027 p3027 0 0 3028 p3028 287336 333381 0 0 3029 p3029 0 0 3030 p3030 0 0 3031 p3031 0 0 3032 p3032 0 0 3033 p3033 0 0 3034 p3034 0 0 3035 p3035 17995 17542 0 0 3036 p3036 89122 40012 0 0 3037 p3037 9463 2909 0 0 3038 p3038 145385 69502 0 0 3039 p3039 35933 26396 0 0 3040 p3040 81529 99626 0 0 3041 p3041 12249 15521 0 0 3042 p3042 0 0 3043 p3043 0 0 3044 p3044 0 0 3045 p3045 0 0 3046 p3046 55366 48765 0 0 3047 p3047 0 0 3048 p3048 0 0 3049 p3049 0 0 3050 p3050 453576 386357 0 0 3051 p3051 0 0 3052 p3052 0 0 3053 p3053 45686 57232 0 0 3054 p3054 0 0 3055 p3055 404843 484560 0 0 3056 p3056 0 0 3057 p3057 0 0 3058 p3058 0 0 3059 p3059 0 0 3060 p3060 33007 8256 0 0 3061 p3061 0 0 3062 p3062 0 0 3063 p3063 20020 18883 0 0 3064 p3064 0 0 3065 p3065 0 0 3066 p3066 0 0 3067 p3067 0 0 3068 p3068 91148 43769 0 0 3069 p3069 110660 52797 0 0 3070 p3070 0 0 3071 p3071 54384 53164 0 0 3072 p3072 0 0 3073 p3073 364217 365207 0 0 3074 p3074 43934 39322 0 0 3075 p3075 113789 120506 0 0 3076 p3076 5804 2706 0 0 3077 p3077 0 0 3078 p3078 0 0 3079 p3079 0 0 3080 p3080 39913 33435 0 0 3081 p3081 110813 46816 0 0 3082 p3082 0 0 3083 p3083 0 0 3084 p3084 409484 430652 0 0 3085 p3085 141457 210513 0 0 3086 p3086 23780 32139 0 0 3087 p3087 0 0 3088 p3088 0 0 3089 p3089 34870 25167 0 0 3090 p3090 0 0 3091 p3091 0 0 3092 p3092 87769 105709 0 0 3093 p3093 23660 4474 0 0 3094 p3094 0 0 3095 p3095 126118 115474 0 0 3096 p3096 0 0 3097 p3097 10155 3442 0 0 3098 p3098 81708 60814 0 0 3099 p3099 48353 30014 0 0 3100 p3100 0 0 3101 p3101 22642 28106 0 0 3102 p3102 0 0 3103 p3103 0 0 3104 p3104 29614 35683 0 0 3105 p3105 0 0 3106 p3106 0 0 3107 p3107 60029 70754 0 0 3108 p3108 0 0 3109 p3109 7676 2483 0 0 3110 p3110 0 0 3111 p3111 19456 4986 0 0 3112 p3112 230412 259437 0 0 3113 p3113 26734 20861 0 0 3114 p3114 0 0 3115 p3115 103072 8011 0 0 3116 p3116 0 0 3117 p3117 84143 75359 0 0 3118 p3118 0 0 3119 p3119 53630 34013 0 0 3120 p3120 81870 110880 0 0 3121 p3121 0 0 3122 p3122 0 0 3123 p3123 46837 43844 0 0 3124 p3124 0 0 3125 p3125 0 0 3126 p3126 35596 38813 0 0 3127 p3127 0 0 3128 p3128 0 0 3129 p3129 0 0 3130 p3130 3838577 3942707 0 0 3131 p3131 159473 165635 0 0 3132 p3132 0 0 3133 p3133 0 0 3134 p3134 0 0 3135 p3135 0 0 3136 p3136 0 0 3137 p3137 0 0 3138 p3138 0 0 3139 p3139 9271 2469 0 0 3140 p3140 91352 87805 0 0 3141 p3141 0 0 3142 p3142 96330 53545 0 0 3143 p3143 0 0 3144 p3144 0 0 3145 p3145 0 0 3146 p3146 0 0 3147 p3147 0 0 3148 p3148 0 0 3149 p3149 0 0 3150 p3150 0 0 3151 p3151 0 0 3152 p3152 0 0 3153 p3153 0 0 3154 p3154 0 0 3155 p3155 0 0 3156 p3156 0 0 3157 p3157 80408 54837 0 0 3158 p3158 0 0 3159 p3159 0 0 3160 p3160 0 0 3161 p3161 0 0 3162 p3162 0 0 3163 p3163 0 0 3164 p3164 0 0 3165 p3165 0 0 3166 p3166 0 0 3167 p3167 0 0 3168 p3168 0 0 3169 p3169 54778 28347 0 0 3170 p3170 0 0 3171 p3171 0 0 3172 p3172 0 0 3173 p3173 0 0 3174 p3174 0 0 3175 p3175 0 0 3176 p3176 0 0 3177 p3177 0 0 3178 p3178 0 0 3179 p3179 0 0 3180 p3180 0 0 3181 p3181 0 0 3182 p3182 0 0 3183 p3183 0 0 3184 p3184 0 0 3185 p3185 0 0 3186 p3186 0 0 3187 p3187 0 0 3188 p3188 0 0 3189 p3189 0 0 3190 p3190 0 0 3191 p3191 0 0 3192 p3192 0 0 3193 p3193 0 0 3194 p3194 0 0 3195 p3195 0 0 3196 p3196 0 0 3197 p3197 0 0 3199 p3199 0 0 3200 p3200 0 0 3201 p3201 0 0 3202 p3202 0 0 3203 p3203 0 0 3204 p3204 0 0 3205 p3205 0 0 3206 p3206 0 0 3207 p3207 0 0 3208 p3208 0 0 3209 p3209 0 0 3210 p3210 0 0 3211 p3211 0 0 3212 p3212 0 0 3213 p3213 0 0 3214 p3214 0 0 3215 p3215 0 0 3216 p3216 0 0 3217 p3217 0 0 3218 p3218 0 0 3219 p3219 0 0 3220 p3220 0 0 3221 p3221 0 0 3222 p3222 0 0 3223 p3223 0 0 3224 p3224 0 0 3225 p3225 0 0 3226 p3226 0 0 3227 p3227 0 0 3228 p3228 0 0 3229 p3229 0 0 3230 p3230 0 0 3231 p3231 0 0 3232 p3232 0 0 3233 p3233 0 0 3234 p3234 0 0 3235 p3235 0 0 3236 p3236 0 0 3237 p3237 0 0 3238 p3238 0 0 3239 p3239 0 0 3241 p3241 0 0 3243 p3243 0 0 3244 p3244 0 0 3245 p3245 0 0 3246 p3246 0 0 3247 p3247 0 0 3249 p3249 0 0 3250 p3250 0 0 3251 p3251 0 0 3252 p3252 0 0 3253 p3253 0 0 3254 p3254 0 0 3255 p3255 0 0 3256 p3256 0 0 3258 p3258 0 0 3259 p3259 0 0 3260 p3260 0 0 3261 p3261 0 0 3262 p3262 0 0 3264 p3264 0 0 3265 p3265 0 0 3266 p3266 0 0 3267 p3267 0 0 3268 p3268 0 0 3269 p3269 0 0 3270 p3270 0 0 3271 p3271 0 0 3272 p3272 0 0 3273 p3273 0 0 3274 p3274 0 0 3275 p3275 0 0 3276 p3276 0 0 3278 p3278 0 0 3279 p3279 0 0 3280 p3280 0 0 3281 p3281 0 0 3282 p3282 0 0 3283 p3283 0 0 3285 p3285 0 0 3286 p3286 0 0 3287 p3287 0 0 3288 p3288 0 0 3289 p3289 0 0 3290 p3290 0 0 3291 p3291 0 0 3292 p3292 0 0 3293 p3293 0 0 3294 p3294 0 0 3295 p3295 0 0 3296 p3296 0 0 3297 p3297 0 0 3299 p3299 0 0 3300 p3300 0 0 3301 p3301 0 0 3302 p3302 0 0 3303 p3303 0 0 3305 p3305 0 0 3306 p3306 0 0 3307 p3307 0 0 3308 p3308 0 0 3309 p3309 0 0 3310 p3310 0 0 3311 p3311 0 0 3312 p3312 0 0 3313 p3313 0 0 3314 p3314 0 0 3315 p3315 0 0 3316 p3316 0 0 3317 p3317 0 0 3318 p3318 0 0 3319 p3319 0 0 3320 p3320 0 0 3321 p3321 0 0 3322 p3322 0 0 3323 p3323 0 0 3325 p3325 0 0 3326 p3326 0 0 3327 p3327 0 0 3329 p3329 0 0 3330 p3330 0 0 3331 p3331 0 0 3332 p3332 0 0 3333 p3333 0 0 3334 p3334 0 0 3335 p3335 0 0 3336 p3336 0 0 3337 p3337 0 0 3338 p3338 0 0 3339 p3339 0 0 3340 p3340 0 0 3341 p3341 0 0 3342 p3342 0 0 3344 p3344 0 0 3345 p3345 0 0 3346 p3346 0 0 3347 p3347 0 0 3348 p3348 0 0 3349 p3349 0 0 3350 p3350 0 0 3352 p3352 0 0 3353 p3353 0 0 3355 p3355 0 0 3357 p3357 0 0 3358 p3358 0 0 3359 p3359 0 0 3360 p3360 0 0 3361 p3361 0 0 3362 p3362 0 0 3363 p3363 0 0 3364 p3364 0 0 3365 p3365 0 0 3366 p3366 0 0 3367 p3367 0 0 3368 p3368 0 0 3371 p3371 0 0 3373 p3373 0 0 3374 p3374 0 0 3375 p3375 0 0 3376 p3376 0 0 3377 p3377 0 0 3378 p3378 0 0 3379 p3379 0 0 3380 p3380 0 0 3381 p3381 0 0 3384 p3384 0 0 -------------- next part -------------- row.names genes Cy3 Cy5 Cy3_b Cy5_b 1 p1 0 0 2 p2 0 0 3 p3 0 0 4 p4 0 0 5 p5 0 0 8 p8 0 0 10 p10 0 0 11 p11 0 0 12 p12 0 0 13 p13 0 0 16 p16 0 0 18 p18 0 0 19 p19 0 0 22 p22 0 0 23 p23 0 0 24 p24 0 0 25 p25 0 0 26 p26 0 0 27 p27 0 0 28 p28 0 0 29 p29 0 0 30 p30 0 0 33 p33 0 0 34 p34 0 0 36 p36 0 0 38 p38 0 0 39 p39 0 0 40 p40 0 0 41 p41 0 0 42 p42 0 0 43 p43 0 0 45 p45 0 0 46 p46 0 0 47 p47 0 0 48 p48 0 0 51 p51 0 0 53 p53 0 0 55 p55 0 0 57 p57 0 0 58 p58 0 0 59 p59 0 0 60 p60 0 0 61 p61 0 0 62 p62 0 0 63 p63 0 0 64 p64 0 0 66 p66 0 0 67 p67 0 0 68 p68 0 0 69 p69 0 0 70 p70 0 0 71 p71 0 0 72 p72 0 0 73 p73 0 0 74 p74 0 0 75 p75 0 0 77 p77 0 0 78 p78 0 0 79 p79 0 0 80 p80 0 0 81 p81 0 0 82 p82 0 0 83 p83 0 0 84 p84 0 0 85 p85 0 0 86 p86 0 0 87 p87 0 0 88 p88 0 0 89 p89 0 0 90 p90 0 0 91 p91 0 0 92 p92 0 0 93 p93 0 0 94 p94 0 0 95 p95 0 0 96 p96 0 0 97 p97 0 0 98 p98 0 0 99 p99 0 0 100 p100 0 0 101 p101 0 0 103 p103 0 0 105 p105 0 0 107 p107 0 0 108 p108 0 0 110 p110 0 0 111 p111 0 0 112 p112 0 0 113 p113 0 0 115 p115 0 0 116 p116 0 0 119 p119 0 0 120 p120 0 0 122 p122 0 0 123 p123 0 0 124 p124 0 0 125 p125 0 0 126 p126 0 0 127 p127 0 0 128 p128 0 0 129 p129 0 0 130 p130 0 0 131 p131 0 0 132 p132 0 0 133 p133 0 0 134 p134 0 0 135 p135 0 0 136 p136 0 0 137 p137 0 0 138 p138 0 0 139 p139 0 0 140 p140 0 0 141 p141 0 0 144 p144 0 0 145 p145 0 0 146 p146 0 0 147 p147 0 0 148 p148 0 0 149 p149 0 0 150 p150 0 0 151 p151 0 0 155 p155 0 0 156 p156 0 0 157 p157 0 0 158 p158 0 0 159 p159 0 0 160 p160 0 0 161 p161 0 0 162 p162 2670743 2628705 0 0 163 p163 0 0 164 p164 2651499 2644432 0 0 165 p165 0 0 166 p166 0 0 167 p167 0 0 168 p168 2993256 3093478 0 0 169 p169 0 0 170 p170 0 0 171 p171 6710284 7683330 0 0 172 p172 556561 763610 0 0 173 p173 0 0 174 p174 0 0 175 p175 0 0 176 p176 161905 322296 0 0 177 p177 0 0 178 p178 0 0 179 p179 0 0 180 p180 0 0 181 p181 1913843 1989633 0 0 182 p182 0 0 183 p183 372753 442902 0 0 184 p184 0 0 185 p185 0 0 186 p186 0 0 187 p187 0 0 188 p188 0 0 189 p189 111086 129243 0 0 190 p190 0 0 191 p191 0 0 192 p192 0 0 193 p193 0 0 194 p194 0 0 195 p195 0 0 196 p196 0 0 197 p197 0 0 198 p198 0 0 199 p199 0 0 200 p200 0 0 201 p201 0 0 202 p202 0 0 203 p203 204067 301380 0 0 204 p204 0 0 205 p205 0 0 208 p208 0 0 209 p209 0 0 210 p210 0 0 211 p211 0 0 212 p212 0 0 213 p213 7773953 7169643 0 0 214 p214 0 0 215 p215 787728 2253628 0 0 216 p216 0 0 217 p217 480412 2092465 0 0 218 p218 104112 185095 0 0 219 p219 384279 519399 0 0 221 p221 0 0 222 p222 0 0 223 p223 425519 2248704 0 0 224 p224 0 0 225 p225 764341 900570 0 0 226 p226 517197 597459 0 0 227 p227 0 0 228 p228 100568 93619 0 0 229 p229 395012 412422 0 0 230 p230 2867833 2959790 0 0 231 p231 5949873 5674696 0 0 232 p232 0 0 233 p233 228132 1437239 0 0 234 p234 273049 399913 0 0 235 p235 0 0 236 p236 0 0 237 p237 0 0 238 p238 176103 844044 0 0 239 p239 132946 181857 0 0 240 p240 0 0 241 p241 2470259 2458534 0 0 242 p242 57925 92736 0 0 243 p243 38030 60136 0 0 244 p244 137146 390093 0 0 245 p245 140378 351013 0 0 246 p246 132974 330437 0 0 247 p247 24286 51472 0 0 248 p248 159737 197194 0 0 249 p249 0 0 250 p250 94496 108714 0 0 251 p251 0 0 252 p252 0 0 253 p253 0 0 254 p254 0 0 255 p255 88639 87521 0 0 256 p256 237813 318026 0 0 257 p257 97344 127661 0 0 258 p258 259595 317965 0 0 259 p259 213552 210890 0 0 260 p260 69063 60836 0 0 261 p261 37033 47176 0 0 262 p262 241210 316947 0 0 263 p263 0 0 264 p264 42029 40059 0 0 265 p265 726324 711992 0 0 266 p266 0 0 267 p267 1023341 966497 0 0 268 p268 0 0 269 p269 0 0 270 p270 0 0 271 p271 198992 238483 0 0 272 p272 0 0 273 p273 235445 292864 0 0 274 p274 172167 175915 0 0 275 p275 281727 312341 0 0 276 p276 173005 193393 0 0 277 p277 548854 600556 0 0 278 p278 945242 1030407 0 0 279 p279 141222 169574 0 0 280 p280 3640022 3875897 0 0 281 p281 2208517 2093379 0 0 282 p282 767856 778200 0 0 283 p283 291119 329659 0 0 284 p284 137600 214490 0 0 285 p285 48046 68808 0 0 286 p286 0 0 287 p287 3290943 3825960 0 0 288 p288 198853 153334 0 0 289 p289 0 0 290 p290 0 0 291 p291 57143 62633 0 0 292 p292 110645 98824 0 0 293 p293 0 0 294 p294 155304 156103 0 0 295 p295 169498 185393 0 0 296 p296 176831 253111 0 0 297 p297 0 0 298 p298 95966 77858 0 0 299 p299 75848 112531 0 0 300 p300 453711 534820 0 0 301 p301 33093 37681 0 0 302 p302 0 0 303 p303 0 0 304 p304 0 0 305 p305 98249 110757 0 0 306 p306 151045 145634 0 0 307 p307 0 0 308 p308 176297 199862 0 0 309 p309 207195 227262 0 0 310 p310 0 0 311 p311 0 0 312 p312 0 0 313 p313 411251 579759 0 0 314 p314 35154 41066 0 0 315 p315 101603 96883 0 0 316 p316 1618641 1639918 0 0 317 p317 0 0 318 p318 73729 77178 0 0 319 p319 132459 156093 0 0 320 p320 6503881 5502512 0 0 321 p321 0 0 322 p322 0 0 323 p323 0 0 324 p324 1590709 1681337 0 0 325 p325 3016948 2831206 0 0 326 p326 2902962 3276979 0 0 327 p327 3192073 2912085 0 0 328 p328 0 0 329 p329 84329 91022 0 0 330 p330 0 0 331 p331 0 0 332 p332 0 0 333 p333 0 0 334 p334 0 0 335 p335 202413 197254 0 0 336 p336 120627 136986 0 0 337 p337 0 0 338 p338 405100 464714 0 0 339 p339 669682 633971 0 0 340 p340 0 0 341 p341 74632 82157 0 0 342 p342 130517 201455 0 0 343 p343 0 0 344 p344 421463 464496 0 0 345 p345 84009 77840 0 0 346 p346 0 0 347 p347 242906 248972 0 0 348 p348 123480 102613 0 0 349 p349 0 0 350 p350 365450 405009 0 0 351 p351 309667 328946 0 0 352 p352 0 0 353 p353 790037 922187 0 0 354 p354 0 0 355 p355 503764 496706 0 0 356 p356 249205 259697 0 0 357 p357 161173 167229 0 0 358 p358 0 0 359 p359 265618 290845 0 0 360 p360 292280 317651 0 0 361 p361 0 0 362 p362 264855 256294 0 0 363 p363 157564 156165 0 0 364 p364 441712 493912 0 0 365 p365 106342 122456 0 0 366 p366 73557 79890 0 0 367 p367 138276 146776 0 0 368 p368 75678 90711 0 0 369 p369 374923 393718 0 0 370 p370 706505 763873 0 0 371 p371 0 0 372 p372 168891 150283 0 0 373 p373 0 0 374 p374 68085 75052 0 0 375 p375 129739 145592 0 0 376 p376 70096 73613 0 0 377 p377 113140 119507 0 0 378 p378 556871 609384 0 0 379 p379 7065766 6304669 0 0 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ADD COMMENTlink modified 2.4 years ago by sm0 • written 8.1 years ago by Tom Wenseleers60
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8.1 years ago by
Australia
Keith Satterley450 wrote:

Hi Tom,

I have checked limmaGUI and I'm not sure why it is not reading your data files correctly when you use the Other format option. I shall look into that later. However, I suggest, as Elmer and Tobias do, that you read the limma Users Guide and use limma at the command line. I have read your data files that you attached using the code below. You should be able to proceed on from there, though I will leave it to others to advise on the statistical approach, as I'm a programmer rather than a statistician.

 >library(limma)
 >setwd("wherever you stored your data")
 >targets <- readTargets("Brain_kol56_targets2.txt")
 >RG <- read.maimages(columns=list(R="Cy3",Rb="Cy3_b",G="Cy5",Gb="Cy5_b"),ext="spot")

and looking at your RGlist I get:

>RG
An object of class "RGList"
$R
brain_56_g1 brain_56_g2
[1,] NA NA
[2,] NA NA
[3,] NA NA
[4,] NA NA
[5,] NA NA
3379 more rows ...

$Rb
brain_56_g1 brain_56_g2
[1,] 0 0
[2,] 0 0
[3,] 0 0
[4,] 0 0
[5,] 0 0
3379 more rows ...

$G
brain_56_g1 brain_56_g2
[1,] NA NA
[2,] NA NA
[3,] NA NA
[4,] NA NA
[5,] NA NA
3379 more rows ...

$Gb
brain_56_g1 brain_56_g2
[1,] 0 0
[2,] 0 0
[3,] 0 0
[4,] 0 0
[5,] 0 0
3379 more rows ...

$targets
FileName
brain_56_g1 brain_56_g1
brain_56_g2 brain_56_g2

$source
[1] "generic"

cheers,

Keith

========================
Keith Satterley
Bioinformatics Division
The Walter and Eliza Hall Institute of Medical Research
Parkville, Melbourne,
Victoria, Australia
=======================

ADD COMMENTlink modified 2.4 years ago by Gordon Smyth32k • written 8.1 years ago by Keith Satterley450
0
gravatar for Elmer Fernández
8.1 years ago by
Elmer Fernández80 wrote:

Hi Tom

Yes, you can use limma by hand. You should build the data matrix by hand and also the design matrix. I already did it and it works fine. The use of Cy2 is highly controversial, some people sais that it add noise but most of the use the Cy2 information, I recommend to do different approaches and use a consensus between all of them.

Above you will find some good references. The first one uses limma

Bes regard
Elmer

Kultima K, Scholz B. Alm H. Sköld K. Svensson M. Crossman AR. Bezard E. Andrén PE. Lönnstedt I. : (2006) Normalization and expression changes in predefined sets of proteins using 2D gel electrophoresis: A proteomic
study of L-DOPA induced dyskinesia in an animal model of Parkinson?s disease using DIGE. BMC Bioinformatics 7:475

*Improving 2D-DIGE protein expression analysis by two-stage linear mixed models: Assessing experimental effects in a melanoma cell study** *Fernández Elmer A, Girotti María R., López Juan A, Llera Andrea S., Podhajcer Osvaldo
L, Cantet Rodolfo J. C. and Balzarini Mónica* **Bioinformatics**
*Advance Access published on September 25, 2008;
doi:10.1093/bioinformatics/btn508* [http://www.ncbi.nlm.nih.gov/pubmed/18818217?ordinalpos=1&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum]*

--
Elmer A. Fernández (Bioing. PhD)
Investigador Asistente CONICET - Research Assistant CONICET
Porf. Inteligencia Artificial -UCC - Prof. Artificial Intelligence @
UCC
tel: +54-(0)351-4938000 int 145
Fax: +54-(0)351-4938081
web page : http://www.uccor.edu.ar/modelo.php?param=3.8.5.15
mail address: Camino Alta Gracia Km 7.1/2- Córdoba-5000-Argentina

ADD COMMENTlink modified 2.4 years ago by Gordon Smyth32k • written 8.1 years ago by Elmer Fernández80
0
gravatar for Tobias Straub
8.1 years ago by
Tobias Straub410
Tobias Straub410 wrote:

hi tom,

you can easily call limma without having to construct complex objects such as MAList or alike
have a look at ?lmFit

if you are able to construct a matrix of either cy3/cy5 ratios or simply the individual channels you are on the right way. if you set the rownames of your matrix to your protein names you will even get back a meaningful output from topTable.

another alternative would be to use a wrapper around lmFit such as provided in the 'st' package. advantage here is that you can easily switch to other t statistics such as studentt, efront, sam etc.

why you think that moderated t is 'better' than student t. any evidences?

best
T.

----------------------------------------------------------------------
Tobias Straub   ++4989218075439   Adolf-Butenandt-Institute, Munchen D

ADD COMMENTlink modified 2.4 years ago by Gordon Smyth32k • written 8.1 years ago by Tobias Straub410
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