The purpose of this package is to detect meso-scale changes in chromatin conformation from 3C data. Typical 3C data might look like the following:
| chr | start | end | EScells_1 |
|---|---|---|---|
| chr2 | 13505797 | 13506141 | 2 |
| chr2 | 13506144 | 13506556 | 2 |
| chr2 | 13654334 | 13655871 | 14 |
| … | … | … | … |
| chr2 | 105656443 | 105656693 | 241 |
| chr2 | 105656696 | 105659412 | 263 |
| chr2 | 105659415 | 105660479 | 126 |
| chr2 | 105662321 | 105663389 | 275 |
| chr2 | 105663392 | 105663974 | 615 |
| … | … | … | … |
| chr2 | 173656857 | 173657083 | 2 |
| chr2 | 173694707 | 173695349 | 2 |
| chr2 | 173698231 | 173698911 | 4 |
The segments shown are restriction enzyme digestion fragments. The counts show the numbers of experimentally captured interactions captured interactions between each of these fragments and the Paupar viewpoint. This viewpoint lies in the region 105661048 -105661864 and we see higher numbers of captures for digestion fragments near the viewpoint. Packages like r3Cseq attempt to determine p-values for the numbers of captures for each fragment. In order to do this, it estimates a ‘background’ level of interaction as a function of distance from the viewpoint. Comparing the count for each fragment to this background level allows it to estimate significance for each fragment.
This method makes only limited use of positional information. While it considers distance from the viewpoint in estimating background levels it ignores the proximity of individual fragments. But suppose we have ten consecutive fragments, each with counts only slightly above background. None may rise to statistical significance on its own, but their co-location may make the group highly significant. We will exploit this observation to look for statistically significant changes in counts between treatments or cell types.
Consider 3C data for the same viewpoint for two replicates each for two different cell types.
| chr | start | end | EScells_1 | EScells_2 | Neu_1 | Neu_2 |
|---|---|---|---|---|---|---|
| chr2 | 6226506 | 6226673 | 0 | 2 | 0 | 0 |
| chr2 | 6235906 | 6237082 | 0 | 0 | 0 | 1 |
| chr2 | 6270043 | 6270850 | 1 | 0 | 0 | 0 |
| … | … | … | … | … | … | … |
| chr2 | 105656443 | 105656693 | 241 | 120 | 184 | 82 |
| chr2 | 105656696 | 105659412 | 263 | 215 | 365 | 225 |
| chr2 | 105659415 | 105660479 | 126 | 182 | 220 | 160 |
| chr2 | 105662321 | 105663389 | 275 | 90 | 171 | 133 |
| chr2 | 105663392 | 105663974 | 615 | 166 | 327 | 301 |
| … | … | … | … | … | … | … |
| chr2 | 179455636 | 179455885 | 0 | 0 | 0 | 1 |
| chr2 | 179473020 | 179473517 | 0 | 0 | 0 | 3 |
| chr2 | 179473520 | 179473584 | 0 | 0 | 0 | 3 |
We wish to discover regions where there is a statisitcally significant change in the interaction levels between the two cell types.
The first task is to normalize the values for each of the cell types. This will allow us to find the mean count for each of the digestion fragments in each of the cell types and consequently find the difference in the mean normalized counts for the two cell types. Normalizing depends on estimating a size factor for each replicate. Here we are relying on DESeq2::estimateSizeFactorsForMatrix(). For further details see the DESeq2 vignette or Love, M.I., Huber, W., Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biology 15(12):550 (2014)
We have included a miniature version of the summarized experiment miniSE in order to demonstrate the process of extracting the delta mean normalized value miniDeltaSE.
{r}
library(deltaCaptureC)
miniDeltaSE = getDeltaSE(miniSE)The actual deltaSE comes from a SummarizedExperiment representing the complete data set for these four replicates and is subsequently trimmed to a region on chromosome2 surrounding our region of interest. After binning to a bin size of 1kb it looks like this:
#> $data
#> start end width delta ctr
#> 1 105161001 105162000 900 -2.43699776 105161500
#> 2 105162001 105163000 900 3.57383506 105162500
#> 3 105163001 105164000 900 9.91840563 105163500
#> 4 105164001 105165000 900 1.02742901 105164500
#> 5 105165001 105166000 900 1.29687709 105165500
#> 6 105166001 105167000 900 4.36381639 105166500
#> 7 105167001 105168000 900 1.25764091 105167500
#> 8 105168001 105169000 900 5.89664430 105168500
#> 9 105169001 105170000 900 6.45325823 105169500
#> 10 105170001 105171000 900 1.59476554 105170500
#> 11 105171001 105172000 900 4.07956283 105171500
#> 12 105172001 105173000 900 2.40338834 105172500
#> 13 105173001 105174000 900 -0.10405596 105173500
#> 14 105174001 105175000 900 1.85935535 105174500
#> 15 105175001 105176000 900 5.81017157 105175500
#> 16 105176001 105177000 900 -0.98089535 105176500
#> 17 105177001 105178000 900 1.82254116 105177500
#> 18 105178001 105179000 900 -1.63380237 105178500
#> 19 105179001 105180000 900 0.57444353 105179500
#> 20 105180001 105181000 900 0.32280599 105180500
#> 21 105181001 105182000 900 1.52375581 105181500
#> 22 105182001 105183000 900 0.15174045 105182500
#> 23 105183001 105184000 900 5.05983720 105183500
#> 24 105184001 105185000 900 5.63902538 105184500
#> 25 105185001 105186000 900 -0.14286512 105185500
#> 26 105186001 105187000 900 2.60200300 105186500
#> 27 105187001 105188000 900 -0.12477195 105187500
#> 28 105188001 105189000 900 -0.30652590 105188500
#> 29 105189001 105190000 900 5.81675180 105189500
#> 30 105190001 105191000 900 1.82304477 105190500
#> 31 105191001 105192000 900 1.80092550 105191500
#> 32 105192001 105193000 900 0.82782150 105192500
#> 33 105193001 105194000 900 -0.76753630 105193500
#> 34 105194001 105195000 900 7.25783038 105194500
#> 35 105195001 105196000 900 -0.01570106 105195500
#> 36 105196001 105197000 900 1.65142818 105196500
#> 37 105197001 105198000 900 0.46891344 105197500
#> 38 105198001 105199000 900 1.60305120 105198500
#> 39 105199001 105200000 900 0.00000000 105199500
#> 40 105200001 105201000 900 0.58922813 105200500
#> 41 105201001 105202000 900 0.65252284 105201500
#> 42 105202001 105203000 900 -3.19818005 105202500
#> 43 105203001 105204000 900 1.44053240 105203500
#> 44 105204001 105205000 900 -0.37050731 105204500
#> 45 105205001 105206000 900 -4.19972956 105205500
#> 46 105206001 105207000 900 -0.39704857 105206500
#> 47 105207001 105208000 900 -1.18774890 105207500
#> 48 105208001 105209000 900 0.53254711 105208500
#> 49 105209001 105210000 900 -1.72730252 105209500
#> 50 105210001 105211000 900 0.64427158 105210500
#> 51 105211001 105212000 900 1.74924201 105211500
#> 52 105212001 105213000 900 0.40394364 105212500
#> 53 105213001 105214000 900 3.65413895 105213500
#> 54 105214001 105215000 900 3.33422703 105214500
#> 55 105215001 105216000 900 9.13466127 105215500
#> 56 105216001 105217000 900 10.71375854 105216500
#> 57 105217001 105218000 900 3.90014982 105217500
#> 58 105218001 105219000 900 4.78954939 105218500
#> 59 105219001 105220000 900 -0.41998836 105219500
#> 60 105220001 105221000 900 3.49247257 105220500
#> 61 105221001 105222000 900 1.29136168 105221500
#> 62 105222001 105223000 900 0.87667550 105222500
#> 63 105223001 105224000 900 5.89145749 105223500
#> 64 105224001 105225000 900 2.86743815 105224500
#> 65 105225001 105226000 900 0.61567988 105225500
#> 66 105226001 105227000 900 0.38410322 105226500
#> 67 105227001 105228000 900 -0.31488585 105227500
#> 68 105228001 105229000 900 -0.37311767 105228500
#> 69 105229001 105230000 900 -0.24105546 105229500
#> 70 105230001 105231000 900 1.17492295 105230500
#> 71 105231001 105232000 900 -7.68562167 105231500
#> 72 105232001 105233000 900 -4.22545600 105232500
#> 73 105233001 105234000 900 -1.50298666 105233500
#> 74 105234001 105235000 900 3.08646564 105234500
#> 75 105235001 105236000 900 -7.02855321 105235500
#> 76 105236001 105237000 900 -4.70484204 105236500
#> 77 105237001 105238000 900 3.38472703 105237500
#> 78 105238001 105239000 900 0.38516313 105238500
#> 79 105239001 105240000 900 3.04562199 105239500
#> 80 105240001 105241000 900 9.28213537 105240500
#> 81 105241001 105242000 900 -0.08549404 105241500
#> 82 105242001 105243000 900 0.02945048 105242500
#> 83 105243001 105244000 900 1.90675585 105243500
#> 84 105244001 105245000 900 -3.42749903 105244500
#> 85 105245001 105246000 900 9.01128240 105245500
#> 86 105246001 105247000 900 3.00736577 105246500
#> 87 105247001 105248000 900 -2.15340696 105247500
#> 88 105248001 105249000 900 -4.04325209 105248500
#> 89 105249001 105250000 900 -0.01725584 105249500
#> 90 105250001 105251000 900 0.00000000 105250500
#> 91 105251001 105252000 900 0.86801981 105251500
#> 92 105252001 105253000 900 -9.66345124 105252500
#> 93 105253001 105254000 900 -0.43837430 105253500
#> 94 105254001 105255000 900 1.10595177 105254500
#> 95 105255001 105256000 900 1.55586023 105255500
#> 96 105256001 105257000 900 1.92056537 105256500
#> 97 105257001 105258000 900 -0.03200697 105257500
#> 98 105258001 105259000 900 -0.52827008 105258500
#> 99 105259001 105260000 900 -0.52827008 105259500
#> 100 105260001 105261000 900 -0.18359177 105260500
#> 101 105261001 105262000 900 7.36847864 105261500
#> 102 105262001 105263000 900 5.96542868 105262500
#> 103 105263001 105264000 900 -1.75988999 105263500
#> 104 105264001 105265000 900 13.71636319 105264500
#> 105 105265001 105266000 900 -0.29676745 105265500
#> 106 105266001 105267000 900 -8.78514061 105266500
#> 107 105267001 105268000 900 8.45712862 105267500
#> 108 105268001 105269000 900 -0.97214550 105268500
#> 109 105269001 105270000 900 -0.67212721 105269500
#> 110 105270001 105271000 900 -1.67409588 105270500
#> 111 105271001 105272000 900 -3.50629030 105271500
#> 112 105272001 105273000 900 0.19443660 105272500
#> 113 105273001 105274000 900 -0.57125181 105273500
#> 114 105274001 105275000 900 -3.57819950 105274500
#> 115 105275001 105276000 900 -0.19316674 105275500
#> 116 105276001 105277000 900 -2.69653169 105276500
#> 117 105277001 105278000 900 -6.12666109 105277500
#> 118 105278001 105279000 900 0.00000000 105278500
#> 119 105279001 105280000 900 0.00000000 105279500
#> 120 105280001 105281000 900 -1.52197516 105280500
#> 121 105281001 105282000 900 -1.13640158 105281500
#> 122 105282001 105283000 900 -1.77676090 105282500
#> 123 105283001 105284000 900 0.67991286 105283500
#> 124 105284001 105285000 900 -4.80287436 105284500
#> 125 105285001 105286000 900 2.74367698 105285500
#> 126 105286001 105287000 900 5.62389117 105286500
#> 127 105287001 105288000 900 -4.35299909 105287500
#> 128 105288001 105289000 900 -3.51003876 105288500
#> 129 105289001 105290000 900 2.07219680 105289500
#> 130 105290001 105291000 900 -4.85950090 105290500
#> 131 105291001 105292000 900 9.27203578 105291500
#> 132 105292001 105293000 900 -1.66360431 105292500
#> 133 105293001 105294000 900 1.25885757 105293500
#> 134 105294001 105295000 900 1.59000575 105294500
#> 135 105295001 105296000 900 -4.17050706 105295500
#> 136 105296001 105297000 900 11.74088507 105296500
#> 137 105297001 105298000 900 -2.39892626 105297500
#> 138 105298001 105299000 900 0.43070429 105298500
#> 139 105299001 105300000 900 1.91033969 105299500
#> 140 105300001 105301000 900 -0.22516351 105300500
#> 141 105301001 105302000 900 -0.77916702 105301500
#> 142 105302001 105303000 900 -3.42310183 105302500
#> 143 105303001 105304000 900 2.18473323 105303500
#> 144 105304001 105305000 900 12.18132964 105304500
#> 145 105305001 105306000 900 5.62158874 105305500
#> 146 105306001 105307000 900 -0.01238106 105306500
#> 147 105307001 105308000 900 6.62605725 105307500
#> 148 105308001 105309000 900 -4.93545703 105308500
#> 149 105309001 105310000 900 -8.57274599 105309500
#> 150 105310001 105311000 900 -4.91028138 105310500
#> 151 105311001 105312000 900 0.87234052 105311500
#> 152 105312001 105313000 900 -1.11204387 105312500
#> 153 105313001 105314000 900 -0.23603225 105313500
#> 154 105314001 105315000 900 4.47258321 105314500
#> 155 105315001 105316000 900 -3.79242417 105315500
#> 156 105316001 105317000 900 1.14082791 105316500
#> 157 105317001 105318000 900 0.59045240 105317500
#> 158 105318001 105319000 900 3.34414429 105318500
#> 159 105319001 105320000 900 1.88625355 105319500
#> 160 105320001 105321000 900 -0.92418804 105320500
#> 161 105321001 105322000 900 -0.14311394 105321500
#> 162 105322001 105323000 900 3.49106168 105322500
#> 163 105323001 105324000 900 -0.58344161 105323500
#> 164 105324001 105325000 900 -1.38772779 105324500
#> 165 105325001 105326000 900 2.67556883 105325500
#> 166 105326001 105327000 900 8.79368702 105326500
#> 167 105327001 105328000 900 3.94867597 105327500
#> 168 105328001 105329000 900 0.72616173 105328500
#> 169 105329001 105330000 900 4.03709307 105329500
#> 170 105330001 105331000 900 5.12771616 105330500
#> 171 105331001 105332000 900 -1.31488136 105331500
#> 172 105332001 105333000 900 0.29208741 105332500
#> 173 105333001 105334000 900 -1.11209681 105333500
#> 174 105334001 105335000 900 -0.94694782 105334500
#> 175 105335001 105336000 900 -0.78143494 105335500
#> 176 105336001 105337000 900 -1.27014867 105336500
#> 177 105337001 105338000 900 -24.89980003 105337500
#> 178 105338001 105339000 900 -7.57270153 105338500
#> 179 105339001 105340000 900 -0.33898446 105339500
#> 180 105340001 105341000 900 0.35498987 105340500
#> 181 105341001 105342000 900 5.92209771 105341500
#> 182 105342001 105343000 900 -4.64263587 105342500
#> 183 105343001 105344000 900 -0.19101471 105343500
#> 184 105344001 105345000 900 -1.71171181 105344500
#> 185 105345001 105346000 900 -5.37225414 105345500
#> 186 105346001 105347000 900 0.54487106 105346500
#> 187 105347001 105348000 900 1.26837402 105347500
#> 188 105348001 105349000 900 0.02506268 105348500
#> 189 105349001 105350000 900 0.02506268 105349500
#> 190 105350001 105351000 900 -5.07606080 105350500
#> 191 105351001 105352000 900 -0.17664059 105351500
#> 192 105352001 105353000 900 2.36507368 105352500
#> 193 105353001 105354000 900 -0.17062785 105353500
#> 194 105354001 105355000 900 2.02347440 105354500
#> 195 105355001 105356000 900 -2.43357455 105355500
#> 196 105356001 105357000 900 -4.06954764 105356500
#> 197 105357001 105358000 900 4.06183539 105357500
#> 198 105358001 105359000 900 6.19177284 105358500
#> 199 105359001 105360000 900 -1.27135025 105359500
#> 200 105360001 105361000 900 5.58148340 105360500
#> 201 105361001 105362000 900 6.31674592 105361500
#> 202 105362001 105363000 900 -0.17921486 105362500
#> 203 105363001 105364000 900 0.91944627 105363500
#> 204 105364001 105365000 900 1.42287606 105364500
#> 205 105365001 105366000 900 6.32131443 105365500
#> 206 105366001 105367000 900 8.52610569 105366500
#> 207 105367001 105368000 900 1.30985750 105367500
#> 208 105368001 105369000 900 0.21581353 105368500
#> 209 105369001 105370000 900 6.16343880 105369500
#> 210 105370001 105371000 900 2.53157463 105370500
#> 211 105371001 105372000 900 -7.46210562 105371500
#> 212 105372001 105373000 900 -1.11739551 105372500
#> 213 105373001 105374000 900 4.71188074 105373500
#> 214 105374001 105375000 900 6.99384523 105374500
#> 215 105375001 105376000 900 3.77993798 105375500
#> 216 105376001 105377000 900 10.23627025 105376500
#> 217 105377001 105378000 900 5.46073626 105377500
#> 218 105378001 105379000 900 3.35041193 105378500
#> 219 105379001 105380000 900 9.62099817 105379500
#> 220 105380001 105381000 900 -5.19768142 105380500
#> 221 105381001 105382000 900 -3.12299016 105381500
#> 222 105382001 105383000 900 -2.72435313 105382500
#> 223 105383001 105384000 900 1.71426925 105383500
#> 224 105384001 105385000 900 -0.83313428 105384500
#> 225 105385001 105386000 900 6.31807396 105385500
#> 226 105386001 105387000 900 -10.80582707 105386500
#> 227 105387001 105388000 900 0.87534320 105387500
#> 228 105388001 105389000 900 -0.94615772 105388500
#> 229 105389001 105390000 900 2.25826029 105389500
#> 230 105390001 105391000 900 2.05835163 105390500
#> 231 105391001 105392000 900 0.70949277 105391500
#> 232 105392001 105393000 900 4.60207562 105392500
#> 233 105393001 105394000 900 7.62705601 105393500
#> 234 105394001 105395000 900 7.53043594 105394500
#> 235 105395001 105396000 900 -12.47294874 105395500
#> 236 105396001 105397000 900 5.54999689 105396500
#> 237 105397001 105398000 900 -4.94843436 105397500
#> 238 105398001 105399000 900 5.56540126 105398500
#> 239 105399001 105400000 900 5.89219790 105399500
#> 240 105400001 105401000 900 3.87053989 105400500
#> 241 105401001 105402000 900 1.27739141 105401500
#> 242 105402001 105403000 900 -9.10684004 105402500
#> 243 105403001 105404000 900 0.40048863 105403500
#> 244 105404001 105405000 900 5.63319552 105404500
#> 245 105405001 105406000 900 3.78038390 105405500
#> 246 105406001 105407000 900 -0.37984691 105406500
#> 247 105407001 105408000 900 0.41469913 105407500
#> 248 105408001 105409000 900 0.49819172 105408500
#> 249 105409001 105410000 900 0.46338475 105409500
#> 250 105410001 105411000 900 3.77561367 105410500
#> 251 105411001 105412000 900 0.31099854 105411500
#> 252 105412001 105413000 900 -1.33693967 105412500
#> 253 105413001 105414000 900 -17.30161885 105413500
#> 254 105414001 105415000 900 -1.43049558 105414500
#> 255 105415001 105416000 900 -1.60906044 105415500
#> 256 105416001 105417000 900 2.42735695 105416500
#> 257 105417001 105418000 900 -0.56056125 105417500
#> 258 105418001 105419000 900 7.81450534 105418500
#> 259 105419001 105420000 900 9.92889558 105419500
#> 260 105420001 105421000 900 6.98876622 105420500
#> 261 105421001 105422000 900 1.22285721 105421500
#> 262 105422001 105423000 900 1.24627546 105422500
#> 263 105423001 105424000 900 3.23566926 105423500
#> 264 105424001 105425000 900 1.29620217 105424500
#> 265 105425001 105426000 900 8.07757556 105425500
#> 266 105426001 105427000 900 7.22850144 105426500
#> 267 105427001 105428000 900 -0.27519041 105427500
#> 268 105428001 105429000 900 1.26588414 105428500
#> 269 105429001 105430000 900 -1.86479728 105429500
#> 270 105430001 105431000 900 -16.38026341 105430500
#> 271 105431001 105432000 900 -7.49952606 105431500
#> 272 105432001 105433000 900 -4.39600547 105432500
#> 273 105433001 105434000 900 11.04773728 105433500
#> 274 105434001 105435000 900 -1.11039459 105434500
#> 275 105435001 105436000 900 -2.58992672 105435500
#> 276 105436001 105437000 900 -0.60125858 105436500
#> 277 105437001 105438000 900 -0.65855266 105437500
#> 278 105438001 105439000 900 1.89591746 105438500
#> 279 105439001 105440000 900 -19.03425219 105439500
#> 280 105440001 105441000 900 -3.04056029 105440500
#> 281 105441001 105442000 900 -1.66415452 105441500
#> 282 105442001 105443000 900 -21.25598841 105442500
#> 283 105443001 105444000 900 -4.62011807 105443500
#> 284 105444001 105445000 900 -11.16686003 105444500
#> 285 105445001 105446000 900 -5.73927840 105445500
#> 286 105446001 105447000 900 9.34863636 105446500
#> 287 105447001 105448000 900 -1.40177037 105447500
#> 288 105448001 105449000 900 -11.52282633 105448500
#> 289 105449001 105450000 900 -7.19031446 105449500
#> 290 105450001 105451000 900 3.66064044 105450500
#> 291 105451001 105452000 900 4.96397064 105451500
#> 292 105452001 105453000 900 9.25549310 105452500
#> 293 105453001 105454000 900 0.35320307 105453500
#> 294 105454001 105455000 900 -9.37343619 105454500
#> 295 105455001 105456000 900 8.38362744 105455500
#> 296 105456001 105457000 900 6.49967889 105456500
#> 297 105457001 105458000 900 -19.60542145 105457500
#> 298 105458001 105459000 900 0.08342873 105458500
#> 299 105459001 105460000 900 0.08342873 105459500
#> 300 105460001 105461000 900 0.08342873 105460500
#> 301 105461001 105462000 900 -0.01905449 105461500
#> 302 105462001 105463000 900 -6.69931026 105462500
#> 303 105463001 105464000 900 9.93836536 105463500
#> 304 105464001 105465000 900 3.30578667 105464500
#> 305 105465001 105466000 900 15.94640023 105465500
#> 306 105466001 105467000 900 1.54249575 105466500
#> 307 105467001 105468000 900 4.23374718 105467500
#> 308 105468001 105469000 900 4.23374718 105468500
#> 309 105469001 105470000 900 3.37647852 105469500
#> 310 105470001 105471000 900 -15.11580554 105470500
#> 311 105471001 105472000 900 1.28390664 105471500
#> 312 105472001 105473000 900 -4.03554159 105472500
#> 313 105473001 105474000 900 7.35665580 105473500
#> 314 105474001 105475000 900 0.15910155 105474500
#> 315 105475001 105476000 900 -0.39302921 105475500
#> 316 105476001 105477000 900 10.86625629 105476500
#> 317 105477001 105478000 900 -3.24419513 105477500
#> 318 105478001 105479000 900 4.71367135 105478500
#> 319 105479001 105480000 900 7.08881781 105479500
#> 320 105480001 105481000 900 -2.18320986 105480500
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#> 674 105834001 105835000 900 -0.33874175 105834500
#> 675 105835001 105836000 900 1.71609570 105835500
#> 676 105836001 105837000 900 -5.75842608 105836500
#> 677 105837001 105838000 900 3.71911004 105837500
#> 678 105838001 105839000 900 0.16313737 105838500
#> 679 105839001 105840000 900 0.30900489 105839500
#> 680 105840001 105841000 900 0.29664469 105840500
#> 681 105841001 105842000 900 0.78629022 105841500
#> 682 105842001 105843000 900 0.48989920 105842500
#> 683 105843001 105844000 900 4.86529179 105843500
#> 684 105844001 105845000 900 3.98632666 105844500
#> 685 105845001 105846000 900 -0.10028316 105845500
#> 686 105846001 105847000 900 -0.06418122 105846500
#> 687 105847001 105848000 900 0.10963327 105847500
#> 688 105848001 105849000 900 0.91813004 105848500
#> 689 105849001 105850000 900 0.00000000 105849500
#> 690 105850001 105851000 900 0.62511688 105850500
#> 691 105851001 105852000 900 -3.16616403 105851500
#> 692 105852001 105853000 900 1.27111031 105852500
#> 693 105853001 105854000 900 5.59761468 105853500
#> 694 105854001 105855000 900 -0.31719192 105854500
#> 695 105855001 105856000 900 1.36908030 105855500
#> 696 105856001 105857000 900 1.19415687 105856500
#> 697 105857001 105858000 900 1.08557393 105857500
#> 698 105858001 105859000 900 -5.48681467 105858500
#> 699 105859001 105860000 900 -0.05841155 105859500
#> 700 105860001 105861000 900 -2.26982244 105860500
#> 701 105861001 105862000 900 -0.83970310 105861500
#> 702 105862001 105863000 900 0.00000000 105862500
#> 703 105863001 105864000 900 0.00000000 105863500
#> 704 105864001 105865000 900 2.09926249 105864500
#> 705 105865001 105866000 900 -12.81316445 105865500
#> 706 105866001 105867000 900 0.77696027 105866500
#> 707 105867001 105868000 900 -0.79657035 105867500
#> 708 105868001 105869000 900 -0.32838248 105868500
#> 709 105869001 105870000 900 -4.73206456 105869500
#> 710 105870001 105871000 900 -9.25187615 105870500
#> 711 105871001 105872000 900 2.68249695 105871500
#> 712 105872001 105873000 900 -1.00749419 105872500
#> 713 105873001 105874000 900 4.50480018 105873500
#> 714 105874001 105875000 900 -3.94012730 105874500
#> 715 105875001 105876000 900 6.08633552 105875500
#> 716 105876001 105877000 900 -8.58604531 105876500
#> 717 105877001 105878000 900 2.55481319 105877500
#> 718 105878001 105879000 900 10.34768860 105878500
#> 719 105879001 105880000 900 11.95873628 105879500
#> 720 105880001 105881000 900 6.02754472 105880500
#> 721 105881001 105882000 900 13.60859764 105881500
#> 722 105882001 105883000 900 1.23460174 105882500
#> 723 105883001 105884000 900 -1.55765881 105883500
#> 724 105884001 105885000 900 -3.58465766 105884500
#> 725 105885001 105886000 900 -2.63818468 105885500
#> 726 105886001 105887000 900 -0.31594484 105886500
#> 727 105887001 105888000 900 -4.11095723 105887500
#> 728 105888001 105889000 900 -4.92477263 105888500
#> 729 105889001 105890000 900 0.27329816 105889500
#> 730 105890001 105891000 900 -1.50629620 105890500
#> 731 105891001 105892000 900 -4.27653513 105891500
#> 732 105892001 105893000 900 -3.20080779 105892500
#> 733 105893001 105894000 900 -3.57963519 105893500
#> 734 105894001 105895000 900 1.51823617 105894500
#> 735 105895001 105896000 900 2.69598786 105895500
#> 736 105896001 105897000 900 -6.18370554 105896500
#> 737 105897001 105898000 900 -5.28648135 105897500
#> 738 105898001 105899000 900 5.17215146 105898500
#> 739 105899001 105900000 900 5.39053384 105899500
#> 740 105900001 105901000 900 0.80546223 105900500
#> 741 105901001 105902000 900 12.39011479 105901500
#> 742 105902001 105903000 900 4.14753609 105902500
#> 743 105903001 105904000 900 3.26192384 105903500
#> 744 105904001 105905000 900 10.27849761 105904500
#> 745 105905001 105906000 900 6.01509539 105905500
#> 746 105906001 105907000 900 -1.37584369 105906500
#> 747 105907001 105908000 900 3.66808192 105907500
#> 748 105908001 105909000 900 -3.39671933 105908500
#> 749 105909001 105910000 900 -2.51118985 105909500
#> 750 105910001 105911000 900 -6.05297667 105910500
#> 751 105911001 105912000 900 -5.99405046 105911500
#> 752 105912001 105913000 900 -3.91480063 105912500
#> 753 105913001 105914000 900 12.62467198 105913500
#> 754 105914001 105915000 900 -8.61280118 105914500
#> 755 105915001 105916000 900 1.50070500 105915500
#> 756 105916001 105917000 900 -2.07682571 105916500
#> 757 105917001 105918000 900 -4.16371378 105917500
#> 758 105918001 105919000 900 -0.16256739 105918500
#> 759 105919001 105920000 900 2.84573281 105919500
#> 760 105920001 105921000 900 -5.50368385 105920500
#> 761 105921001 105922000 900 -16.67466460 105921500
#> 762 105922001 105923000 900 -2.46887283 105922500
#> 763 105923001 105924000 900 0.28992030 105923500
#> 764 105924001 105925000 900 2.86598866 105924500
#> 765 105925001 105926000 900 6.51675886 105925500
#> 766 105926001 105927000 900 -4.42922098 105926500
#> 767 105927001 105928000 900 -4.27154021 105927500
#> 768 105928001 105929000 900 -2.96048690 105928500
#> 769 105929001 105930000 900 6.57523708 105929500
#> 770 105930001 105931000 900 -9.22952212 105930500
#> 771 105931001 105932000 900 -3.16016744 105931500
#> 772 105932001 105933000 900 -1.29013987 105932500
#> 773 105933001 105934000 900 -2.21068169 105933500
#> 774 105934001 105935000 900 -1.21353606 105934500
#> 775 105935001 105936000 900 0.12275941 105935500
#> 776 105936001 105937000 900 3.03344859 105936500
#> 777 105937001 105938000 900 -6.49455107 105937500
#> 778 105938001 105939000 900 -2.46458575 105938500
#> 779 105939001 105940000 900 -4.78182622 105939500
#> 780 105940001 105941000 900 1.38231491 105940500
#> 781 105941001 105942000 900 5.94460463 105941500
#> 782 105942001 105943000 900 -0.86682783 105942500
#> 783 105943001 105944000 900 0.00000000 105943500
#> 784 105944001 105945000 900 0.00000000 105944500
#> 785 105945001 105946000 900 0.00000000 105945500
#> 786 105946001 105947000 900 0.00000000 105946500
#> 787 105947001 105948000 900 0.00000000 105947500
#> 788 105948001 105949000 900 -3.98130871 105948500
#> 789 105949001 105950000 900 -1.43670141 105949500
#> 790 105950001 105951000 900 8.88538272 105950500
#> 791 105951001 105952000 900 -3.04079320 105951500
#> 792 105952001 105953000 900 0.31335869 105952500
#> 793 105953001 105954000 900 -14.99445482 105953500
#> 794 105954001 105955000 900 0.58227434 105954500
#> 795 105955001 105956000 900 0.24721992 105955500
#> 796 105956001 105957000 900 8.99770119 105956500
#> 797 105957001 105958000 900 10.37905471 105957500
#> 798 105958001 105959000 900 -6.16557697 105958500
#> 799 105959001 105960000 900 -10.00322646 105959500
#> 800 105960001 105961000 900 -0.45789841 105960500
#> 801 105961001 105962000 900 -3.74534085 105961500
#> 802 105962001 105963000 900 -0.77430871 105962500
#> 803 105963001 105964000 900 -1.12639269 105963500
#> 804 105964001 105965000 900 -1.90164055 105964500
#> 805 105965001 105966000 900 -1.14049352 105965500
#> 806 105966001 105967000 900 -1.14049352 105966500
#> 807 105967001 105968000 900 1.14658046 105967500
#> 808 105968001 105969000 900 -1.80834249 105968500
#> 809 105969001 105970000 900 -4.51490753 105969500
#> 810 105970001 105971000 900 7.20485907 105970500
#> 811 105971001 105972000 900 1.53900987 105971500
#> 812 105972001 105973000 900 -2.75706802 105972500
#> 813 105973001 105974000 900 -10.86431702 105973500
#> 814 105974001 105975000 900 2.49447033 105974500
#> 815 105975001 105976000 900 5.71984730 105975500
#> 816 105976001 105977000 900 0.39288321 105976500
#> 817 105977001 105978000 900 6.65090977 105977500
#> 818 105978001 105979000 900 -0.93592871 105978500
#> 819 105979001 105980000 900 1.78892121 105979500
#> 820 105980001 105981000 900 -0.47898488 105980500
#> 821 105981001 105982000 900 2.91604616 105981500
#> 822 105982001 105983000 900 -1.41664040 105982500
#> 823 105983001 105984000 900 -5.31092262 105983500
#> 824 105984001 105985000 900 3.04149341 105984500
#> 825 105985001 105986000 900 1.40255537 105985500
#> 826 105986001 105987000 900 -16.33052610 105986500
#> 827 105987001 105988000 900 1.92596612 105987500
#> 828 105988001 105989000 900 3.01537665 105988500
#> 829 105989001 105990000 900 6.74037466 105989500
#> 830 105990001 105991000 900 -0.10024602 105990500
#> 831 105991001 105992000 900 -1.19098146 105991500
#> 832 105992001 105993000 900 -3.25893941 105992500
#> 833 105993001 105994000 900 -0.84348740 105993500
#> 834 105994001 105995000 900 -2.47251603 105994500
#> 835 105995001 105996000 900 -1.37841333 105995500
#> 836 105996001 105997000 900 -6.17122090 105996500
#> 837 105997001 105998000 900 -3.18056723 105997500
#> 838 105998001 105999000 900 2.20531363 105998500
#> 839 105999001 106000000 900 1.50650078 105999500
#> 840 106000001 106001000 900 4.09009132 106000500
#> 841 106001001 106002000 900 -4.46501972 106001500
#> 842 106002001 106003000 900 -2.95728774 106002500
#> 843 106003001 106004000 900 1.55965253 106003500
#> 844 106004001 106005000 900 3.68044163 106004500
#> 845 106005001 106006000 900 0.38000278 106005500
#> 846 106006001 106007000 900 -0.42496518 106006500
#> 847 106007001 106008000 900 -3.88501217 106007500
#> 848 106008001 106009000 900 -0.99807129 106008500
#> 849 106009001 106010000 900 3.75045853 106009500
#> 850 106010001 106011000 900 1.53297825 106010500
#> 851 106011001 106012000 900 9.11956033 106011500
#> 852 106012001 106013000 900 0.69680688 106012500
#> 853 106013001 106014000 900 -1.86866606 106013500
#> 854 106014001 106015000 900 -1.86866606 106014500
#> 855 106015001 106016000 900 -4.97801729 106015500
#> 856 106016001 106017000 900 -23.07269182 106016500
#> 857 106017001 106018000 900 -0.99030326 106017500
#> 858 106018001 106019000 900 -4.18315419 106018500
#> 859 106019001 106020000 900 -3.82443234 106019500
#> 860 106020001 106021000 900 -1.90860835 106020500
#> 861 106021001 106022000 900 1.49593744 106021500
#> 862 106022001 106023000 900 -0.60744379 106022500
#> 863 106023001 106024000 900 -4.32783522 106023500
#> 864 106024001 106025000 900 -11.06546743 106024500
#> 865 106025001 106026000 900 -0.10188580 106025500
#> 866 106026001 106027000 900 1.15347809 106026500
#> 867 106027001 106028000 900 0.42520323 106027500
#> 868 106028001 106029000 900 -10.17381501 106028500
#> 869 106029001 106030000 900 -5.22148412 106029500
#> 870 106030001 106031000 900 5.31114043 106030500
#> 871 106031001 106032000 900 4.28687488 106031500
#> 872 106032001 106033000 900 -1.68851345 106032500
#> 873 106033001 106034000 900 -4.07567209 106033500
#> 874 106034001 106035000 900 6.07000404 106034500
#> 875 106035001 106036000 900 -0.21287417 106035500
#> 876 106036001 106037000 900 -14.41881047 106036500
#> 877 106037001 106038000 900 -5.65036291 106037500
#> 878 106038001 106039000 900 -10.15538384 106038500
#> 879 106039001 106040000 900 -2.29466650 106039500
#> 880 106040001 106041000 900 -17.03897841 106040500
#> 881 106041001 106042000 900 -15.11958304 106041500
#> 882 106042001 106043000 900 -33.50738191 106042500
#> 883 106043001 106044000 900 1.17524754 106043500
#> 884 106044001 106045000 900 -1.25432570 106044500
#> 885 106045001 106046000 900 1.44062393 106045500
#> 886 106046001 106047000 900 -0.82830530 106046500
#> 887 106047001 106048000 900 1.65702831 106047500
#> 888 106048001 106049000 900 3.66285167 106048500
#> 889 106049001 106050000 900 4.87790371 106049500
#> 890 106050001 106051000 900 -1.86114448 106050500
#> 891 106051001 106052000 900 1.47010750 106051500
#> 892 106052001 106053000 900 -3.35858583 106052500
#> 893 106053001 106054000 900 0.08378930 106053500
#> 894 106054001 106055000 900 -0.27228786 106054500
#> 895 106055001 106056000 900 -4.69264999 106055500
#> 896 106056001 106057000 900 -0.75077157 106056500
#> 897 106057001 106058000 900 -7.07273338 106057500
#> 898 106058001 106059000 900 -2.45406856 106058500
#> 899 106059001 106060000 900 -3.28803329 106059500
#> 900 106060001 106061000 900 8.99884286 106060500
#> 901 106061001 106062000 900 -5.44183681 106061500
#> 902 106062001 106063000 900 -3.36088332 106062500
#> 903 106063001 106064000 900 4.62570752 106063500
#> 904 106064001 106065000 900 -7.95195351 106064500
#> 905 106065001 106066000 900 -11.05617626 106065500
#> 906 106066001 106067000 900 -1.88622864 106066500
#> 907 106067001 106068000 900 -1.14970503 106067500
#> 908 106068001 106069000 900 -6.46198273 106068500
#> 909 106069001 106070000 900 -7.12796142 106069500
#> 910 106070001 106071000 900 -6.23822004 106070500
#> 911 106071001 106072000 900 2.62377185 106071500
#> 912 106072001 106073000 900 -0.52298767 106072500
#> 913 106073001 106074000 900 1.79058335 106073500
#> 914 106074001 106075000 900 -12.63210467 106074500
#> 915 106075001 106076000 900 -3.47463655 106075500
#> 916 106076001 106077000 900 -0.70928565 106076500
#> 917 106077001 106078000 900 -5.27505424 106077500
#> 918 106078001 106079000 900 8.32394267 106078500
#> 919 106079001 106080000 900 1.34534947 106079500
#> 920 106080001 106081000 900 -9.85752566 106080500
#> 921 106081001 106082000 900 2.55533612 106081500
#> 922 106082001 106083000 900 3.23096428 106082500
#> 923 106083001 106084000 900 -8.10232022 106083500
#> 924 106084001 106085000 900 -4.93087867 106084500
#> 925 106085001 106086000 900 4.03245783 106085500
#> 926 106086001 106087000 900 -7.13426941 106086500
#> 927 106087001 106088000 900 -2.49246902 106087500
#> 928 106088001 106089000 900 -8.52119075 106088500
#> 929 106089001 106090000 900 1.37212196 106089500
#> 930 106090001 106091000 900 -6.28299928 106090500
#> 931 106091001 106092000 900 -1.01314719 106091500
#> 932 106092001 106093000 900 -21.19201766 106092500
#> 933 106093001 106094000 900 -9.35656736 106093500
#> 934 106094001 106095000 900 -7.91660864 106094500
#> 935 106095001 106096000 900 4.82197557 106095500
#> 936 106096001 106097000 900 -14.46048135 106096500
#> 937 106097001 106098000 900 -3.75433738 106097500
#> 938 106098001 106099000 900 -5.12037607 106098500
#> 939 106099001 106100000 900 12.18426552 106099500
#> 940 106100001 106101000 900 0.90261554 106100500
#> 941 106101001 106102000 900 0.80648689 106101500
#> 942 106102001 106103000 900 -0.94864588 106102500
#> 943 106103001 106104000 900 -9.16406801 106103500
#> 944 106104001 106105000 900 -2.16775258 106104500
#> 945 106105001 106106000 900 -1.05855580 106105500
#> 946 106106001 106107000 900 -1.60006968 106106500
#> 947 106107001 106108000 900 -0.54422973 106107500
#> 948 106108001 106109000 900 -5.16729103 106108500
#> 949 106109001 106110000 900 3.06919322 106109500
#> 950 106110001 106111000 900 -0.86263993 106110500
#> 951 106111001 106112000 900 -1.76564818 106111500
#> 952 106112001 106113000 900 -5.10209312 106112500
#> 953 106113001 106114000 900 -0.15303609 106113500
#> 954 106114001 106115000 900 6.05325156 106114500
#> 955 106115001 106116000 900 10.32775073 106115500
#> 956 106116001 106117000 900 5.96260087 106116500
#> 957 106117001 106118000 900 -2.03309967 106117500
#> 958 106118001 106119000 900 -2.26355109 106118500
#> 959 106119001 106120000 900 -3.64722269 106119500
#> 960 106120001 106121000 900 -0.65966882 106120500
#> 961 106121001 106122000 900 -0.68403736 106121500
#> 962 106122001 106123000 900 -2.67963486 106122500
#> 963 106123001 106124000 900 -14.49038996 106123500
#> 964 106124001 106125000 900 1.43773489 106124500
#> 965 106125001 106126000 900 -1.00214629 106125500
#> 966 106126001 106127000 900 -2.67548708 106126500
#> 967 106127001 106128000 900 -0.84418564 106127500
#> 968 106128001 106129000 900 -4.04024560 106128500
#> 969 106129001 106130000 900 -0.93540401 106129500
#> 970 106130001 106131000 900 0.19033554 106130500
#> 971 106131001 106132000 900 0.34025787 106131500
#> 972 106132001 106133000 900 -9.02415964 106132500
#> 973 106133001 106134000 900 -1.27850430 106133500
#> 974 106134001 106135000 900 -2.33439438 106134500
#> 975 106135001 106136000 900 2.53663562 106135500
#> 976 106136001 106137000 900 1.83688649 106136500
#> 977 106137001 106138000 900 -0.03530281 106137500
#> 978 106138001 106139000 900 0.36434120 106138500
#> 979 106139001 106140000 900 -2.14572544 106139500
#> 980 106140001 106141000 900 -2.04454607 106140500
#> 981 106141001 106142000 900 -0.54210025 106141500
#> 982 106142001 106143000 900 -0.45546279 106142500
#> 983 106143001 106144000 900 -7.69146361 106143500
#> 984 106144001 106145000 900 -5.89847463 106144500
#> 985 106145001 106146000 900 1.53978839 106145500
#> 986 106146001 106147000 900 0.06800094 106146500
#> 987 106147001 106148000 900 0.33623294 106147500
#> 988 106148001 106149000 900 2.31461311 106148500
#> 989 106149001 106150000 900 1.75655118 106149500
#> 990 106150001 106151000 900 -1.12194536 106150500
#> 991 106151001 106152000 900 -2.29733164 106151500
#> 992 106152001 106153000 900 -1.25375339 106152500
#> 993 106153001 106154000 900 -1.38991321 106153500
#> 994 106154001 106155000 900 1.76369571 106154500
#> 995 106155001 106156000 900 -1.09647414 106155500
#> 996 106156001 106157000 900 -3.98084108 106156500
#> 997 106157001 106158000 900 2.79188678 106157500
#> 998 106158001 106159000 900 -10.23162365 106158500
#> 999 106159001 106160000 900 -15.29232188 106159500
#> 1000 106160001 106161000 900 0.19911058 106160500
#> 1001 106161001 106162000 900 6.82269228 106161500
#>
#> $layers
#> $layers[[1]]
#> geom_col: just = 0.5, width = NULL, na.rm = FALSE
#> stat_identity: na.rm = FALSE
#> position_stack
#>
#>
#> $scales
#> <ggproto object: Class ScalesList, gg>
#> add: function
#> add_defaults: function
#> add_missing: function
#> backtransform_df: function
#> clone: function
#> find: function
#> get_scales: function
#> has_scale: function
#> input: function
#> map_df: function
#> n: function
#> non_position_scales: function
#> scales: list
#> set_palettes: function
#> train_df: function
#> transform_df: function
#> super: <ggproto object: Class ScalesList, gg>
#>
#> $guides
#> <Guides[0] ggproto object>
#>
#> <empty>
#>
#> $mapping
#> $x
#> <quosure>
#> expr: ^ctr
#> env: global
#>
#> $y
#> <quosure>
#> expr: ^delta
#> env: global
#>
#> $fill
#> <quosure>
#> expr: ^delta > 0
#> env: global
#>
#> attr(,"class")
#> [1] "uneval"
#>
#> $theme
#> list()
#>
#> $coordinates
#> <ggproto object: Class CoordCartesian, Coord, gg>
#> aspect: function
#> backtransform_range: function
#> clip: on
#> default: TRUE
#> distance: function
#> draw_panel: function
#> expand: TRUE
#> is_free: function
#> is_linear: function
#> labels: function
#> limits: list
#> modify_scales: function
#> range: function
#> render_axis_h: function
#> render_axis_v: function
#> render_bg: function
#> render_fg: function
#> reverse: none
#> setup_data: function
#> setup_layout: function
#> setup_panel_guides: function
#> setup_panel_params: function
#> setup_params: function
#> train_panel_guides: function
#> transform: function
#> super: <ggproto object: Class CoordCartesian, Coord, gg>
#>
#> $facet
#> <ggproto object: Class FacetNull, Facet, gg>
#> attach_axes: function
#> attach_strips: function
#> compute_layout: function
#> draw_back: function
#> draw_front: function
#> draw_labels: function
#> draw_panel_content: function
#> draw_panels: function
#> finish_data: function
#> format_strip_labels: function
#> init_gtable: function
#> init_scales: function
#> map_data: function
#> params: list
#> set_panel_size: function
#> setup_data: function
#> setup_panel_params: function
#> setup_params: function
#> shrink: TRUE
#> train_scales: function
#> vars: function
#> super: <ggproto object: Class FacetNull, Facet, gg>
#>
#> $plot_env
#> <environment: R_GlobalEnv>
#>
#> $layout
#> <ggproto object: Class Layout, gg>
#> coord: NULL
#> coord_params: list
#> facet: NULL
#> facet_params: list
#> finish_data: function
#> get_scales: function
#> layout: NULL
#> map_position: function
#> panel_params: NULL
#> panel_scales_x: NULL
#> panel_scales_y: NULL
#> render: function
#> render_labels: function
#> reset_scales: function
#> resolve_label: function
#> setup: function
#> setup_panel_guides: function
#> setup_panel_params: function
#> train_position: function
#> super: <ggproto object: Class Layout, gg>
#>
#> $labels
#> $labels$title
#> [1] "Binned delta mean values for EsCells - Neurons"
#>
#> $labels$x
#> [1] "ctr"
#>
#> $labels$y
#> [1] "delta"
#>
#> $labels$fill
#> [1] "delta > 0"
#>
#>
#> attr(,"class")
#> [1] "gg" "ggplot"Our interface carries out the binning inside the call to the function getSignificantRegions(). The delta data (here deltaSE) is then mined for significant regions.
The algorithm proceeds as follows:
significantRegions = getSignificantRegions(deltaSE,
regionOfInterest,
viewpointRegion,
smallBinSize,
bigBinSize,
numPermutations,
pValue)which are then ploted using
library(deltaCaptureC)
significantRegionsPlot = plotSignificantRegions(significantRegions,
significanceType,
plotTitle)This gives the following result:
#> NULLWe have given an example above of the use of getDeltaSE(), converting miniSE into miniDeltaSE. This is a multi-step process, first normalizing the columns of miniSE, then averaging the values for each of the two treatments and finally taking their difference. For normalization, we relied on DESeq2::estimateSizeFactorsForMatrix().
library(SummarizedExperiment)
library(deltaCaptureC)
se = miniSE
counts = assays(se)[['counts']]
sizeFactors = DESeq2::estimateSizeFactorsForMatrix(counts)
colData(se)$sizeFactors = sizeFactors
assays(se)[['normalizedCounts']] = counts
for(i in seq_len(ncol(assays(se)[['normalizedCounts']])))
{
assays(se)[['normalizedCounts']][,i] =
assays(se)[['normalizedCounts']][,i] /
colData(se)$sizeFactors[i]
}Depending on your application, you may wish to use your own method of normalization.
The delta SummarizedExperiment is then the difference between the mean expressions for the two treatments.
library(SummarizedExperiment)
library(deltaCaptureC)
meanNormalizedCountsSE = getMeanNormalizedCountsSE(miniSE)
meanCounts = assay(meanNormalizedCountsSE)
delta = matrix(meanCounts[,1] - meanCounts[,2],ncol=1)
colData = data.frame(delta=sprintf('%s - %s',
as.character(colData(meanNormalizedCountsSE)$treatment[1]),
as.character(colData(meanNormalizedCountsSE)$treatment[2])),
stringsAsFactors=FALSE)
deltaSE = SummarizedExperiment(assay=list(delta=delta),
colData=colData)
rowRanges(deltaSE) = rowRanges(meanNormalizedCountsSE)Binning
Binning is the rate-limiting step as can be seen from the binning of a small Summarized experiment into a small set of bins:
library(deltaCaptureC)
print(length(smallSetOfSmallBins))
#> [1] 201
print(length(smallerDeltaSE))
#> [1] 401
tictoc::tic('binning into small bins')
binnedSummarizedExperiment = binSummarizedExperiment(smallSetOfSmallBins,smallerDeltaSE)
tictoc::toc()
#> binning into small bins: 77.029 sec elapsedThe algorithm depends on the permutation of small bins and the rebinning of the resulting permuted data into large bins for comparison to the actual data binned into the same larger bins. The binning into smaller bins only happens once, while the rebinning into larger bins is carried out for each permutation. Consequently, we require the larger bin size to be an integer multiple of the smaller and this rebinning is much faster.