Calculate pair-wise correlations between samples using the expr() slots of a list of two ExpressionSets.

This function acts as a wrapper around ComBat (sva package) and cor(), to calculate pairwise correlations within one or between two ExpressionSets.

Usage

corFinder(eset.pair, separator = ":", use.ComBat = TRUE, ...)

Arguments

eset.pair: a list of ExpressionSets, with two elements. If the two elements are identical, return the correlation matrix for pairs of samples in the first element. If not identical, return pairs between the two elements.
separator: Separator between dataset name and sample name. Dataset names are added to sample names to keep track of dataset of origin.
use.ComBat: Use the sva::ComBat function for batch correction of the expr() data between the two datasets.
...: Extra arguments passed to the cor() function.

Value

Returns a matrix of sample-wise Pearson Correlations.

Author

Levi Waldron, Markus Riester, Marcel Ramos

Examples


example("phenoFinder")
#> 
#> phnFnd> library(curatedOvarianData)
#> 
#> phnFnd> data(GSE32063_eset)
#> 
#> phnFnd> data(GSE17260_eset)
#> 
#> phnFnd> esets2 <- list(JapaneseB=GSE32063_eset,
#> phnFnd+                 Yoshihara2010=GSE17260_eset)
#> 
#> phnFnd> ## standardize the sample ids to improve matching based on clinical annotation
#> phnFnd> esets2 <- lapply(esets2, function(X){
#> phnFnd+     X$alt_sample_name <- paste(X$sample_type, gsub("[^0-9]", "", X$alt_sample_name), sep="_")
#> phnFnd+ 
#> phnFnd+ ## Removal of columns that cannot possibly match also helps duplicated patients to stand out
#> phnFnd+     pData(X) <- pData(X)[, !grepl("uncurated_author_metadata", colnames(pData(X)))]
#> phnFnd+     X <- X[, 1:20]  ##speed computations
#> phnFnd+     return(X) })
#> 
#> phnFnd> ## See first six samples in both rows and columns
#> phnFnd> phenoFinder(esets2)[1:6, 1:6]
#>           GSM432220 GSM432221 GSM432222 GSM432223 GSM432224  GSM432225
#> GSM795125 0.2351904 0.1014047 0.3525417 0.7274151 0.2189890 0.27397077
#> GSM795126 0.5404524 0.2588727 0.4083015 0.4079720 0.2927870 0.74123368
#> GSM795127 0.3791279 0.5008562 0.4983502 0.4981226 0.6385506 0.04416984
#> GSM795128 0.2351904 0.1014047 0.3525417 0.3523760 0.2189890 0.27397077
#> GSM795129 0.1076309 0.2395470 0.2190910 0.2189890 0.3643260 0.16030839
#> GSM795130 0.2603947 0.1344290 0.1077761 0.1076793 0.2489234 0.29544860

corFinder(esets2)
#> Found2batches
#> Adjusting for0covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding parametric adjustments
#> Adjusting the Data
#>           GSM432220 GSM432221 GSM432222 GSM432223 GSM432224 GSM432225 GSM432226
#> GSM795125 0.7036383 0.8076965 0.7861202 0.9694795 0.8045911 0.7649717 0.7793360
#> GSM795126 0.7179379 0.7723147 0.7455840 0.7779347 0.7576207 0.9626765 0.7763060
#> GSM795127 0.7597161 0.8091662 0.7485431 0.7926815 0.7391370 0.7753552 0.9695461
#> GSM795128 0.7831360 0.7942215 0.7376993 0.7867722 0.7190443 0.7772283 0.7974600
#> GSM795129 0.7785400 0.8185891 0.7439683 0.7857085 0.7196601 0.7706538 0.8222017
#> GSM795130 0.7134996 0.7420610 0.6547772 0.7059997 0.6564982 0.6730458 0.6892836
#> GSM795131 0.7797818 0.8289165 0.7185256 0.7952971 0.7314630 0.7663868 0.7919936
#> GSM795132 0.7952559 0.8302757 0.7576711 0.7969228 0.8040730 0.7794664 0.7907678
#> GSM795133 0.7186756 0.7949272 0.8024418 0.8033600 0.7626050 0.7679198 0.7794541
#> GSM795134 0.7825912 0.8116178 0.7615561 0.7922744 0.7313048 0.7473650 0.7975605
#> GSM795135 0.7488563 0.8063966 0.7155879 0.7637829 0.7203206 0.7272796 0.8141144
#> GSM795136 0.7846748 0.8288424 0.7473626 0.7905084 0.7456827 0.7923890 0.8163982
#> GSM795137 0.8324305 0.8893188 0.7693861 0.8281942 0.7736172 0.7980584 0.8466171
#> GSM795138 0.7176253 0.7701914 0.7975560 0.8045460 0.8388217 0.7757431 0.7559317
#> GSM795139 0.7809251 0.8420668 0.7851108 0.8198034 0.8035660 0.8130093 0.8209771
#> GSM795140 0.7867166 0.7694014 0.6867525 0.7324631 0.7041552 0.7260312 0.7455840
#> GSM795141 0.7735490 0.7580311 0.6880061 0.7062260 0.6831737 0.7116233 0.6985395
#> GSM795142 0.8036546 0.8196717 0.7143447 0.7554330 0.7200963 0.7310726 0.7737431
#> GSM795143 0.8142711 0.8429483 0.7963035 0.8178958 0.7797394 0.7914247 0.8162633
#> GSM795144 0.7617599 0.7781933 0.7162055 0.7301727 0.7091113 0.7455589 0.7679618
#>           GSM432227 GSM432228 GSM432229 GSM432230 GSM432231 GSM432232 GSM432233
#> GSM795125 0.7746494 0.7731191 0.7753846 0.7947495 0.8087515 0.7655434 0.7096709
#> GSM795126 0.7448651 0.7773854 0.7748230 0.7769026 0.7945813 0.7257834 0.6887378
#> GSM795127 0.7811615 0.7975837 0.8250260 0.7938600 0.8255716 0.7811439 0.7466590
#> GSM795128 0.7469487 0.9640154 0.8013297 0.8263570 0.8185767 0.7604447 0.7255770
#> GSM795129 0.7718693 0.7969023 0.9695206 0.7594169 0.7932134 0.7554612 0.7743897
#> GSM795130 0.7173254 0.7342153 0.6852207 0.7024899 0.7029786 0.7228259 0.6861631
#> GSM795131 0.7797985 0.8146862 0.8204196 0.7815351 0.8004691 0.7520284 0.7742019
#> GSM795132 0.8332580 0.7802046 0.7928001 0.7978369 0.7934185 0.8000076 0.7819689
#> GSM795133 0.7917829 0.7468728 0.7962692 0.7774325 0.8178100 0.7933635 0.7709905
#> GSM795134 0.7727996 0.8102965 0.8064747 0.7908613 0.8069634 0.7937744 0.7738896
#> GSM795135 0.7823027 0.7609267 0.7920369 0.7795796 0.8154953 0.7869330 0.7980005
#> GSM795136 0.8098019 0.7818036 0.8331184 0.7586660 0.7912581 0.7844103 0.8072714
#> GSM795137 0.8429355 0.8327782 0.8451724 0.8105083 0.8301875 0.8314046 0.8081145
#> GSM795138 0.7629109 0.7545269 0.7494508 0.8031032 0.8216577 0.7801121 0.6878260
#> GSM795139 0.8095239 0.8201311 0.8087617 0.8243289 0.8265585 0.7874973 0.7617801
#> GSM795140 0.7654795 0.7598895 0.7251945 0.7412566 0.7484714 0.7920746 0.7542593
#> GSM795141 0.7365493 0.7192507 0.7462946 0.6835080 0.7034446 0.7229784 0.7546771
#> GSM795142 0.8109563 0.7559941 0.7897763 0.7607832 0.7731009 0.7801846 0.7985608
#> GSM795143 0.8051201 0.8146114 0.8197569 0.8085633 0.8118480 0.8037307 0.7913036
#> GSM795144 0.7686488 0.7449652 0.7418798 0.7496875 0.7682887 0.7971254 0.7490261
#>           GSM432234 GSM432235 GSM432236 GSM432237 GSM432238 GSM432239
#> GSM795125 0.7798673 0.6888101 0.7768255 0.7908561 0.7045634 0.7023582
#> GSM795126 0.7701759 0.6867187 0.7748989 0.7835926 0.6990678 0.7176949
#> GSM795127 0.8106644 0.7288669 0.7455993 0.8053609 0.7413881 0.6911001
#> GSM795128 0.7841015 0.7709194 0.7375537 0.7912146 0.7508685 0.6812935
#> GSM795129 0.8203668 0.7698166 0.7486665 0.8197575 0.7842630 0.6754667
#> GSM795130 0.6969698 0.7246886 0.6918105 0.7166296 0.6869300 0.7023532
#> GSM795131 0.8147329 0.7763624 0.7574096 0.7927500 0.8018153 0.7170205
#> GSM795132 0.8230309 0.7941844 0.8282671 0.8206277 0.7633376 0.7678647
#> GSM795133 0.8056109 0.7160274 0.7820513 0.7607189 0.7460040 0.7299487
#> GSM795134 0.8005839 0.7687947 0.7313321 0.7932588 0.7624902 0.6912938
#> GSM795135 0.8211848 0.7648910 0.7628294 0.7471971 0.8132785 0.7239211
#> GSM795136 0.8300504 0.7810857 0.7913019 0.7991178 0.8251311 0.7511098
#> GSM795137 0.8587760 0.7804507 0.8038023 0.8577165 0.8108898 0.7602621
#> GSM795138 0.8002194 0.7034035 0.8319705 0.7682553 0.6937195 0.7664818
#> GSM795139 0.8270084 0.7586145 0.8161679 0.8274053 0.7489568 0.7590448
#> GSM795140 0.7933985 0.7389336 0.7483187 0.7500885 0.7424051 0.7910037
#> GSM795141 0.7731484 0.7635797 0.7294718 0.7620522 0.7301885 0.7523329
#> GSM795142 0.8253336 0.7671623 0.7719307 0.7722658 0.7750929 0.7547615
#> GSM795143 0.8292492 0.7820081 0.8109419 0.8308835 0.7762770 0.7338403
#> GSM795144 0.7899882 0.7265691 0.7603143 0.7572879 0.7434314 0.7813522