Correlating Matrices

ceabigr
R
tidyverse
methylation
snp
Author
Affiliation
Published

September 10, 2023

56 - Matrix Synergy

Steven Roberts 11 September, 2023

We have a few matrices comparing samples but the are not directly comparable.

Currently we have SNP data like this..

snp https://github.com/sr320/ceabigr/blob/main/output/53-revisit-epi-SNPs/epiMATRIX_mbd_rab.txt

Gene expression data like this..

gene Methylation like ..

meth

1 Will Redo methylation to get all samples..

1.1 sample metadata

Sample.ID OldSample.ID Treatment Sex TreatmentN Parent.ID
12M S12M Exposed M 3 EM05
13M S13M Control M 1 CM04
16F S16F Control F 2 CF05
19F S19F Control F 2 CF08
22F S22F Exposed F 4 EF02
23M S23M Exposed M 3 EM04
29F S29F Exposed F 4 EF07
31M S31M Exposed M 3 EM06
35F S35F Exposed F 4 EF08
36F S36F Exposed F 4 EF05
39F S39F Control F 2 CF06
3F S3F Exposed F 4 EF06
41F S41F Exposed F 4 EF03
44F S44F Control F 2 CF03
48M S48M Exposed M 3 EM03
50F S50F Exposed F 4 EF01
52F S52F Control F 2 CF07
53F S53F Control F 2 CF02
54F S54F Control F 2 CF01
59M S59M Exposed M 3 EM01
64M S64M Control M 1 CM05
6M S6M Control M 1 CM02
76F S76F Control F 2 CF04
77F S77F Exposed F 4 EF04
7M S7M Control M 1 CM01
9M S9M Exposed M 3 EM02
cd ../data/big

curl -O https://gannet.fish.washington.edu/seashell/bu-github/2018_L18-adult-methylation/analyses/myobj_oa
filtered.myobj=filterByCoverage(myobj_oa,lo.count=10,lo.perc=NULL,
                                      hi.count=NULL,hi.perc=98)

meth_filter=unite(filtered.myobj, min.per.group=NULL, destrand=TRUE)

clusterSamples(meth_filter, dist="correlation", method="ward", plot=TRUE)


PCASamples(meth_filter)

Laura’s code

perc.meth=percMethylation(meth_filter, rowids=T)
#Save % methylation df to object and .tab file 
save(perc.meth, file = "../output/56-matrix-synergy/all-perc.meth") #save object to file 
load(file = "../output/56-matrix-synergy/all-perc.meth") #load object if needed
#write.table((as.data.frame(perc.meth) %>% tibble::rownames_to_column("contig")), file = "../output/55-methylation-matrix/male-perc.meth.tab", sep = '\t', na = "NA", row.names = FALSE, col.names = TRUE)
perc.meth_T <- t(perc.meth)
correlationMatrix <- cor(perc.meth_T)
distanceMatrix <- dist(perc.meth_T)
# Convert distance matrix to a regular matrix
matrixForm <- as.matrix(distanceMatrix)

# Display the matrix
print(matrixForm)
##          12M      13M      16F      19F      22F      23M      29F      31M
## 12M     0.00 23304.01 30773.35 29629.70 30346.25 23098.58 31184.73 23129.98
## 13M 23304.01     0.00 31014.11 29879.93 30587.51 23344.65 30795.72 23356.35
## 16F 30773.35 31014.11     0.00 21908.45 22130.65 31511.83 21954.90 31138.59
## 19F 29629.70 29879.93 21908.45     0.00 21383.26 30272.65 21751.81 30048.86
## 22F 30346.25 30587.51 22130.65 21383.26     0.00 31126.69 21748.06 30843.14
## 23M 23098.58 23344.65 31511.83 30272.65 31126.69     0.00 31986.18 23189.74
## 29F 31184.73 30795.72 21954.90 21751.81 21748.06 31986.18     0.00 31192.70
## 31M 23129.98 23356.35 31138.59 30048.86 30843.14 23189.74 31192.70     0.00
## 35F 29283.62 29464.21 22231.28 21656.49 21939.90 29955.70 22065.75 29655.75
## 36F 29251.41 29474.21 22292.71 21511.49 21806.09 29935.49 22170.41 29648.06
## 39F 29579.97 29236.69 22057.29 21624.73 21906.64 30199.07 21423.26 29902.22
## 3F  31919.23 31853.28 22432.76 21911.40 22252.87 32447.18 21904.46 31683.66
## 41F 32063.83 32161.13 22808.30 22031.13 22128.82 32830.28 22217.91 32443.45
## 44F 31068.04 31178.57 21399.95 21324.27 21368.22 31540.12 20634.29 31412.10
## 48M 22942.88 22983.32 30990.10 29744.86 30423.65 22483.58 31359.26 23310.80
## 50F 30802.18 30925.90 22163.84 21635.20 21877.25 31438.81 21873.67 31145.29
## 52F 32363.67 32560.67 22081.44 22468.54 22479.71 33105.26 22489.10 32763.03
## 53F 29715.85 29916.97 22096.18 21609.50 21920.39 30529.01 21910.00 30078.30
## 54F 30263.18 30218.75 21707.26 21245.89 21462.58 30907.61 21081.23 30618.47
## 59M 23373.46 22750.43 31684.28 30506.55 31287.24 21719.67 31365.02 23638.96
## 64M 23109.80 23470.15 31088.76 29805.08 30607.09 23134.88 31270.04 23368.54
## 6M  22788.21 22821.71 31009.81 29855.33 30526.70 23003.41 31223.09 23359.32
## 76F 28416.24 28133.35 22821.71 21997.49 22735.05 28182.87 22293.72 28600.92
## 77F 29508.27 29423.39 22009.22 21394.53 21836.51 29941.71 21642.05 29756.03
## 7M  23029.43 23402.97 31265.45 30162.05 30724.11 23068.02 31741.13 23465.14
## 9M  22958.67 23244.69 31048.02 29908.63 30620.98 23214.31 31179.96 22945.08
##          35F      36F      39F       3F      41F      44F      48M      50F
## 12M 29283.62 29251.41 29579.97 31919.23 32063.83 31068.04 22942.88 30802.18
## 13M 29464.21 29474.21 29236.69 31853.28 32161.13 31178.57 22983.32 30925.90
## 16F 22231.28 22292.71 22057.29 22432.76 22808.30 21399.95 30990.10 22163.84
## 19F 21656.49 21511.49 21624.73 21911.40 22031.13 21324.27 29744.86 21635.20
## 22F 21939.90 21806.09 21906.64 22252.87 22128.82 21368.22 30423.65 21877.25
## 23M 29955.70 29935.49 30199.07 32447.18 32830.28 31540.12 22483.58 31438.81
## 29F 22065.75 22170.41 21423.26 21904.46 22217.91 20634.29 31359.26 21873.67
## 31M 29655.75 29648.06 29902.22 31683.66 32443.45 31412.10 23310.80 31145.29
## 35F     0.00 22013.48 21863.41 22476.44 23001.00 21806.94 29461.33 22206.57
## 36F 22013.48     0.00 21946.62 22265.72 22625.56 21727.24 29237.54 22039.75
## 39F 21863.41 21946.62     0.00 22223.98 22783.80 21444.18 29546.26 22094.86
## 3F  22476.44 22265.72 22223.98     0.00 22331.44 21401.95 31986.42 22021.11
## 41F 23001.00 22625.56 22783.80 22331.44     0.00 21778.71 32225.97 21921.02
## 44F 21806.94 21727.24 21444.18 21401.95 21778.71     0.00 31055.08 21422.94
## 48M 29461.33 29237.54 29546.26 31986.42 32225.97 31055.08     0.00 30972.88
## 50F 22206.57 22039.75 22094.86 22021.11 21921.02 21422.94 30972.88     0.00
## 52F 22885.74 22929.15 22604.14 22616.16 23005.97 21831.33 32573.71 22538.13
## 53F 21911.35 21903.17 21895.49 22009.51 22388.48 21503.49 29991.58 21826.59
## 54F 21600.18 21442.54 21312.10 21646.93 22041.85 20586.03 30095.88 21371.09
## 59M 30049.34 30070.87 30074.60 32655.96 33083.72 31343.95 22089.64 31665.59
## 64M 29515.73 29439.19 29731.60 31980.40 32138.93 31173.58 22999.28 30930.63
## 6M  29538.67 29424.15 29381.81 32092.12 32304.58 31281.94 22890.16 31055.19
## 76F 22279.71 22178.59 21720.06 22850.60 23668.90 21542.05 28105.05 22620.45
## 77F 21759.72 21715.88 21211.64 22177.69 22708.95 21159.08 29112.22 22171.96
## 7M  29702.59 29743.46 30021.30 32418.32 32604.45 31613.32 22890.79 31317.87
## 9M  29555.82 29228.70 29783.48 31724.89 32318.57 31117.51 22928.40 31126.25
##          52F      53F      54F      59M      64M       6M      76F      77F
## 12M 32363.67 29715.85 30263.18 23373.46 23109.80 22788.21 28416.24 29508.27
## 13M 32560.67 29916.97 30218.75 22750.43 23470.15 22821.71 28133.35 29423.39
## 16F 22081.44 22096.18 21707.26 31684.28 31088.76 31009.81 22821.71 22009.22
## 19F 22468.54 21609.50 21245.89 30506.55 29805.08 29855.33 21997.49 21394.53
## 22F 22479.71 21920.39 21462.58 31287.24 30607.09 30526.70 22735.05 21836.51
## 23M 33105.26 30529.01 30907.61 21719.67 23134.88 23003.41 28182.87 29941.71
## 29F 22489.10 21910.00 21081.23 31365.02 31270.04 31223.09 22293.72 21642.05
## 31M 32763.03 30078.30 30618.47 23638.96 23368.54 23359.32 28600.92 29756.03
## 35F 22885.74 21911.35 21600.18 30049.34 29515.73 29538.67 22279.71 21759.72
## 36F 22929.15 21903.17 21442.54 30070.87 29439.19 29424.15 22178.59 21715.88
## 39F 22604.14 21895.49 21312.10 30074.60 29731.60 29381.81 21720.06 21211.64
## 3F  22616.16 22009.51 21646.93 32655.96 31980.40 32092.12 22850.60 22177.69
## 41F 23005.97 22388.48 22041.85 33083.72 32138.93 32304.58 23668.90 22708.95
## 44F 21831.33 21503.49 20586.03 31343.95 31173.58 31281.94 21542.05 21159.08
## 48M 32573.71 29991.58 30095.88 22089.64 22999.28 22890.16 28105.05 29112.22
## 50F 22538.13 21826.59 21371.09 31665.59 30930.63 31055.19 22620.45 22171.96
## 52F     0.00 22619.24 22269.99 33291.68 32613.68 32578.74 23697.28 22652.05
## 53F 22619.24     0.00 20850.13 30789.88 29919.82 30063.84 22370.49 21719.54
## 54F 22269.99 20850.13     0.00 30695.49 30366.81 30475.49 21760.94 20823.23
## 59M 33291.68 30789.88 30695.49     0.00 23189.99 23272.60 27812.48 29895.81
## 64M 32613.68 29919.82 30366.81 23189.99     0.00 23192.01 28615.15 29621.81
## 6M  32578.74 30063.84 30475.49 23272.60 23192.01     0.00 28373.61 29473.32
## 76F 23697.28 22370.49 21760.94 27812.48 28615.15 28373.61     0.00 21258.78
## 77F 22652.05 21719.54 20823.23 29895.81 29621.81 29473.32 21258.78     0.00
## 7M  32871.65 30334.13 30679.36 23290.29 23120.35 22998.42 28973.02 29872.25
## 9M  32641.74 30154.49 30400.04 23362.47 23315.06 22998.20 28502.96 29486.18
##           7M       9M
## 12M 23029.43 22958.67
## 13M 23402.97 23244.69
## 16F 31265.45 31048.02
## 19F 30162.05 29908.63
## 22F 30724.11 30620.98
## 23M 23068.02 23214.31
## 29F 31741.13 31179.96
## 31M 23465.14 22945.08
## 35F 29702.59 29555.82
## 36F 29743.46 29228.70
## 39F 30021.30 29783.48
## 3F  32418.32 31724.89
## 41F 32604.45 32318.57
## 44F 31613.32 31117.51
## 48M 22890.79 22928.40
## 50F 31317.87 31126.25
## 52F 32871.65 32641.74
## 53F 30334.13 30154.49
## 54F 30679.36 30400.04
## 59M 23290.29 23362.47
## 64M 23120.35 23315.06
## 6M  22998.42 22998.20
## 76F 28973.02 28502.96
## 77F 29872.25 29486.18
## 7M      0.00 22970.83
## 9M  22970.83     0.00
heatmap(matrixForm, Rowv = NA, Colv = NA, col = cm.colors(256), scale = "none")
dataFrameForm <- as.data.frame(matrixForm)
print(dataFrameForm)
write.table((as.data.frame(matrixForm) %>% tibble::rownames_to_column("sample")), file = "../output/56-matrix-synergy/all.meth-distance.tab", sep = '\t', na = "NA", row.names = FALSE, col.names = TRUE)

2 checking for consistency

text file..

head -2 ../output/56-matrix-synergy/all.meth-distance.tab
## "sample" "12M"   "13M"   "16F"   "19F"   "22F"   "23M"   "29F"   "31M"   "35F"   "36F"   "39F"   "3F"    "41F"   "44F"   "48M"   "50F"   "52F"   "53F"   "54F"   "59M"   "64M"   "6M"    "76F"   "77F"   "7M"    "9M"
## "12M"    0   23304.0069449018    30773.3548066289    29629.7022439496    30346.249437145 23098.5830496124    31184.7314314988    23129.978377816 29283.6227675114    29251.4098948392    29579.9714132613    31919.2256764399    32063.8313572642    31068.0409488934    22942.8760393046    30802.1750112176    32363.6733837327    29715.84843474  30263.1784448872    23373.4562991821    23109.804844686 22788.2103589562    28416.2384891171    29508.2720313387    23029.4325530648    22958.6680752021
head -2 ../output/53-revisit-epi-SNPs/epiMATRIX_mbd_rab.txt
## "sample" "12M"   "13M"   "16F"   "19F"   "22F"   "23M"   "29F"   "31M"   "35F"   "36F"   "39F"   "3F"    "41F"   "44F"   "48M"   "50F"   "52F"   "53F"   "54F"   "59M"   "64M"   "6M"    "76F"   "77F"   "7M"    "9M"
## "12M"    0   0.051833    0.065087    0.074943    0.052083    0.056348    0.063667    0.057863    0.072606    0.067544    0.061042    0.042357    0.057715    0.067394    0.059371    0.055395    0.067694    0.060491    0.061507    0.050297    0.058001    0.059446    0.044517    0.058163    0.06416 0.055671

3 checking for consistency

matrices..

load(file = "../output/53-revisit-epi-SNPs/distrab") #load object if needed
str(matrixForm)
##  num [1:26, 1:26] 0 23304 30773 29630 30346 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:26] "12M" "13M" "16F" "19F" ...
##   ..$ : chr [1:26] "12M" "13M" "16F" "19F" ...
str(distrab)
##  num [1:26, 1:26] 0 0.0518 0.0651 0.0749 0.0521 ...
##  - attr(*, "dimnames")=List of 2
##   ..$ : chr [1:26] "12M" "13M" "16F" "19F" ...
##   ..$ : chr [1:26] "12M" "13M" "16F" "19F" ...

4 correlation

cor_matrix <- cor(matrixForm,distrab)
heatmap(cor_matrix)

# Create a data frame from the correlation matrix
cor_melted <- as.data.frame(as.table(cor_matrix))

# Create the heatmap
ggplot(data=cor_melted, aes(x=Var1, y=Var2)) +
  geom_tile(aes(fill=Freq), color='white') +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint=0) +
 # geom_text(aes(label=sprintf("%.2f", Freq)), vjust=1) +
  theme_minimal() +
  labs(fill="Correlation")

cor_long <- as.data.frame(as.table(cor_matrix))

cor_long_sorted <- cor_long %>%
  filter(Var1 != Var2) %>%
  arrange(desc(abs(Freq)))

print(cor_long_sorted)
##     Var1 Var2          Freq
## 1    59M  76F -0.5958548721
## 2    76F  59M -0.4731418012
## 3    59M  29F -0.4005978750
## 4    19F  12M -0.3859781485
## 5    39F   6M -0.3849202691
## 6    36F   9M -0.3785606877
## 7    13M  29F -0.3755641839
## 8    35F  12M -0.3625761112
## 9    52F   7M -0.3615916264
## 10   29F   9M -0.3607029960
## 11   39F  13M -0.3579498902
## 12   29F  13M -0.3568732686
## 13   19F   7M -0.3428635431
## 14   54F  53F -0.3418980839
## 15   59M  23M -0.3398098801
## 16    9M   3F -0.3333974660
## 17   52F  12M -0.3315183087
## 18   16F   7M -0.3248913238
## 19   36F   7M -0.3248006321
## 20   59M  22F  0.3232793764
## 21   44F  12M -0.3218638583
## 22   22F  76F  0.3214810844
## 23   44F   9M -0.3204748893
## 24   76F  23M -0.3197749738
## 25   23M  76F -0.3175789109
## 26   23M  59M -0.3135704981
## 27    9M  29F -0.3115060769
## 28   36F  12M -0.3089921957
## 29   77F  48M -0.3083561070
## 30   54F   7M -0.3032553477
## 31   16F  12M -0.3030487002
## 32   53F  76F  0.2978910887
## 33   41F  29F  0.2965878275
## 34   29F   6M -0.2938804443
## 35   29F  12M -0.2898694635
## 36   35F  22F -0.2847618833
## 37   77F   9M -0.2794185184
## 38    3F   9M -0.2785544158
## 39   31M   3F -0.2774884786
## 40    9M  31M -0.2774160775
## 41   59M  53F  0.2746245981
## 42   76F  44F -0.2741093729
## 43   35F   7M -0.2738426095
## 44   31M   7M  0.2726752287
## 45   22F  29F  0.2709407214
## 46   54F  12M -0.2682967258
## 47    6M  29F -0.2679065310
## 48   77F   6M -0.2665468348
## 49   52F  76F  0.2640992044
## 50   53F  64M -0.2640634566
## 51   44F   7M -0.2639337882
## 52   13M  39F -0.2634213535
## 53   31M  22F  0.2599438501
## 54   41F  76F  0.2588279247
## 55   59M  12M  0.2587169682
## 56   23M  44F -0.2577109631
## 57    3F  29F  0.2569075176
## 58   59M   9M  0.2569043984
## 59   48M  77F -0.2560015968
## 60   16F  22F -0.2552088338
## 61   50F   7M -0.2551453419
## 62   29F  64M -0.2547145634
## 63   50F  29F  0.2538060950
## 64   19F  76F  0.2502249560
## 65   48M  76F -0.2501274369
## 66   39F  12M -0.2492073243
## 67   16F  76F  0.2482472849
## 68   44F  23M -0.2478931437
## 69   53F  54F -0.2475069372
## 70   59M  50F  0.2456519174
## 71   53F  12M -0.2445990689
## 72   23M   9M  0.2439689854
## 73   76F  48M -0.2439305559
## 74   39F   7M -0.2428607944
## 75    3F  76F  0.2427587857
## 76   54F   9M -0.2413975900
## 77   77F  54F -0.2389313129
## 78   13M  12M  0.2386103231
## 79   41F  12M -0.2359274846
## 80   16F   9M -0.2355854762
## 81   54F  48M -0.2341688541
## 82   31M  29F -0.2338554430
## 83   54F  64M -0.2322243495
## 84   50F  22F -0.2314524686
## 85    6M  39F -0.2314375979
## 86   48M  59M -0.2312471418
## 87   19F  50F -0.2295367321
## 88   35F  76F  0.2274858923
## 89    6M   9M  0.2270109412
## 90   50F  41F -0.2269464285
## 91   59M  41F  0.2256083102
## 92   77F  12M -0.2230645743
## 93   59M  35F  0.2219517072
## 94   19F  29F  0.2219308112
## 95   29F  31M -0.2214621327
## 96   52F  22F -0.2205802874
## 97   77F   7M -0.2202637742
## 98   13M   7M  0.2202614432
## 99   53F  50F -0.2180172262
## 100  50F  76F  0.2177038358
## 101  44F  64M -0.2175488372
## 102  19F  41F -0.2164027684
## 103  36F  22F -0.2163403563
## 104  13M  22F  0.2159048560
## 105  41F  22F -0.2141623343
## 106  36F  76F  0.2111820243
## 107  41F  50F -0.2102510244
## 108  16F  29F  0.2102157220
## 109  53F  29F  0.2101917349
## 110  19F  22F -0.2097814983
## 111  50F  12M -0.2088308435
## 112   3F  59M  0.2083918364
## 113  29F   7M -0.2071118589
## 114  52F   9M -0.2070577612
## 115  52F  50F -0.2067840144
## 116  23M  12M  0.2066599757
## 117  35F  52F -0.2064845361
## 118  59M   3F  0.2064444678
## 119  54F  22F -0.2060258693
## 120  41F   7M -0.2057259139
## 121  59M   7M  0.2053226897
## 122   9M  36F -0.2052465017
## 123   9M  12M  0.2044519087
## 124  54F  13M -0.2042403342
## 125  23M   7M  0.2035478167
## 126   3F  39F  0.2034682631
## 127  36F  16F -0.2014138591
## 128   3F  77F  0.2002359695
## 129  12M  29F -0.1979862287
## 130  22F   7M -0.1973244447
## 131  13M  59M -0.1958749342
## 132  53F   7M -0.1947589303
## 133  35F   9M -0.1938199288
## 134  31M   6M  0.1936642564
## 135  19F   9M -0.1928992829
## 136  76F  53F  0.1926258238
## 137  13M  76F -0.1910019563
## 138  76F  13M -0.1905852670
## 139  64M   9M  0.1905081494
## 140  76F  19F  0.1898436853
## 141  64M  29F -0.1886187567
## 142  53F  41F -0.1885692611
## 143  59M  48M -0.1881857910
## 144  19F  52F -0.1865885922
## 145  52F  29F  0.1856616365
## 146  39F  64M -0.1852744587
## 147  31M  12M  0.1851114923
## 148  41F  64M -0.1830885907
## 149  36F  52F -0.1814249867
## 150  59M  44F -0.1807593073
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## 622  29F   3F  0.0093607010
## 623   7M  35F -0.0092290334
## 624  31M  64M  0.0088905242
## 625  16F  39F -0.0087855916
## 626  50F  16F -0.0085539542
## 627  50F  48M -0.0085355208
## 628  59M   6M  0.0078735859
## 629  76F  64M -0.0077920687
## 630  36F  44F  0.0075353405
## 631  31M  16F  0.0067545939
## 632  39F  53F -0.0061843433
## 633  53F  48M -0.0061656721
## 634  29F  16F -0.0061502518
## 635  23M  50F  0.0059206014
## 636  50F  54F  0.0054048597
## 637  31M  23M  0.0048394490
## 638  31M  77F -0.0047653051
## 639  77F  53F  0.0044374828
## 640  16F  23M -0.0042830154
## 641  19F  77F  0.0040145572
## 642   3F  23M -0.0040023851
## 643  23M  31M  0.0038292279
## 644  23M  52F -0.0036669821
## 645  48M  19F -0.0031344337
## 646  64M  16F  0.0024176264
## 647   6M  48M  0.0017065403
## 648  44F  76F -0.0016070404
## 649  22F  48M -0.0010679395
## 650  29F  48M  0.0006227683

Citation

BibTeX citation:
@online{roberts2023,
  author = {Roberts, Steven},
  title = {Correlating {Matrices}},
  date = {2023-09-10},
  url = {https://sr320.github.io/tumbling-oysters/posts/sr320-05-matrix/},
  langid = {en}
}
For attribution, please cite this work as:
Roberts, Steven. 2023. “Correlating Matrices.” September 10, 2023. https://sr320.github.io/tumbling-oysters/posts/sr320-05-matrix/.