Correlating the Matrices
56 - Matrix Synergy
Steven Roberts 11 September, 2023
- 1 Will Redo methylation to get all samples..
- 2 checking for consistency
- 3 checking for consistency
- 4 correlation
We have a few matrices comparing samples but the are not directly comparable.
Currently we have SNP data like this..
https://github.com/sr320/ceabigr/blob/main/output/53-revisit-epi-SNPs/epiMATRIX_mbd_rab.txt
Gene expression data like this..
Methylation like ..
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
## 151 36F 64M -0.1806566959
## 152 52F 35F -0.1802339811
## 153 53F 22F -0.1799292341
## 154 36F 29F 0.1797888406
## 155 22F 12M -0.1794617429
## 156 35F 50F -0.1788392453
## 157 16F 64M -0.1785873830
## 158 19F 53F -0.1765503140
## 159 64M 12M 0.1763392616
## 160 29F 54F -0.1756986180
## 161 52F 41F -0.1756243222
## 162 7M 52F -0.1744210746
## 163 77F 13M -0.1742491860
## 164 35F 41F -0.1736700502
## 165 44F 22F -0.1731985287
## 166 19F 35F -0.1725508345
## 167 64M 7M 0.1713948989
## 168 6M 12M 0.1705784197
## 169 41F 77F 0.1705737807
## 170 48M 12M 0.1689832554
## 171 76F 77F -0.1687281108
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Written on September 12, 2023