Hi @mtoufiq,
Here is some code to read your data matrix and reshape it into a new data.frame. I wasn't able to produce your desired output since its not clear to me how you want the data "summarized" or subsetted.
Adding annotation text to the left-hand side of heatmap() output is not easy - other forum members may have some ideas.
suppressPackageStartupMessages(library(tidyverse))
input.df <- structure(
list(
'E0 Compound 1' = c(
0.028643405,
-0.170597892,
-0.133431392,
0.362602406,
0.65949239,
0.129862499,
0.308021483,
0.191411185,
-0.408706956,
0.148431726
),
'E0 Compound 2' = c(
0.312575401,
0.08023047,
-0.952555759,
-0.087364689,
2.492343411,
0.594530215,
1.257403638,
-0.005561728,
-0.372470805,
0.019060103
),
'E0 Compound 3' = c(
-1.088143295,
-0.282639558,
1.283875318,
0.436371912,
7.875801388,
3.888644506,
4.851154755,
2.112242014,
0.35468163,
2.231527721
),
'P0 Compound 1' = c(
0.0487268,
-0.00328775,
2.07844595,
-0.014618298,
4.735091392,
-0.220327528,
2.872283004,
0.77093127,
0.168573042,
1.237777012
),
'P0 Compound 2' = c(
-1.125845192,
-0.801624332,
1.669743718,
0.415204306,
4.986942824,
1.496006014,
3.336380665,
1.162817817,
1.491530314,
1.484104864
),
'P0 Compound 3' = c(
2.082646448,
2.708726878,
1.256777456,
-0.491426149,
2.459378121,
2.981105751,
0.974149318,
-0.222540059,
-1.407150779,
-0.147072039
),
'R0 Compound 1' = c(
-0.326900367,
-0.362728527,
0.509587017,
-0.165210608,
-0.283082038,
0.753676032,
0.360670113,
0.037390082,
0.103973162,
0.311012902
),
'R0 Compound 2' = c(
-2.793220245,
-0.826729075,
2.509040525,
-0.344938067,
2.868355513,
-1.025542457,
1.875640888,
0.096367013,
0.233463623,
0.646241163
),
'R0 Compound 3' = c(
-0.415009733,
0.089591797,
1.245990797,
1.529969694,
1.696672088,
1.578365108,
2.218401723,
1.391261115,
-1.716869822,
1.869097396
)
),
class = "data.frame",
row.names = c(
"Feature_1",
"Feature_2",
"Feature_3",
"Feature_4",
"Feature_5",
"Feature_6",
"Feature_7",
"Feature_8",
"Feature_9",
"Feature_10"))
input.df
#> E0 Compound 1 E0 Compound 2 E0 Compound 3 P0 Compound 1
#> Feature_1 0.02864341 0.312575401 -1.0881433 0.04872680
#> Feature_2 -0.17059789 0.080230470 -0.2826396 -0.00328775
#> Feature_3 -0.13343139 -0.952555759 1.2838753 2.07844595
#> Feature_4 0.36260241 -0.087364689 0.4363719 -0.01461830
#> Feature_5 0.65949239 2.492343411 7.8758014 4.73509139
#> Feature_6 0.12986250 0.594530215 3.8886445 -0.22032753
#> Feature_7 0.30802148 1.257403638 4.8511548 2.87228300
#> Feature_8 0.19141119 -0.005561728 2.1122420 0.77093127
#> Feature_9 -0.40870696 -0.372470805 0.3546816 0.16857304
#> Feature_10 0.14843173 0.019060103 2.2315277 1.23777701
#> P0 Compound 2 P0 Compound 3 R0 Compound 1 R0 Compound 2
#> Feature_1 -1.1258452 2.0826464 -0.32690037 -2.79322025
#> Feature_2 -0.8016243 2.7087269 -0.36272853 -0.82672908
#> Feature_3 1.6697437 1.2567775 0.50958702 2.50904053
#> Feature_4 0.4152043 -0.4914261 -0.16521061 -0.34493807
#> Feature_5 4.9869428 2.4593781 -0.28308204 2.86835551
#> Feature_6 1.4960060 2.9811058 0.75367603 -1.02554246
#> Feature_7 3.3363807 0.9741493 0.36067011 1.87564089
#> Feature_8 1.1628178 -0.2225401 0.03739008 0.09636701
#> Feature_9 1.4915303 -1.4071508 0.10397316 0.23346362
#> Feature_10 1.4841049 -0.1470720 0.31101290 0.64624116
#> R0 Compound 3
#> Feature_1 -0.4150097
#> Feature_2 0.0895918
#> Feature_3 1.2459908
#> Feature_4 1.5299697
#> Feature_5 1.6966721
#> Feature_6 1.5783651
#> Feature_7 2.2184017
#> Feature_8 1.3912611
#> Feature_9 -1.7168698
#> Feature_10 1.8690974
# To get close to image posted
heatmap(as.matrix(input.df), Rowv=NA, Colv=NA, revC=TRUE, cexRow=0.7, cexCol=0.6)
input.df %>%
rownames_to_column("Feature") %>%
pivot_longer(cols=contains("Compound")) %>%
mutate(Feature = as.factor(Feature),
Subject = as.factor(str_sub(name, start=1, end=2)),
Compound = as.factor(str_sub(name, start=4, end=13)),
Compound = as.factor(str_replace(Compound, " ", "_"))) %>%
select(-name) -> new.df
new.df
#> # A tibble: 90 × 4
#> Feature value Subject Compound
#> <fct> <dbl> <fct> <fct>
#> 1 Feature_1 0.0286 E0 Compound_1
#> 2 Feature_1 0.313 E0 Compound_2
#> 3 Feature_1 -1.09 E0 Compound_3
#> 4 Feature_1 0.0487 P0 Compound_1
#> 5 Feature_1 -1.13 P0 Compound_2
#> 6 Feature_1 2.08 P0 Compound_3
#> 7 Feature_1 -0.327 R0 Compound_1
#> 8 Feature_1 -2.79 R0 Compound_2
#> 9 Feature_1 -0.415 R0 Compound_3
#> 10 Feature_2 -0.171 E0 Compound_1
#> # … with 80 more rows
str(new.df)
#> tibble [90 × 4] (S3: tbl_df/tbl/data.frame)
#> $ Feature : Factor w/ 10 levels "Feature_1","Feature_10",..: 1 1 1 1 1 1 1 1 1 3 ...
#> $ value : num [1:90] 0.0286 0.3126 -1.0881 0.0487 -1.1258 ...
#> $ Subject : Factor w/ 3 levels "E0","P0","R0": 1 1 1 2 2 2 3 3 3 1 ...
#> $ Compound: Factor w/ 3 levels "Compound_1","Compound_2",..: 1 2 3 1 2 3 1 2 3 1 ...
Created on 2022-06-04 by the reprex package (v2.0.1)
Hope this helps.