How to sort data from heatmap columns, or sort them?

OSDIAZ · February 17, 2022, 9:21pm

Hello everyone, I hope you are very well. I made a heatmap, which I really liked how my data is displayed. However, my data (pathways) appears out of order. Is there any way to classify certain data within the heatmap?

For example, P-starvation to P-uptake belong to phosphorus metabolism.

Cysteine to Assimilatory belong to sulfur metabolism, and Denitrification to Nitrification belong to nitrogen metabolism.

Could the data sort them, or could a dividing line between each metabolism?

I would like to do that in the last heatmap, blue with yellow and red.

Thanks!

# Primero creo una nueva Data a partir de tu Data original.
# Primero debes llmar a tu data original con tu script anterior.
Data <- data.frame (tibble::tribble(
                                 ~Pathways,  ~July, ~August, ~September,
                            "P-starvation", 10462L,   7798L,      8853L,
                             "Inorganic-P",  4419L,   3169L,      3742L,
                               "Organic-P", 33916L,  24480L,     28117L,
                                "P-uptake", 44204L,  33561L,     36611L,
                                "Cysteine",  9247L,   7030L,      7551L,
                                     "Sox",  8780L,   6351L,      7816L,
                            "Dissmilatory",   784L,    607L,       576L,
                            "Assimilatory", 30984L,  22860L,     24885L,
                         "Denitrification",  7303L,   5030L,      5414L,
                       "Nitrogen_Fixation",   561L,    411L,       587L,
                                "COMAMMOX",  3566L,   2455L,      2604L,
                          "Dissimilatoria",  8378L,   5726L,      6008L,
                           "Assimilatoria",  3655L,   1967L,      2524L,
                           "Nitrification",   144L,    107L,       115L
                       )
)
Data_2 <- Data
head(Data_2)
#>       Pathways  July August September
#> 1 P-starvation 10462   7798      8853
#> 2  Inorganic-P  4419   3169      3742
#> 3    Organic-P 33916  24480     28117
#> 4     P-uptake 44204  33561     36611
#> 5     Cysteine  9247   7030      7551
#> 6          Sox  8780   6351      7816

rownames(Data_2) <- Data_2$Pathways 
Data_2 <- Data_2[-1]
class(Data_2)
#> [1] "data.frame"

MiTabla<- as.matrix(Data_2)
MiTabla
#>                    July August September
#> P-starvation      10462   7798      8853
#> Inorganic-P        4419   3169      3742
#> Organic-P         33916  24480     28117
#> P-uptake          44204  33561     36611
#> Cysteine           9247   7030      7551
#> Sox                8780   6351      7816
#> Dissmilatory        784    607       576
#> Assimilatory      30984  22860     24885
#> Denitrification    7303   5030      5414
#> Nitrogen_Fixation   561    411       587
#> COMAMMOX           3566   2455      2604
#> Dissimilatoria     8378   5726      6008
#> Assimilatoria      3655   1967      2524
#> Nitrification       144    107       115

MisDistancias<- dist(MiTabla, method = "euclidean")

MisDistancias
#>                   P-starvation Inorganic-P  Organic-P   P-uptake   Cysteine
#> Inorganic-P          9168.8500                                             
#> Organic-P           34633.5233  43799.2278                                 
#> P-uptake            50722.3944  59890.6591 16138.6350                      
#> Cysteine             1939.3950   7261.2248 36551.6404 52634.2988           
#> Sox                  2449.1431   6763.2035 37048.7481 53145.0459   865.6529
#> Dissmilatory        14624.7357   5458.9949 49256.0274 65343.5822 12709.3636
#> Assimilatory        30083.9052  39248.7090  4654.7662 20658.6272 31993.0246
#> Denitrification      5428.4276   3817.9001 40024.7483 56115.5466  3513.6740
#> Nitrogen_Fixation   14863.5233   5696.0296 49495.1885 65584.7736 12952.0289
#> COMAMMOX            10730.9117   1591.3670 45355.6644 61445.1489  8813.4667
#> Dissimilatoria       4090.2647   5229.4059 38635.6033 54725.0684  2199.1876
#> Assimilatoria       10972.3312   1874.0395 45580.3131 61679.2170  9065.0517
#> Nitrification       15555.2322   6388.0042 50186.6827 66275.2724 13641.3575
#>                          Sox Dissmilatory Assimilatory Denitrification
#> Inorganic-P                                                           
#> Organic-P                                                             
#> P-uptake                                                              
#> Cysteine                                                              
#> Sox                                                                   
#> Dissmilatory      12220.7672                                          
#> Assimilatory      32510.2362   44700.8220                             
#> Denitrification    3113.8680    9244.8112   35465.7370                
#> Nitrogen_Fixation 12453.6742     297.0959   44943.4015       9491.5570
#> COMAMMOX           8338.4385    3907.3536   40798.9425       5337.7799
#> Dissimilatoria     1954.7616   10647.9867   34072.6624       1411.6930
#> Assimilatoria      8572.6510    3726.5192   41029.3311       5571.5324
#> Nitrification     13148.1342     933.8742   45632.8556      10176.7682
#>                   Nitrogen_Fixation   COMAMMOX Dissimilatoria Assimilatoria
#> Inorganic-P                                                                
#> Organic-P                                                                  
#> P-uptake                                                                   
#> Cysteine                                                                   
#> Sox                                                                        
#> Dissmilatory                                                               
#> Assimilatory                                                               
#> Denitrification                                                            
#> Nitrogen_Fixation                                                          
#> COMAMMOX                  4156.4709                                        
#> Dissimilatoria           10896.8782  6741.0682                             
#> Assimilatoria             3968.1155   502.4590      6969.5815              
#> Nitrification              699.3490  4839.2467     11580.1281     4646.5043

MiClusteringJerargico<- hclust(MisDistancias, method = "complete")

MiClusteringJerargico
#> 
#> Call:
#> hclust(d = MisDistancias, method = "complete")
#> 
#> Cluster method   : complete 
#> Distance         : euclidean 
#> Number of objects: 14

plot(MiClusteringJerargico)


MiTablaEstandarizada<- t(scale(t(MiTabla)))

# Heatmap con datos Estandarizados:

heatmap(MiTablaEstandarizada, Colv = NA, cexCol=1)
library(pheatmap)

pheatmap(MiTablaEstandarizada, display_numbers = T)

^{Created on 2022-02-17 by the reprex package (v2.0.1)}

AlexisW · February 20, 2022, 12:13am

Thank you for the great reprex!

Does this do what you want?

pheatmap(MiTablaEstandarizada,
         display_numbers = T,
         cluster_rows = FALSE,
         gaps_row = c(4,8))

I used cluster_rows = FALSE to keep the original order (prevent reordering), and then use gaps_row to manually set the gaps.

In addition, you can add a color code for the metabolism:

annot_df <- data.frame(row.names = rownames(MiTablaEstandarizada),
                       Metabolism = c(rep("phosphorous", 4),
                                      rep("sulfur", 4),
                                      rep("nitrogen", 6)))

pheatmap(MiTablaEstandarizada,
         display_numbers = T,
         cluster_rows = FALSE,
         gaps_row = c(4,8),
         annotation_row = annot_df)

OSDIAZ · February 24, 2022, 4:50pm

Thank you so much!!
It is exactly what I wanted to get.

OSDIAZ · March 2, 2022, 8:24pm

AlexisW:

annot_df <- data.frame(row.names = rownames(MiTablaEstandarizada),
                       Metabolism = c(rep("phosphorous", 4),
                                      rep("sulfur", 4),
                                      rep("nitrogen", 6)))

pheatmap(MiTablaEstandarizada,
         display_numbers = T,
         cluster_rows = FALSE,
         gaps_row = c(4,8),
         annotation_row = annot_df)

Excuse me, how could I order the information (legend) of the mechanisms on the upper right side? In the heatmap the phosphorus results are presented first, but in the legend this is the second data.

Thank you

AlexisW · March 8, 2022, 1:05am

For the values of Metabolism to be ordered, that column needs to be a factor, rather than a character vector.

You can create it with the function as.factor(), but I find it easier to use fct_inorder() in the package {forcats} (part of the tidyverse).

annot_df <- data.frame(row.names = rownames(MiTablaEstandarizada),
                       Metabolism = c(rep("phosphorous", 4),
                                      rep("sulfur", 4),
                                      rep("nitrogen", 6)))

annot_df$Metabolism <- fct_inorder(annot_df$Metabolism)

pheatmap(MiTablaEstandarizada,
         display_numbers = T,
         cluster_rows = FALSE,
         gaps_row = c(4,8),
         annotation_row = annot_df)

system · March 15, 2022, 1:05am

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