Remove NAs from dataframe generated with full_join from two scRNA-seq dataframes with equal nrow values and rownames

Question

I've been working with a Log2 dataframe which looks like this:

library(dplyr)

str(df[1:10])

 $ 5W_Female_C#1_1    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_2    : num  2.28 0 0 0 0 ...
 $ 5W_Female_C#1_3    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_4    : num  2.15 0 0 1.79 0 ...
 $ 5W_Female_C#1_5    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_6    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_7    : num  0 0 0 1.41 0 ...
 $ 5W_Female_C#1_8    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_9    : num  0 0 0 0 0 ...
 $ 5W_Female_C#1_10   : num  0.18 0 0.18 0 0 ...

     dput(df[1:10,1:20])
structure(list(`5W_Female_C#1_1` = c(0, 0, 0, 0, 0, 0, 0, 1.23695175858808, 
2.08983709011962, 1.78366618090783), `5W_Female_C#1_2` = c(2.28362550060704, 
0, 0, 0, 0, 0.417920007811965, 0, 0, 4.23488447596799, 0), `5W_Female_C#1_3` = c(0, 
0, 0, 0, 0, 0, 0, 1.49722912878761, 2.95084163754915, 0), `5W_Female_C#1_4` = c(2.15088457130503, 
0, 0, 1.78993786898019, 0, 0.219091058246197, 0, 0, 3.48000655138599, 
0), `5W_Female_C#1_5` = c(0, 0, 0, 0, 0, 0, 0, 1.77610398807316, 
2.50182126542091, 0), `5W_Female_C#1_6` = c(0, 0, 0, 0, 0, 
0, 0, 3.01506932171765, 2.76107247078864, 1.42115596066222), 
    `5W_Female_C#1_7` = c(0, 0, 0, 1.40544784370754, 0, 0, 
    0, 1.12300395405482, 2.88009774972197, 0), `5W_Female_C#1_8` = c(0, 
    0, 0, 0, 0, 2.31875066934634, 0, 2.92257845650856, 3.34695688937888, 
    1.48284828306847), `5W_Female_C#1_9` = c(0, 0, 0, 0, 0, 
    0, 0, 1.61917821605907, 1.77273024776718, 2.09761079662642
    ), `5W_Female_C#1_10` = c(0.180147861158429, 0, 0.180147861158429, 
    0, 0, 0, 0, 0.180147861158429, 3.75103517666786, 0), `5W_Female_C#1_11` = c(0, 
    0, 0, 0.336854639125465, 0, 0, 0, 0, 2.7614980445501, 0), 
    `5W_Female_C#1_12` = c(0, 0, 0, 0, 0, 0, 0, 1.04404433270602, 
    3.39985467357243, 0), `5W_Female_C#1_13` = c(0, 0, 0, 0, 
    0, 0, 0, 0, 3.29484127140614, 1.12101540096137), `5W_Female_FGC#1_14` = c(0, 
    0, 0, 0, 0, 0, 0, 0, 3.00934717225925, 0), `5W_Female_C#1_15` = c(0.207892851641333, 
    0, 0, 0, 0, 1.26243315763135, 0, 0, 1.98294795515753, 0.829443681366591
    ), `5W_Female_C#1_16` = c(0, 0, 0, 1.15639661659767, 0, 
    0, 0, 1.8611613657534, 3.03509599789673, 0), `5W_Female_C#1_17` = c(0, 
    0, 0, 1.57385922157595, 0, 0, 0, 4.02874594222712, 0, 3.91513842592446
    ), `5W_Female_C#1_18` = c(0, 0, 0, 0, 0, 0, 0, 2.45180455572049, 
    3.72628628972067, 0), `5W_Female_C#1_19` = c(0.702214251010441, 
    0, 0, 0, 0, 0, 0, 1.41792000781196, 2.63853727921519, 1.17248751551013
    ), `5W_Female_C#1_20` = c(3.1243281350022, 0, 0, 0, 0, 
    0, 0, 0, 3.07963411236871, 0)), row.names = c("A1BG", "A1BG-AS1", 
"A1CF", "A2M", "A2M-AS1", "A2ML1", "A2MP1", "A4GALT", "AAAS", 
"AACS"), class = "data.frame")

Small window of data

This has been subsetted according to a list of genes/gene vector.

gene_list <- c("gene1","gene2","gene3","gene4","gene5")

This was followed by a subsequent subsetting according to age using the grep function. scdata4 <- as.data.frame(df[,grep("4W", colnames(df))]) scdata5 <- as.data.frame(df[,grep("5W", colnames(df))])

After this step, the row names(genes) were put under a column called genes using:

tibble::rownames_to_column(df, var="gene")

Finally, two of the dataframes generated were given as input for the full_join function and they have the same nrow value, rownames.

scdatajoin <- full_join(scdata4,scdata5, by = "gene")

And here is where I get errors, when I insert this output into pheatmap function after converting to matrix format with as.matrix().

scdatajoin <- as.matrix(scdatajoin) pheatmap(scdatajoin, color=rev(brewer.pal(9,"RdBu")), main = "4plus5w")

I get this error:

Error in hclust(d, method = method) : NA/NaN/Inf in foreign function call (arg 11)

Can somebody tell me how to correct this?

Answer 1

After removing one gene that despite being present in the Dataset, was filled with NAs instead of numerical values, i wass able to run the function. I realised this by using the heatmap.2 function from the package gplots , where i could visualize the absence of values on the measurements of this gene.