I'm trying to plot the interaction between "condition" and "snarc_num" in the model (built with lme4 package):
quant_num<-lmer(response_time1~condition*snarc_num+(1+condition|subject_nr) + (1|couple), data=quant_slopes)
To plot the interaction I should use the plotLMER.fnc from the languageR package, and I tried:
plotLMER.fnc(quant_num, xlabel = "snarc_num", ylabel ="RTs", ylimit = c(6.5,7.5),
fun = NA, pred = "snarc_num", control =list(quant_slopes$condition, quant_slopes$snarc_num), ranefs = NA, n = 100,
intr =list("condition","snarc_num"), lockYlim = TRUE, addlines = TRUE,
withList = FALSE, cexsize = 0.5, linecolor = 2, addToExistingPlot = FALSE,
verbose = TRUE)
but it doesn't work.
Could anyone help me with this plot?
Thank you!
I made the heatmap using the code below:
library(pheatmap)
library(dplyr)
data = data.frame(matrix(runif(10*10), ncol=10))
data$sample = rep(c("tumour", "normal"), 5)
data$subject.ID = paste('Subject', 1:10)
data = data %>% arrange(sample)
# for row annotation
my_sample_col = data %>% select(sample)
rownames(my_sample_col) = data$subject.ID
# data matrix
mat = as.matrix(data %>% select(-sample, -subject.ID))
rownames(mat) = data$subject.ID
pheatmap(mat,
scale='row',
annotation_row = my_sample_col,
annotation_names_row=F,
cluster_rows = FALSE,
cluster_cols = FALSE,
show_colnames = FALSE,
show_rownames = FALSE)
I want to put a gap between row 5 and row 6, to separate the heatmap according to my row annotation.
In pheatmap function, the argument gaps_row seems to do the job.
vector of row indices that show shere to put gaps into heatmap. Used only if the rows are not clustered.
I'm not sure how to implement that. Can someone help me with this? Thanks a lot.
I would recommend using ComplexHeatmap package (website; Gu et al, 2016). You can install it with devtools::install_github("jokergoo/ComplexHeatmap").
It has more functionalities, but you also have to invest more time (eg., row annotation and matrix scaling).
library(ComplexHeatmap)
# Create annotation for rows
my_sample_col_ano <- rowAnnotation(sample = my_sample_col$sample,
show_annotation_name = FALSE)
# Scale original matrix row-wise
matS <- t(apply(mat, 1, scale))
# Plot heatmap
Heatmap(matS,
# Remove name from fill legend
name = "",
# Keep original row/col order
row_order = rownames(matS), column_order = colnames(matS),
# Add left annotation (legend with tumor/normal)
left_annotation = my_sample_col_ano,
# ACTUAL SPLIT by sample group
row_split = my_sample_col$sample,
show_row_names = FALSE, show_column_names = FALSE,
show_row_dend = FALSE, show_column_dend = FALSE,
row_title = NULL)
If you want to use original pheatmap pass argument to gaps_row which is equal to the size of your group (ie, normal):
pheatmap(mat,
scale='row',
gaps_row = 5,
annotation_row = my_sample_col,
annotation_names_row=F,
cluster_rows = FALSE,
cluster_cols = FALSE,
show_colnames = FALSE,
show_rownames = FALSE)
If you can more groups than two instead of hardcoding numeric value to gaps_row (ie, gaps_row = 5) you can pass this snippet (head(as.numeric(cumsum(table(my_sample_col$sample))), -1)).
I wanna plot a heatmap and cluster only the rows (i.e. genes in this tydf1).
Also, wanna keep order of the heatmap's column labels as same as in the df (i.e. tydf1)?
Sample data
df1 <- structure(list(Gene = c("AA", "PQ", "XY", "UBQ"), X_T0_R1 = c(1.46559502, 0.220140568, 0.304127515, 1.098842127), X_T0_R2 = c(1.087642983, 0.237500819, 0.319844338, 1.256624804), X_T0_R3 = c(1.424945196, 0.21066267, 0.256496284, 1.467120048), X_T1_R1 = c(1.289943948, 0.207778662, 0.277942721, 1.238400358), X_T1_R2 = c(1.376535013, 0.488774258, 0.362562315, 0.671502431), X_T1_R3 = c(1.833390311, 0.182798731, 0.332856558, 1.448757569), X_T2_R1 = c(1.450753714, 0.247576125, 0.274415259, 1.035410946), X_T2_R2 = c(1.3094609, 0.390028842, 0.352460646, 0.946426593), X_T2_R3 = c(0.5953716, 1.007079177, 1.912258811, 0.827119776), X_T3_R1 = c(0.7906009, 0.730242116, 1.235644748, 0.832287694), X_T3_R2 = c(1.215333041, 1.012914813, 1.086362205, 1.00918082), X_T3_R3 = c(1.069312467, 0.780421013, 1.002313082, 1.031761442), Y_T0_R1 = c(0.053317766, 3.316414959, 3.617213894, 0.788193798), Y_T0_R2 = c(0.506623748, 3.599442788, 1.734075583, 1.179462912), Y_T0_R3 = c(0.713670106, 2.516735845, 1.236204882, 1.075393433), Y_T1_R1 = c(0.740998252, 1.444496448, 1.077023349, 0.869258744), Y_T1_R2 = c(0.648231834, 0.097957459, 0.791438659, 0.428805547), Y_T1_R3 = c(0.780499252, 0.187840968, 0.820430227, 0.51636582), Y_T2_R1 = c(0.35344654, 1.190274584, 0.401845911, 1.223534348), Y_T2_R2 = c(0.220223951, 1.367784148, 0.362815405, 1.102117612), Y_T2_R3 = c(0.432856978, 1.403057729, 0.10802472, 1.304233845), Y_T3_R1 = c(0.234963735, 1.232129062, 0.072433381, 1.203096462), Y_T3_R2 = c(0.353770497, 0.885122768, 0.011662112, 1.188149743), Y_T3_R3 = c(0.396091395, 1.333921747, 0.192594116, 1.838029829), Z_T0_R1 = c(0.398000559, 1.286528398, 0.129147097, 1.452769794), Z_T0_R2 = c(0.384759325, 1.122251177, 0.119475721, 1.385513609), Z_T0_R3 = c(1.582230097, 0.697419716, 2.406671502, 0.477415567), Z_T1_R1 = c(1.136843842, 0.804552001, 2.13213228, 0.989075996), Z_T1_R2 = c(1.275683837, 1.227821594, 0.31900326, 0.835941568), Z_T1_R3 = c(0.963349308, 0.968589683, 1.706670339, 0.807060135), Z_T2_R1 = c(3.765036263, 0.477443352, 1.712841882, 0.469173869), Z_T2_R2 = c(1.901023385, 0.832736132, 2.223429427, 0.593558769), Z_T2_R3 = c(1.407713024, 0.911920317, 2.011259223, 0.692553388), Z_T3_R1 = c(0.988333629, 1.095130142, 1.648598854, 0.629915612), Z_T3_R2 = c(0.618606729, 0.497458337, 0.549147265, 1.249492088), Z_T3_R3 = c(0.429823986, 0.471389536, 0.977124788, 1.136635484)), row.names = c(NA, -4L ), class = c("data.table", "data.frame"))
Scripts used
library(dplyr)
library(stringr)
library(tidyr)
gdf1 <- gather(df1, "group", "Expression", -Gene)
gdf1$tgroup <- apply(str_split_fixed(gdf1$group, "_", 3)[, c(1, 2)],
1, paste, collapse ="_")
library(dplyr)
tydf1 <- gdf1 %>%
group_by(Gene, tgroup) %>%
summarize(expression_mean = mean(Expression)) %>%
spread(., tgroup, expression_mean)
#1 heatmap script is being used
library(tidyverse)
tydf1 <- tydf1 %>%
as.data.frame() %>%
column_to_rownames(var=colnames(tydf1)[1])
library(gplots)
library(vegan)
randup.m <- as.matrix(tydf1)
scaleRYG <- colorRampPalette(c("red","yellow","darkgreen"),
space = "rgb")(30)
data.dist <- vegdist(randup.m, method = "euclidean")
row.clus <- hclust(data.dist, "aver")
heatmap.2(randup.m, Rowv = as.dendrogram(row.clus),
dendrogram = "row", col = scaleRYG, margins = c(7,10),
density.info = "none", trace = "none", lhei = c(2,6),
colsep = 1:3, sepcolor = "black", sepwidth = c(0.001,0.0001),
xlab = "Identifier", ylab = "Rows")
#2 heatmap script is being used
df2 <- as.matrix(tydf1[, -1])
heatmap(df2)
Also, I want to add a color key.
It is still unclear to me, what the desired output is. There are some notes:
You don't need to use vegdist() to calculate distance matrix for your hclust() call. Because if you check all(vegdist(randup.m, method = "euclidian") == dist(randup.m)) it returns TRUE;
Specifying Colv = F in your heatmap.2() call will prevent reordering of the columns (default is TRUE);
Maybe it is better to scale your data by row (see the uncommented row);
Your call of heatmap.2() returns the heatmap with color key.
So summing it up - in your first script you just miss the Colv = F argument, and after a little adjustment it looks like this:
heatmap.2(randup.m,
Rowv = as.dendrogram(row.clus),
Colv = F,
dendrogram = "row",
#scale = "row",
col = scaleRYG,
density.info = "none",
trace = "none",
srtCol = -45,
adjCol = c(.1, .5),
xlab = "Identifier",
ylab = "Rows"
)
However I am still not sure - is it what you need?
I am plotting a time series with the timePlot function of the open air package of R. The graph has grey grid lines in the background that I would like to turn off but I do not find a way to do it. I would expect something simple such as grid = FALSE, but that is not the case. It appears to be rather complex, requiring the use of extra arguments which are passed to xyplot of the library lattice. I believe the answer lies some where in the par.settings function but all attempts have failed. Does anyone have any suggestions to this issue?
Here is by script:
timeozone <- import(i, date="date", date.format = "%m/%d/%Y", header=TRUE, na.strings="")
ROMO = timePlot(timeozone, pollutant = c("C7", "C9", "C10"), group = TRUE, stack = FALSE,y.relation = "same", date.breaks = 9, lty = c(1,2,3), lwd = c(2, 3, 3), fontsize = 15, cols = c("black", "black"), ylab = "Ozone (ppbv)")
panel = function(x, y) {
panel.grid(h = 0, v = 0)
panel.xyplot(x,y)
}