kevinblighe / e-mtab-6141 Goto Github PK

Hi Kevin,

Could you please provide the processed file having normalized count or RLD files for blood RNA-seq data. I am interested to look into correlation of few genes across samples.

Thanks
Gyan

hi, I am trying to repeat the code you supplied, but found 2 errors in windows R. thanks in advance

1 'Gene\nZ-\nscore' will give errors in windows, not like linux, windows does not support \n， maybe it connected with file name
2 one line code repeat in the following
# row (gene) parameters cluster_rows = TRUE, show_row_dend = TRUE, #row_title = 'Statistically significant genes', row_title_side = 'left', row_title_gp = gpar(fontsize = 12, fontface = 'bold'), row_title_rot = 90, show_row_names = FALSE, **row_names_gp = gpar(fontsize = 10, fontface = 'bold'),** row_names_side = 'left', row_dend_width = unit(25,'mm'),

and I found the gene names font size is so big, I change the row_title_gp = gpar(fontsize = 1, fontface = 'bold'), but does not make sense, how should I revise it?

and after change Gene\nZ-\nscore to Gene_Z--score, it outsided the plot area,

--Hi,
is your mat.tsv table normalized such as logCPM ?
thank you --

Hello Kevin,

Thank you for this tutorial that has been very useful (even 4 years later).

I have a question regarding the cluster distance metric you use, specifically regarding the difference between row and column distance.

You define the following:

clustering_distance_columns = function(x) as.dist(1 - cor(t(x))), clustering_method_columns = 'ward.D2', clustering_distance_rows = function(x) as.dist(1 - cor(t(x))), clustering_method_rows = 'ward.D2',

I understand that, for rows (genes), you use 1 - Pearson correlation of the transposed matrix.

But I see that you use the same formula for the column (sample) clustering.
In the case of columns, shouldn't it be the 1 - Pearson correlation of the matrix itself? e.g:

clustering_distance_columns = function(x) as.dist(1 - cor(x))

I'm new to the field of RNAseq analysis, so forgive me is the question is naive, but I cannot visualise what it means to use the distance of the rows as metrics for the column clustering.

Thank you for your insight, and have a good day.