From 7c02d167c8ed81adc33080a53260699801c7f495 Mon Sep 17 00:00:00 2001 From: almeidasilvaf <61153286+almeidasilvaf@users.noreply.github.com> Date: Fri, 3 Nov 2023 20:09:08 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20almeidas?= =?UTF-8?q?ilvaf/HybridExpress@a2151b02d52b8f1ba49b37ee3505ee9e380d1bde=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- articles/HybridExpress.html | 221 ++++++++++++------ .../figure-html/unnamed-chunk-11-1.png | Bin 58317 -> 58648 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 70140 -> 70508 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 259437 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 0 -> 396824 bytes .../figure-html/unnamed-chunk-16-1.png | Bin 0 -> 147405 bytes .../figure-html/unnamed-chunk-16-2.png | Bin 0 -> 121766 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 55799 -> 56706 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 108323 -> 108077 bytes pkgdown.yml | 2 +- reference/pca_plot-1.png | Bin 56796 -> 56476 bytes reference/plot_expression_partitions-1.png | Bin 178773 -> 182679 bytes reference/plot_partition_frequencies-1.png | Bin 104683 -> 110387 bytes reference/plot_samplecor-1.png | Bin 108626 -> 108266 bytes 14 files changed, 150 insertions(+), 73 deletions(-) create mode 100644 articles/HybridExpress_files/figure-html/unnamed-chunk-14-1.png create mode 100644 articles/HybridExpress_files/figure-html/unnamed-chunk-15-1.png create mode 100644 articles/HybridExpress_files/figure-html/unnamed-chunk-16-1.png create mode 100644 articles/HybridExpress_files/figure-html/unnamed-chunk-16-2.png diff --git a/articles/HybridExpress.html b/articles/HybridExpress.html index 2f90a24..bc1b974 100644 --- a/articles/HybridExpress.html +++ b/articles/HybridExpress.html @@ -259,19 +259,19 @@
We will proceed our analyses with the midparent expression values
obtained from the mean of the counts, stored in the se
object.
Now, we will use get_deg_list()
to get differentially
expressed genes (DEGs) for each contrast while using spike-in
normalization.
To summarize the frequencies of up- and down-regulated genes per
contrast in a single data frame, use the function
get_deg_counts()
.
It is important to note that the columns perc_up
,
perc_down
, and perc_total
show the percentages
of up-regulated, down-regulated, and all differentially expressed genes
@@ -515,10 +515,10 @@
Finally, we can summarize everything in a single publication-ready
figure using the plot plot_expression_triangle()
, which
shows the ‘experimental trio’ (i.e., hybrid and its progenitors) as a
@@ -533,8 +533,8 @@
@@ -548,6 +548,83 @@
Expression-based gene classification
+After identifying DEGs for different contrasts, you’d typically want +to classify your genes into expression partitions based on their +expression patterns. This can be performed with the function +
+expression_partitioning()
, which classifies genes into one +of the 12 categories as in Rapp, +Udall, and Wendel (2009), and into 5 major +classes that summarize the 12 categories. The five +classes are:+
+- +Transgressive up-regulation (UP): gene is +up-regulated in the hybrid as compared to both parents.
+- +Transgressive down-regulation (DOWN): gene is +down-regulated in the hybrid as compared to both parents.
+- +Additivity (ADD): gene expression in the hybrid is +the mean of both parents (additive effect).
+- +Expression-level dominance toward parent 1 +(ELD_P1): gene expression in the hybrid is the same as in +parent 1, but different from parent 2.
+- +Expression-level dominance toward parent 2 +(ELD_P2): gene expression in the hybrid is the same as in +parent 2, but different from parent 1.
+++# Classify genes in expression partitions +exp_partitions <- expression_partitioning(deg_list) + +# Inspect the output +head(exp_partitions) +#> Gene Category Class lFC_F1_vs_P1 lFC_F1_vs_P2 +#> 1 Cre01.g003650 1 ADD 0.7833401 -1.0484704 +#> 2 Cre01.g005150 1 ADD 1.0468778 -0.5043874 +#> 3 Cre01.g008600 1 ADD 5.0497424 -1.4856888 +#> 4 Cre01.g013500 1 ADD 2.1098246 -1.5331691 +#> 5 Cre01.g034850 1 ADD 1.5835491 -0.7610260 +#> 6 Cre01.g800005 1 ADD 1.4927376 -0.9315539 + +# Count number of genes per category +table(exp_partitions$Category) +#> +#> 1 2 3 4 5 6 7 8 9 10 11 12 +#> 113 292 113 3090 152 647 238 763 304 285 1681 2578 + +# Count number of genes per class +table(exp_partitions$Class) +#> +#> UP DOWN ADD ELD_P1 ELD_P2 +#> 1562 636 2691 3394 1973
To visualize the expression partitions as a scatter plot of +expression divergences, you can use the function +
+plot_expression_partitions()
.+ ++# Plot partitions as a scatter plot of divergences +plot_expression_partitions(exp_partitions, group_by = "Category")
By default, genes are grouped by
+Category
. However, you +can also group genes byClass
as follows:+ ++# Group by `Class` +plot_expression_partitions(exp_partitions, group_by = "Class")
You can also visualize the frequencies of genes in each partition +with the function
+plot_partition_frequencies()
.+ ++# Visualize frequency of genes in each partition +## By `Category` (default) +plot_partition_frequencies(exp_partitions)
+++## By `Class` +plot_partition_frequencies(exp_partitions, group_by = "Class")
FAQ @@ -613,7 +690,7 @@
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