From 5c3915c9bf212e417f140f65f9334a475247ce8c Mon Sep 17 00:00:00 2001
From: billila Original Authors: Martin Morgan, Sonali Arora, Lori Shepherd Objective: Gain confidence working with base R commands and data
structures. Functions transform inputs to outputs, perhaps with side effects Argument matching first by name, then by position Functions may define (some) arguments to have default values Use Remind yourselves about various ways to subset and access columns of a
data.frame Color the female and male points differently. To do this, use the
Plotting large numbers of points means that they are often
over-plotted, potentially obscuring important patterns. Experiment
@@ -1309,18 +1309,18 @@ Color the female and male points differently. Create a subset of the data containing only observations from 2010
and make density curves for male and female groups. Use the Plotting large numbers of points means that they are often
over-plotted, potentially obscuring important patterns. Make the
points semi-transparent using alpha. Here we make them 60%
@@ -1371,11 +1371,11 @@ Add a fitted regression model to the scatter plot. Original Authors: Martin Morgan, Sonali Arora, Lori Shepherd Objective: Gain confidence working with ‘tidy’ R commands and data
structures.
**Presenting Author:** [Davide Risso][], [Ilaria Billato][]
-**Date:** 6-11 July, 2025
+**Date:** 24-29 May, 2026
**Back:** [Monday labs](lab-1-intro-to-r-bioc.html)
[Davide Risso]: mailto: davide.risso@unipd.it
-[Ilaria Billato]: mailto: ilaria.billato@phd.unipd.it
+[Ilaria Billato]: mailto: ilaria.billato@unipd.it
**Objective**: Gain confidence working with base R commands and data
diff --git a/intro-to-r-bioc/L1.1-r-intro.html b/intro-to-r-bioc/L1.1-r-intro.html
index a47214c..918a2db 100644
--- a/intro-to-r-bioc/L1.1-r-intro.html
+++ b/intro-to-r-bioc/L1.1-r-intro.html
@@ -10,7 +10,7 @@
-
+
Lab 1.1: Introduction to R
-4 July 2025
+18 May 2026
@@ -748,8 +748,8 @@ Contents
});
-Presenting Author: Davide Risso, Ilaria Billato
-Date: 6-11 July, 2025
+Presenting Author: Davide Risso, Ilaria Billato
+Date: 24-29 May, 2025
Back: Monday labs1.3 Function, generic, method
-rnorm(5)## [1] 1.8403581 -0.5875253 0.3752672 1.4805688 0.2594510## [1] -1.2742519 0.8572209 -0.2628611 -0.6515463 -0.2685614
1.4 Programming
## list of length 5, each containsing a sample (with replacement) of letters
lets <- replicate(5, sample(letters, 50, TRUE), simplify=FALSE)
sapply(lets, fun)
-
+## [1] 22 22 23 24 20## [1] 18 24 24 22 221.5 Introspection & Help
@@ -962,24 +962,24 @@ 2.1 Linear model
## Analysis of Variance Table
##
## Response: y
-## Df Sum Sq Mean Sq F value Pr(>F)
-## x 1 967.24 967.24 4032.6 < 2.2e-16 ***
-## Residuals 998 239.38 0.24
+## Df Sum Sq Mean Sq F value Pr(>F)
+## x 1 1020.95 1020.95 4243.6 < 2.2e-16 ***
+## Residuals 998 240.11 0.24
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
-plot(y ~ x, df) # methods(plot); ?plot.formula
abline(fit, col="red", lwd=3, lty=2) # a function, not generic.methodmethods() for introspection 9calss and method discovery), e.g.,methods(class=class(fit)) # introspection## [1] add1 alias anova case.names coerce
## [6] confint cooks.distance deviance dfbeta dfbetas
-## [11] drop1 dummy.coef effects extractAIC family
-## [16] formula hatvalues influence initialize kappa
-## [21] labels logLik model.frame model.matrix nobs
-## [26] plot predict print proj qr
-## [31] residuals rstandard rstudent show simulate
-## [36] slotsFromS3 summary variable.names vcov
+## [11] dffits drop1 dummy.coef effects extractAIC
+## [16] family formula hatvalues influence initialize
+## [21] kappa labels logLik model.frame model.matrix
+## [26] nobs plot predict print proj
+## [31] qr residuals rstandard rstudent show
+## [36] simulate slotsFromS3 summary variable.names vcov
## see '?methods' for accessing help and source code3.1 ALL phenotypic data
## 5 REL 11/4/1997
## 6 REL 12/15/1997
-summary(pdata$sex)
+## Length Class Mode
-## 127 character character## Length N.unique N.blank Min.nchar Max.nchar NAs
+## 127 2 0 1 1 2
-summary(pdata$cyto.normal)## Mode FALSE TRUE NA's
+## Mode FALSE TRUE NAs
## logical 69 24 343.1 ALL phenotypic data
## mean in group BCR/ABL mean in group NEG
## 40.25000 28.07042
-boxplot(age ~ mol.biol, bcrabl)3.2 Weighty matters
@@ -1270,7 +1270,7 @@ 3.2 Weighty matters
Y-axis. Experiment with different plotting symbols (try the command
example(points) to view different points).
-plot(sqrt(Weight) ~ Height, brfss, main="All Years, Both Sexes")col argument to plot(). Provide as a value to that argument a
vector of colors, subset by brfss$Sex.3.2 Weighty matters
main="2010, Female")
plot(sqrt(Weight) ~ Height, brfss2010[brfss2010$Sex == "Male", ],
main="2010, Male")
-par(opar) # reset 'par' to original value3.2 Weighty matters
## 'quick' plot
qplot(Height, sqrt(Weight), data=brfss)
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
-## This warning is displayed once every 8 hours.
+## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
-## Warning: Removed 735 rows containing missing values or values outside the scale range
## (`geom_point()`).## specify the data set and 'aesthetics', then how to plot
ggplot(brfss, aes(x=Height, y=sqrt(Weight))) +
geom_point()
-## Warning: Removed 735 rows containing missing values or values outside the scale range
## (`geom_point()`).
+
qplot() gives us a warning which states that it has removed rows
containing missing values. This is actually very helpful because we
find out that our dataset contains NA’s and we can take a design
@@ -1340,21 +1340,21 @@ 3.2 Weighty matters
-qplot(Height, sqrt(Weight), data=brfss) +
ylab("Square root of Weight") +
ggtitle("All Years, Both Sexes")
-ggplot(brfss, aes(x=Height, y=sqrt(Weight), color=Sex)) +
geom_point()
+
One can also change the shape of the points for the female and male
groups
-ggplot(brfss, aes(x=Height, y = sqrt(Weight), color=Sex, shape=Sex)) +
geom_point()
+
or plot Male and Female in different panels using
facet_grid()
-ggplot(brfss, aes(x=Height, y = sqrt(Weight), color=Sex)) +
geom_point() +
facet_grid(Sex ~ .)fill
aesthetic to indicate that each sex is to be calculated separately,
@@ -1362,7 +1362,7 @@ 3.2 Weighty matters
-brfss2010 <- brfss[brfss$Year == "2010", ]
ggplot(brfss2010, aes(x=sqrt(Weight), fill=Sex)) +
geom_density(alpha=.25)3.2 Weighty matters
variable modified at a later point.
-sp <- ggplot(brfss, aes(x=Height, y=sqrt(Weight)))
sp + geom_point(alpha=.4)sp + geom_point() + stat_smooth(method=lm)
-## `geom_smooth()` using formula = 'y ~ x'
+
By default,
stat_smooth() also adds a 95% confidence region for
the regression fit. The confidence interval can be changed by
setting level, or it can be disabled with se=FALSE.3.2 Weighty matters
scale_colour_brewer(palette="Set1")
sps + geom_smooth(method="lm")
-## `geom_smooth()` using formula = 'y ~ x'4 End matter
4.1 Session Info
-sessionInfo()## R version 4.5.1 (2025-06-13)
+
+## [1] Matrix_1.7-5 gtable_0.3.6 jsonlite_2.0.0
+## [4] dplyr_1.2.1 compiler_4.6.0 BiocManager_1.30.27
+## [7] tidyselect_1.2.1 Rcpp_1.1.1-1.1 tinytex_0.59
+## [10] magick_2.9.1 dichromat_2.0-0.1 jquerylib_0.1.4
+## [13] splines_4.6.0 scales_1.4.0 yaml_2.3.12
+## [16] fastmap_1.2.0 lattice_0.22-9 R6_2.6.1
+## [19] labeling_0.4.3 generics_0.1.4 knitr_1.51
+## [22] tibble_3.3.1 bookdown_0.46 bslib_0.11.0
+## [25] pillar_1.11.1 RColorBrewer_1.1-3 rlang_1.2.0
+## [28] cachem_1.1.0 xfun_0.57 sass_0.4.10
+## [31] S7_0.2.2 otel_0.2.0 cli_3.6.6
+## [34] mgcv_1.9-4 withr_3.0.2 magrittr_2.0.5
+## [37] digest_0.6.39 grid_4.6.0 rstudioapi_0.18.0
+## [40] nlme_3.1-169 lifecycle_1.0.5 vctrs_0.7.3
+## [43] evaluate_1.0.5 glue_1.8.1 farver_2.1.2
+## [46] codetools_0.2-20 rmarkdown_2.31 pkgconfig_2.0.3
+## [49] tools_4.6.0 htmltools_0.5.9## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
-## Running under: Ubuntu 24.04.2 LTS
+## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
@@ -1421,26 +1421,26 @@ 4.1 Session Info
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
-## [1] ggplot2_3.5.2 BiocStyle_2.36.0
+## [1] ggplot2_4.0.3 BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
-## [1] Matrix_1.7-3 gtable_0.3.6 jsonlite_2.0.0
-## [4] crayon_1.5.3 dplyr_1.1.4 compiler_4.5.1
-## [7] BiocManager_1.30.26 tidyselect_1.2.1 Rcpp_1.1.0
-## [10] tinytex_0.57 magick_2.8.7 dichromat_2.0-0.1
-## [13] jquerylib_0.1.4 splines_4.5.1 scales_1.4.0
-## [16] yaml_2.3.10 fastmap_1.2.0 lattice_0.22-7
-## [19] R6_2.6.1 labeling_0.4.3 generics_0.1.4
-## [22] knitr_1.50 tibble_3.3.0 bookdown_0.43
-## [25] bslib_0.9.0 pillar_1.10.2 RColorBrewer_1.1-3
-## [28] rlang_1.1.6 cachem_1.1.0 xfun_0.52
-## [31] sass_0.4.10 cli_3.6.5 mgcv_1.9-3
-## [34] withr_3.0.2 magrittr_2.0.3 digest_0.6.37
-## [37] grid_4.5.1 rstudioapi_0.17.1 nlme_3.1-168
-## [40] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.4
-## [43] glue_1.8.0 farver_2.1.2 codetools_0.2-20
-## [46] rmarkdown_2.29 tools_4.5.1 pkgconfig_2.0.3
-## [49] htmltools_0.5.8.14.2 Acknowledgements
From b2d2af2f6508e442742a9bc1c288861e0bf77d41 Mon Sep 17 00:00:00 2001
From: billila
**Presenting Author:** [Davide Risso][], [Ilaria Billato][]
-**Date:** 6-11 July, 2025
+**Date:** 24-29 May, 2026
**Back:** [Monday labs](lab-1-intro-to-r-bioc.html)
[Davide Risso]: mailto: davide.risso@unipd.it
-[Ilaria Billato]: mailto: ilaria.billato@phd.unipd.it
+[Ilaria Billato]: mailto: ilaria.billato@unipd.it
**Objective**: Gain confidence working with 'tidy' R commands and data
diff --git a/intro-to-r-bioc/L1.2-tidy-r-intro.html b/intro-to-r-bioc/L1.2-tidy-r-intro.html
index 85be840..9bb6a1b 100644
--- a/intro-to-r-bioc/L1.2-tidy-r-intro.html
+++ b/intro-to-r-bioc/L1.2-tidy-r-intro.html
@@ -10,7 +10,7 @@
-
+
Lab 1.2: Introduction to tidy R
-4 July 2025
+18 May 2026
Contents
});
-Presenting Author: Davide Risso, Ilaria Billato
-Date: 6-11 July, 2025
+Presenting Author: Davide Risso, Ilaria Billato
+Date: 24-29 May, 2025
Back: Monday labs1 Introduction to the tidyverse
AveAge = mean(Age, na.rm=TRUE),
AveWeight = mean(Weight, na.rm=TRUE)
)
-
+## `summarise()` has grouped output by 'Sex'. You can override using the `.groups`
-## argument.## `summarise()` has regrouped the output.
+## ℹ Summaries were computed grouped by Sex and Year.
+## ℹ Output is grouped by Sex.
+## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
+## ℹ Use `summarise(.by = c(Sex, Year))` for per-operation grouping
+## (`?dplyr::dplyr_by`) instead.## # A tibble: 4 × 4
## # Groups: Sex [2]
## Sex Year AveAge AveWeight
@@ -937,7 +941,7 @@ for use,
we need to explicitly indicate that the data should be the second
argument. One way of doing this is to use the special symbol 2.1.2 Using un-tidy functions:
. to
-represent the location of the incoming data, invoking t.test(age ~ sex, data = .):
t.test(age ~ sex, data = _):
pdata |>
t.test(age ~ sex, data = _)
Exercise Perform a t-test to ask whether there is evidence of @@ -1174,7 +1178,7 @@
library(ggplot2)
ggplot(gene_summaries, aes(ave_log_count)) +
geom_density()
-For a tidy interface to some Bioconductor classes, see the tidyomics package and paper.
@@ -1183,9 +1187,9 @@sessionInfo()
-## R version 4.5.1 (2025-06-13)
+## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
-## Running under: Ubuntu 24.04.2 LTS
+## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
@@ -1206,27 +1210,28 @@ 3.1 Session Info
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
-## [1] tidyr_1.3.1 ggplot2_3.5.2 dplyr_1.1.4 tibble_3.3.0
-## [5] readr_2.1.5 BiocStyle_2.36.0
+## [1] tidyr_1.3.2 ggplot2_4.0.3 dplyr_1.2.1 tibble_3.3.1
+## [5] readr_2.2.0 BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.10 utf8_1.2.6 generics_0.1.4
-## [4] hms_1.1.3 digest_0.6.37 magrittr_2.0.3
-## [7] evaluate_1.0.4 grid_4.5.1 RColorBrewer_1.1-3
-## [10] bookdown_0.43 fastmap_1.2.0 jsonlite_2.0.0
-## [13] tinytex_0.57 BiocManager_1.30.26 purrr_1.0.4
+## [4] hms_1.1.4 digest_0.6.39 magrittr_2.0.5
+## [7] evaluate_1.0.5 grid_4.6.0 RColorBrewer_1.1-3
+## [10] bookdown_0.46 fastmap_1.2.0 jsonlite_2.0.0
+## [13] tinytex_0.59 BiocManager_1.30.27 purrr_1.2.2
## [16] scales_1.4.0 codetools_0.2-20 jquerylib_0.1.4
-## [19] cli_3.6.5 rlang_1.1.6 crayon_1.5.3
-## [22] bit64_4.6.0-1 withr_3.0.2 cachem_1.1.0
-## [25] yaml_2.3.10 tools_4.5.1 parallel_4.5.1
-## [28] tzdb_0.5.0 vctrs_0.6.5 R6_2.6.1
-## [31] lifecycle_1.0.4 magick_2.8.7 bit_4.6.0
-## [34] vroom_1.6.5 pkgconfig_2.0.3 archive_1.1.12
-## [37] pillar_1.10.2 bslib_0.9.0 gtable_0.3.6
-## [40] Rcpp_1.1.0 glue_1.8.0 xfun_0.52
-## [43] tidyselect_1.2.1 rstudioapi_0.17.1 knitr_1.50
-## [46] dichromat_2.0-0.1 farver_2.1.2 htmltools_0.5.8.1
-## [49] rmarkdown_2.29 labeling_0.4.3 compiler_4.5.1
+## [19] cli_3.6.6 rlang_1.2.0 crayon_1.5.3
+## [22] bit64_4.8.0 withr_3.0.2 cachem_1.1.0
+## [25] yaml_2.3.12 otel_0.2.0 tools_4.6.0
+## [28] parallel_4.6.0 tzdb_0.5.0 vctrs_0.7.3
+## [31] R6_2.6.1 magick_2.9.1 lifecycle_1.0.5
+## [34] bit_4.6.0 vroom_1.7.1 pkgconfig_2.0.3
+## [37] pillar_1.11.1 bslib_0.11.0 gtable_0.3.6
+## [40] Rcpp_1.1.1-1.1 glue_1.8.1 xfun_0.57
+## [43] tidyselect_1.2.1 rstudioapi_0.18.0 knitr_1.51
+## [46] dichromat_2.0-0.1 farver_2.1.2 htmltools_0.5.9
+## [49] rmarkdown_2.31 labeling_0.4.3 compiler_4.6.0
+## [52] S7_0.2.2
G|C
-[1,] 0.336276
+ G|C
+[1,] 0.3454924
Exercises
@@ -1334,18 +1333,23 @@sessionInfo()R version 4.6.0 (2026-04-24)
-Platform: aarch64-apple-darwin23
-Running under: macOS Tahoe 26.4.1
+Platform: x86_64-pc-linux-gnu
+Running under: Ubuntu 24.04.4 LTS
Matrix products: default
-BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
-LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
+BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
locale:
-[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
+ [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
+ [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
+ [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
+ [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
+ [9] LC_ADDRESS=C LC_TELEPHONE=C
+[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
-time zone: Europe/Berlin
-tzcode source: internal
+time zone: Europe/Rome
+tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
@@ -1371,60 +1375,58 @@ Session Info
[17] rtracklayer_1.72.0
[18] BiocIO_1.22.0
[19] GenomeInfoDb_1.48.0
-[20] pwalign_1.8.0
-[21] GenomicRanges_1.64.0
-[22] Biostrings_2.80.0
-[23] Seqinfo_1.2.0
-[24] XVector_0.52.0
-[25] IRanges_2.46.0
-[26] S4Vectors_0.50.0
-[27] BiocGenerics_0.58.0
-[28] generics_0.1.4
-[29] BiocStyle_2.40.0
+[20] GenomicRanges_1.64.0
+[21] Biostrings_2.80.0
+[22] Seqinfo_1.2.0
+[23] XVector_0.52.0
+[24] IRanges_2.46.0
+[25] S4Vectors_0.50.1
+[26] BiocGenerics_0.58.1
+[27] generics_0.1.4
loaded via a namespace (and not attached):
[1] DBI_1.3.0 bitops_1.0-9
[3] httr2_1.2.2 rlang_1.2.0
- [5] magrittr_2.0.5 gypsum_1.8.0
- [7] compiler_4.6.0 RSQLite_2.4.6
- [9] png_0.1-9 vctrs_0.7.3
-[11] ProtGenerics_1.44.0 pkgconfig_2.0.3
-[13] crayon_1.5.3 fastmap_1.2.0
-[15] dbplyr_2.5.2 rmarkdown_2.31
-[17] UCSC.utils_1.8.0 scRNAseq_2.26.0
-[19] purrr_1.2.2 bit_4.6.0
-[21] xfun_0.57 cachem_1.1.0
-[23] cigarillo_1.2.0 jsonlite_2.0.0
-[25] blob_1.3.0 rhdf5filters_1.24.0
-[27] DelayedArray_0.38.1 Rhdf5lib_2.0.0
-[29] parallel_4.6.0 R6_2.6.1
-[31] Rcpp_1.1.1-1.1 knitr_1.51
-[33] Matrix_1.7-5 tidyselect_1.2.1
-[35] rstudioapi_0.18.0 abind_1.4-8
-[37] yaml_2.3.12 codetools_0.2-20
-[39] curl_7.1.0 lattice_0.22-9
-[41] alabaster.sce_1.12.0 tibble_3.3.1
-[43] withr_3.0.2 KEGGREST_1.52.0
-[45] evaluate_1.0.5 BiocFileCache_3.2.0
-[47] alabaster.schemas_1.12.0 ExperimentHub_3.2.0
-[49] pillar_1.11.1 BiocManager_1.30.27
-[51] filelock_1.0.3 RCurl_1.98-1.18
-[53] BiocVersion_3.23.1 ensembldb_2.36.0
-[55] alabaster.base_1.13.0 alabaster.ranges_1.12.0
-[57] glue_1.8.1 alabaster.matrix_1.12.0
-[59] lazyeval_0.2.3 tools_4.6.0
-[61] AnnotationHub_4.2.0 XML_3.99-0.23
-[63] rhdf5_2.56.0 grid_4.6.0
-[65] SingleCellExperiment_1.34.0 HDF5Array_1.40.0
-[67] restfulr_0.0.16 cli_3.6.6
-[69] rappdirs_0.3.4 S4Arrays_1.12.0
-[71] dplyr_1.2.1 AnnotationFilter_1.36.0
-[73] alabaster.se_1.12.0 digest_0.6.39
-[75] SparseArray_1.12.2 rjson_0.2.23
-[77] htmlwidgets_1.6.4 memoise_2.0.1
-[79] htmltools_0.5.9 lifecycle_1.0.5
-[81] h5mread_1.4.0 httr_1.4.8
-[83] bit64_4.8.0
+ [5] magrittr_2.0.5 otel_0.2.0
+ [7] gypsum_1.8.0 compiler_4.6.0
+ [9] RSQLite_3.52.0 png_0.1-9
+[11] vctrs_0.7.3 ProtGenerics_1.44.0
+[13] pkgconfig_2.0.3 crayon_1.5.3
+[15] fastmap_1.2.0 dbplyr_2.5.2
+[17] rmarkdown_2.31 UCSC.utils_1.8.0
+[19] scRNAseq_2.26.0 purrr_1.2.2
+[21] bit_4.6.0 xfun_0.57
+[23] cachem_1.1.0 cigarillo_1.2.0
+[25] jsonlite_2.0.0 blob_1.3.0
+[27] rhdf5filters_1.24.0 DelayedArray_0.38.1
+[29] Rhdf5lib_2.0.0 parallel_4.6.0
+[31] R6_2.6.1 Rcpp_1.1.1-1.1
+[33] knitr_1.51 Matrix_1.7-5
+[35] tidyselect_1.2.1 rstudioapi_0.18.0
+[37] abind_1.4-8 yaml_2.3.12
+[39] codetools_0.2-20 curl_7.1.0
+[41] lattice_0.22-9 alabaster.sce_1.12.0
+[43] tibble_3.3.1 withr_3.0.2
+[45] KEGGREST_1.52.0 evaluate_1.0.5
+[47] BiocFileCache_3.2.0 alabaster.schemas_1.12.0
+[49] ExperimentHub_3.2.0 pillar_1.11.1
+[51] BiocManager_1.30.27 filelock_1.0.3
+[53] RCurl_1.98-1.18 BiocVersion_3.23.1
+[55] ensembldb_2.36.0 alabaster.base_1.12.0
+[57] glue_1.8.1 alabaster.ranges_1.12.0
+[59] alabaster.matrix_1.12.0 lazyeval_0.2.3
+[61] tools_4.6.0 AnnotationHub_4.2.0
+[63] XML_3.99-0.23 rhdf5_2.56.0
+[65] grid_4.6.0 SingleCellExperiment_1.34.0
+[67] HDF5Array_1.40.0 restfulr_0.0.16
+[69] cli_3.6.6 rappdirs_0.3.4
+[71] S4Arrays_1.12.0 dplyr_1.2.1
+[73] AnnotationFilter_1.36.0 alabaster.se_1.12.0
+[75] digest_0.6.39 SparseArray_1.12.2
+[77] rjson_0.2.23 htmlwidgets_1.6.4
+[79] memoise_2.0.1 htmltools_0.5.9
+[81] lifecycle_1.0.5 h5mread_1.4.0
+[83] httr_1.4.8 bit64_4.8.0
Original Authors: Martin Morgan, Sonali Arora, Lori Shepherd
-Presenting Author: Davide Risso, Ilaria Billato
-Date: 6-11 July, 2025
+Presenting Author: Davide Risso, Ilaria Billato
+Date: 24-29 May, 2026
Back: Monday labs
Objective: Learn about Bioconductor resources for gene and genome annotation.
@@ -934,15 +934,15 @@##
## Please see: help('select') for usage information
The idea is that there are keytypes() that can be mapped to
@@ -978,7 +978,7 @@
head(keys(org.Hs.eg.db, "SYMBOL"))
-## [1] "A1BG" "A2M" "A2MP1" "NAT1" "NAT2" "NATP"## [1] "A1BG" "A2M" "NAT1" "NAT2" "NATP" "SERPINA3"
There are two basic ways of extracting data from an org.* package
– mapIds() to create a 1:1 mapping between key and a single
column, and select() (it’s often necessary to specify this
@@ -988,28 +988,28 @@
## 'select()' returned 1:1 mapping between keys and columns
-## 130060841 130067052 127892030 127830407 129994078
-## "LOC130060841" "LOC130067052" "LOC127892030" "LOC127830407" "LOC129994078"
-## 127408916
-## "LOC127408916"
+## 130060875 130067086 127892063 127830440 129994112
+## "LOC130060875" "LOC130067086" "LOC127892063" "LOC127830440" "LOC129994112"
+## 127408949
+## "LOC127408949"
AnnotationDbi::select(
org.Hs.eg.db, egid, c("SYMBOL", "ENSEMBL", "GENENAME"), "ENTREZID"
)
## 'select()' returned 1:1 mapping between keys and columns
## ENTREZID SYMBOL ENSEMBL
-## 1 130060841 LOC130060841 <NA>
-## 2 130067052 LOC130067052 <NA>
-## 3 127892030 LOC127892030 <NA>
-## 4 127830407 LOC127830407 <NA>
-## 5 129994078 LOC129994078 <NA>
-## 6 127408916 LOC127408916 <NA>
-## GENENAME
-## 1 ATAC-STARR-seq lymphoblastoid active region 12162
-## 2 ATAC-STARR-seq lymphoblastoid active region 18723
-## 3 NANOG-H3K27ac-H3K4me1 hESC enhancer GRCh37_chr19:50373269-50373916
-## 4 H3K4me1 hESC enhancer GRCh37_chr15:79497097-79497660
-## 5 ATAC-STARR-seq lymphoblastoid active region 22684
-## 6 OCT4-NANOG hESC enhancer GRCh37_chr7:38026488-38027240Some key - column mappings are 1:many, e.g., Entrez ID "3812"
maps to 44 Ensembl Ids. What does mapIds() return when mapping
Entrez ID "3812" to Ensembl ids? Use the additional argument
@@ -1026,7 +1026,7 @@
## 'select()' returned 1:many mapping between keys and columns
## CharacterList of length 1
-## [["3812"]] ENSG00000240403 ENSG00000288389 ... ENSG00000277181 ENSG00000275566
+## [["3812"]] ENSG00000240403 ENSG00000288389 ... ENSG00000277709 ENSG00000275566
AnnotationDbi::select(
org.Hs.eg.db, egid, c("SYMBOL", "ENSEMBL"),
multiVals = "CharacterList"
@@ -1035,40 +1035,40 @@ 1.3 Exercises
## ENTREZID SYMBOL ENSEMBL
## 1 3812 KIR3DL2 ENSG00000240403
## 2 3812 KIR3DL2 ENSG00000288389
-## 3 3812 KIR3DL2 ENSG00000284213
-## 4 3812 KIR3DL2 ENSG00000274722
-## 5 3812 KIR3DL2 ENSG00000278474
+## 3 3812 KIR3DL2 ENSG00000278809
+## 4 3812 KIR3DL2 ENSG00000275629
+## 5 3812 KIR3DL2 ENSG00000284213
## 6 3812 KIR3DL2 ENSG00000284295
-## 7 3812 KIR3DL2 ENSG00000276004
-## 8 3812 KIR3DL2 ENSG00000284192
-## 9 3812 KIR3DL2 ENSG00000275629
-## 10 3812 KIR3DL2 ENSG00000276882
-## 11 3812 KIR3DL2 ENSG00000278809
-## 12 3812 KIR3DL2 ENSG00000276424
-## 13 3812 KIR3DL2 ENSG00000278656
-## 14 3812 KIR3DL2 ENSG00000275416
-## 15 3812 KIR3DL2 ENSG00000275626
-## 16 3812 KIR3DL2 ENSG00000275083
-## 17 3812 KIR3DL2 ENSG00000278726
-## 18 3812 KIR3DL2 ENSG00000277982
-## 19 3812 KIR3DL2 ENSG00000278361
-## 20 3812 KIR3DL2 ENSG00000273735
-## 21 3812 KIR3DL2 ENSG00000284466
-## 22 3812 KIR3DL2 ENSG00000278710
-## 23 3812 KIR3DL2 ENSG00000276357
-## 24 3812 KIR3DL2 ENSG00000275262
-## 25 3812 KIR3DL2 ENSG00000278442
-## 26 3812 KIR3DL2 ENSG00000277709
-## 27 3812 KIR3DL2 ENSG00000276739
-## 28 3812 KIR3DL2 ENSG00000278403
-## 29 3812 KIR3DL2 ENSG00000278758
-## 30 3812 KIR3DL2 ENSG00000275838
-## 31 3812 KIR3DL2 ENSG00000284528
-## 32 3812 KIR3DL2 ENSG00000273911
-## 33 3812 KIR3DL2 ENSG00000278850
-## 34 3812 KIR3DL2 ENSG00000278707
-## 35 3812 KIR3DL2 ENSG00000275511
-## 36 3812 KIR3DL2 ENSG00000277181
+## 7 3812 KIR3DL2 ENSG00000284192
+## 8 3812 KIR3DL2 ENSG00000275416
+## 9 3812 KIR3DL2 ENSG00000276424
+## 10 3812 KIR3DL2 ENSG00000278726
+## 11 3812 KIR3DL2 ENSG00000274722
+## 12 3812 KIR3DL2 ENSG00000275083
+## 13 3812 KIR3DL2 ENSG00000284466
+## 14 3812 KIR3DL2 ENSG00000278850
+## 15 3812 KIR3DL2 ENSG00000277982
+## 16 3812 KIR3DL2 ENSG00000278710
+## 17 3812 KIR3DL2 ENSG00000276357
+## 18 3812 KIR3DL2 ENSG00000278361
+## 19 3812 KIR3DL2 ENSG00000275262
+## 20 3812 KIR3DL2 ENSG00000275511
+## 21 3812 KIR3DL2 ENSG00000284528
+## 22 3812 KIR3DL2 ENSG00000278442
+## 23 3812 KIR3DL2 ENSG00000276739
+## 24 3812 KIR3DL2 ENSG00000278707
+## 25 3812 KIR3DL2 ENSG00000276882
+## 26 3812 KIR3DL2 ENSG00000273911
+## 27 3812 KIR3DL2 ENSG00000278474
+## 28 3812 KIR3DL2 ENSG00000275838
+## 29 3812 KIR3DL2 ENSG00000276004
+## 30 3812 KIR3DL2 ENSG00000278403
+## 31 3812 KIR3DL2 ENSG00000278656
+## 32 3812 KIR3DL2 ENSG00000275626
+## 33 3812 KIR3DL2 ENSG00000273735
+## 34 3812 KIR3DL2 ENSG00000277181
+## 35 3812 KIR3DL2 ENSG00000278758
+## 36 3812 KIR3DL2 ENSG00000277709
## 37 3812 KIR3DL2 ENSG00000275566
It seems like it might often be useful to use the tidyverse on
return values from mapIds() and select(); explore this usage
## # A tibble: 193,430 × 2
+## # A tibble: 193,797 × 2
## ENTREZID value
## <chr> <chr>
## 1 1 A1BG
## 2 2 A2M
-## 3 3 A2MP1
-## 4 4 NAT1
-## 5 5 NAT2
-## 6 6 NATP
-## 7 7 SERPINA3
-## 8 8 AADAC
-## 9 9 AAMP
-## 10 10 AANAT
-## # ℹ 193,420 more rows
+## 3 3 NAT1
+## 4 4 NAT2
+## 5 5 NATP
+## 6 6 SERPINA3
+## 7 7 AADAC
+## 8 8 AAMP
+## 9 9 AANAT
+## 10 10 AARS1
+## # ℹ 193,787 more rows
AnnotationDbi::select(
org.Hs.eg.db, egid, c("SYMBOL", "GO", "GENENAME"), "ENTREZID"
) |>
as_tibble()
-## # A tibble: 532,170 × 6
+## # A tibble: 633,772 × 6
## ENTREZID SYMBOL GO EVIDENCE ONTOLOGY GENENAME
## <chr> <chr> <chr> <chr> <chr> <chr>
-## 1 1 A1BG GO:0002764 IBA BP alpha-1-B glycoprotein
+## 1 1 A1BG GO:0004888 IBA MF alpha-1-B glycoprotein
## 2 1 A1BG GO:0005576 HDA CC alpha-1-B glycoprotein
## 3 1 A1BG GO:0005576 IDA CC alpha-1-B glycoprotein
-## 4 1 A1BG GO:0005576 TAS CC alpha-1-B glycoprotein
-## 5 1 A1BG GO:0005615 HDA CC alpha-1-B glycoprotein
-## 6 1 A1BG GO:0005886 IBA CC alpha-1-B glycoprotein
-## 7 1 A1BG GO:0031093 TAS CC alpha-1-B glycoprotein
-## 8 1 A1BG GO:0034774 TAS CC alpha-1-B glycoprotein
-## 9 1 A1BG GO:0062023 HDA CC alpha-1-B glycoprotein
-## 10 1 A1BG GO:0070062 HDA CC alpha-1-B glycoprotein
-## # ℹ 532,160 more rows
Concept Check:
After completing this previous section you should be able to answer the following:
@@ -1133,10 +1133,10 @@library(biomaRt)
head(listMarts())
## biomart version
-## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 114
-## 2 ENSEMBL_MART_MOUSE Mouse strains 114
-## 3 ENSEMBL_MART_SNP Ensembl Variation 114
-## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 114
+## 1 ENSEMBL_MART_ENSEMBL Ensembl Genes 115
+## 2 ENSEMBL_MART_MOUSE Mouse strains 115
+## 3 ENSEMBL_MART_SNP Ensembl Variation 115
+## 4 ENSEMBL_MART_FUNCGEN Ensembl Regulation 115
mart <- useMart("ENSEMBL_MART_ENSEMBL")
Use listDatasets() and useDataset() to select the Homo
sapiens gene dataset.
## # A tibble: 367 × 2
+## # A tibble: 371 × 2
## pathway description
## <chr> <chr>
## 1 hsa01100 Metabolic pathways - Homo sapiens (human)
@@ -1229,14 +1229,14 @@ 1.3 Exercises
## 8 hsa01240 Biosynthesis of cofactors - Homo sapiens (human)
## 9 hsa01320 Sulfur cycle - Homo sapiens (human)
## 10 hsa00010 Glycolysis / Gluconeogenesis - Homo sapiens (human)
-## # ℹ 357 more rows
Use keggLink() to recover the genes in each pathway.
hsa_path_eg <-
keggLink("pathway", "hsa") |>
enframe(name = "egid", value = "pathway") |>
mutate(egid = sub("hsa:", "", egid))
hsa_path_eg
-## # A tibble: 39,313 × 2
+## # A tibble: 39,348 × 2
## egid pathway
## <chr> <chr>
## 1 10327 path:hsa00010
@@ -1249,24 +1249,24 @@ 1.3 Exercises
## 8 130589 path:hsa00010
## 9 131 path:hsa00010
## 10 160287 path:hsa00010
-## # ℹ 39,303 more rows
+## # ℹ 39,338 more rows
hsa_path_eg |>
group_by(pathway) |>
summarize(genes = list(egid))
-## # A tibble: 367 × 2
+## # A tibble: 371 × 2
## pathway genes
## <chr> <list>
## 1 path:hsa00010 <chr [67]>
## 2 path:hsa00020 <chr [30]>
## 3 path:hsa00030 <chr [31]>
-## 4 path:hsa00040 <chr [36]>
-## 5 path:hsa00051 <chr [34]>
+## 4 path:hsa00040 <chr [37]>
+## 5 path:hsa00051 <chr [35]>
## 6 path:hsa00052 <chr [32]>
-## 7 path:hsa00053 <chr [30]>
+## 7 path:hsa00053 <chr [31]>
## 8 path:hsa00061 <chr [18]>
## 9 path:hsa00062 <chr [28]>
## 10 path:hsa00071 <chr [43]>
-## # ℹ 357 more rows
Update the hsa_path_eg table to include information on gene
symbol and Ensembl id from the org.Hs.eg.db package. Retrieve the
relevant information using mapIds(). How would you deal with
@@ -1282,7 +1282,7 @@
Use left_join() to append pathway descriptions to the
hsa_kegg_anno table.
left_join(hsa_kegg_anno, hsa_pathways, by = "pathway")
-## # A tibble: 39,313 × 5
+## # A tibble: 39,348 × 5
## egid pathway symbol ensembl description
## <chr> <chr> <chr> <chr> <chr>
## 1 10327 path:hsa00010 AKR1A1 ENSG00000117448 <NA>
@@ -1295,7 +1295,7 @@ 1.3 Exercises
## 8 130589 path:hsa00010 GALM ENSG00000143891 <NA>
## 9 131 path:hsa00010 ADH7 ENSG00000196344 <NA>
## 10 160287 path:hsa00010 LDHAL6A ENSG00000166800 <NA>
-## # ℹ 39,303 more rows
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
The main purpose of this package is to provide genomic coordinates @@ -1370,7 +1369,7 @@
transcripts() and genes(). Explore, for example,
ex <- exons(txdb)
ex
-## GRanges object with 966585 ranges and 1 metadata column:
+## GRanges object with 966596 ranges and 1 metadata column:
## seqnames ranges strand | exon_id
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr1 11121-11211 + | 1
@@ -1379,26 +1378,26 @@ 2.3 Exercises
## [4] chr1 11411-11671 + | 4
## [5] chr1 11426-11671 + | 5
## ... ... ... ... . ...
-## [966581] chrX_MU273397v1_alt 314193-314248 - | 966581
-## [966582] chrX_MU273397v1_alt 314813-315236 - | 966582
-## [966583] chrX_MU273397v1_alt 315258-315407 - | 966583
-## [966584] chrX_MU273397v1_alt 316254-316302 - | 966584
-## [966585] chrX_MU273397v1_alt 324527-324923 - | 966585
+## [966592] chrX_MU273397v1_alt 314193-314248 - | 966592
+## [966593] chrX_MU273397v1_alt 314813-315236 - | 966593
+## [966594] chrX_MU273397v1_alt 315258-315407 - | 966594
+## [966595] chrX_MU273397v1_alt 316254-316302 - | 966595
+## [966596] chrX_MU273397v1_alt 324527-324923 - | 966596
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genome
library(ggplot2)
qplot(log10(width(ex)))
## Warning: `qplot()` was deprecated in ggplot2 3.4.0.
-## This warning is displayed once every 8 hours.
+## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
-## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
-
+## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.
+
ex[ which.max(width(ex)) ]
## GRanges object with 1 range and 1 metadata column:
## seqnames ranges strand | exon_id
## <Rle> <IRanges> <Rle> | <integer>
-## [1] chrX 113616300-113963599 - | 887690
+## [1] chrX 113616300-113963599 - | 887716
## -------
## seqinfo: 711 sequences (1 circular) from hg38 genome
Extract all genes, and then keep only the ‘standard’ chromosomes @@ -1412,11 +1411,11 @@
length(gn)
-## [1] 35332
+## [1] 35356
std <- paste0("chr", c(1:22, "X", "Y", "M"))
seqlevels(gn, pruning.mode = "coarse") <- std
length(gn)
-## [1] 35138
+## [1] 35161
seqlevels(gn)
## [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr8" "chr9"
## [10] "chr10" "chr11" "chr12" "chr13" "chr14" "chr15" "chr16" "chr17" "chr18"
@@ -1424,25 +1423,25 @@ 2.3 Exercises
table( seqnames(gn) )
##
## chr1 chr2 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chr10 chr11 chr12 chr13
-## 3485 2489 1992 1567 1709 1723 1703 1294 1493 1507 1984 1772 898
+## 3488 2491 1995 1568 1711 1726 1704 1297 1496 1508 1984 1771 899
## chr14 chr15 chr16 chr17 chr18 chr19 chr20 chr21 chr22 chrX chrY chrM
-## 1106 1157 1365 1704 672 1866 1002 487 755 1271 137 0
+## 1108 1158 1365 1705 672 1865 1001 487 755 1271 136 0
tibble(chr = as.factor(seqnames(gn))) |>
group_by(chr) |>
summarize(n = n())
## # A tibble: 24 × 2
## chr n
## <fct> <int>
-## 1 chr1 3485
-## 2 chr2 2489
-## 3 chr3 1992
-## 4 chr4 1567
-## 5 chr5 1709
-## 6 chr6 1723
-## 7 chr7 1703
-## 8 chr8 1294
-## 9 chr9 1493
-## 10 chr10 1507
+## 1 chr1 3488
+## 2 chr2 2491
+## 3 chr3 1995
+## 4 chr4 1568
+## 5 chr5 1711
+## 6 chr6 1726
+## 7 chr7 1704
+## 8 chr8 1297
+## 9 chr9 1496
+## 10 chr10 1508
## # ℹ 14 more rowsexonsBy() groups exons by gene or transcript; extract exons
grouped by gene. (Challenging!) can you identify genes with exons
@@ -1454,7 +1453,7 @@
## trans
## 1 2 3 4 5 6 7 8 9 10 12 13 16
-## 35332 1706 157 56 28 39 77 75 2 17 2 1 1
+## 35356 1706 157 56 28 38 77 76 2 17 2 1 1
## 31 33 38 39 41 42
## 1 3 1 1 1 1
seqnames( exByGn[ trans > 1 ] )
@@ -1675,7 +1674,7 @@ Extract the coding sequences of each transcript. EnsDb databases provide
annotations from Ensembl and use hence Ensembl style chromosome names (such as
“Y”) while the BSgenome package is based on UCSC annotations that use a naming
@@ -1824,11 +1823,11 @@
library(AnnotationHub)
hub = AnnotationHub()
hub
-## AnnotationHub with 70637 records
-## # snapshotDate(): 2025-04-08
-## # $dataprovider: Ensembl, BroadInstitute, UCSC, ftp://ftp.ncbi.nlm.nih.gov/g...
+## AnnotationHub with 69129 records
+## # snapshotDate(): 2026-04-23
+## # $dataprovider: Ensembl, BroadInstitute, UCSC, Haemcode, FANTOM5,DLRP,IUPHA...
## # $species: Homo sapiens, Mus musculus, Drosophila melanogaster, Rattus norv...
-## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, OrgDb, ChainFile...
+## # $rdataclass: GRanges, TwoBitFile, BigWigFile, EnsDb, Rle, ChainFile, SQLit...
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
@@ -1841,11 +1840,11 @@ 3 AnnotationHub
## AH5015 | Recomb Rate
## AH5016 | ENCODE Pilot
## ... ...
-## AH121712 | Data.table for PubMed Author Information
-## AH121713 | Data.table for PMC
-## AH121714 | Data.table for MeSH (Descriptor)
-## AH121715 | Data.table for MeSH (Qualifier)
-## AH121716 | Data.table for MeSH (SCR)
+## AH122182 | Data.table for PubMed Author Information
+## AH122183 | Data.table for PMC
+## AH122184 | Data.table for MeSH (Descriptor)
+## AH122185 | Data.table for MeSH (Qualifier)
+## AH122186 | Data.table for MeSH (SCR)
Finding what you need in the hubs requires a well formed query or subset call against the metadata columns of the searchable database. What types of metadata columns are available?
@@ -1866,7 +1865,7 @@query(hub, c("granges","homo sapiens","ensembl"))
## AnnotationHub with 121 records
-## # snapshotDate(): 2025-04-08
+## # snapshotDate(): 2026-04-23
## # $dataprovider: Ensembl, UCSC, TargetScan,miRTarBase,USCS,ENSEMBL
## # $species: Homo sapiens, homo sapiens
## # $rdataclass: GRanges
@@ -1892,7 +1891,7 @@ 3 AnnotationHub
locally.
hub["AH110100"]
## AnnotationHub with 1 record
-## # snapshotDate(): 2025-04-08
+## # snapshotDate(): 2026-04-23
## # names(): AH110100
## # $dataprovider: Ensembl
## # $species: homo sapiens
@@ -1923,9 +1922,9 @@ 5 End matter
5.1 Session Info
sessionInfo()
-## R version 4.5.1 (2025-06-13)
+## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
-## Running under: Ubuntu 24.04.2 LTS
+## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
@@ -1947,97 +1946,99 @@ 5.1 Session Info
## [8] methods base
##
## other attached packages:
-## [1] KEGGREST_1.48.1
-## [2] lubridate_1.9.4
-## [3] forcats_1.0.0
-## [4] stringr_1.5.1
-## [5] dplyr_1.1.4
-## [6] purrr_1.0.4
-## [7] readr_2.1.5
-## [8] tidyr_1.3.1
-## [9] tibble_3.3.0
-## [10] ggplot2_3.5.2
+## [1] KEGGREST_1.52.0
+## [2] lubridate_1.9.5
+## [3] forcats_1.0.1
+## [4] stringr_1.6.0
+## [5] dplyr_1.2.1
+## [6] purrr_1.2.2
+## [7] readr_2.2.0
+## [8] tidyr_1.3.2
+## [9] tibble_3.3.1
+## [10] ggplot2_4.0.3
## [11] tidyverse_2.0.0
-## [12] AnnotationHub_3.16.0
-## [13] BiocFileCache_2.16.0
-## [14] dbplyr_2.5.0
-## [15] Gviz_1.52.0
-## [16] biomaRt_2.64.0
+## [12] AnnotationHub_4.2.0
+## [13] BiocFileCache_3.2.0
+## [14] dbplyr_2.5.2
+## [15] Gviz_1.56.0
+## [16] biomaRt_2.68.0
## [17] BSgenome.Hsapiens.UCSC.hg38_1.4.5
-## [18] BSgenome_1.76.0
-## [19] rtracklayer_1.68.0
-## [20] BiocIO_1.18.0
-## [21] Biostrings_2.76.0
-## [22] XVector_0.48.0
-## [23] EnsDb.Hsapiens.v86_2.99.0
-## [24] ensembldb_2.32.0
-## [25] AnnotationFilter_1.32.0
-## [26] TxDb.Hsapiens.UCSC.hg38.knownGene_3.21.0
-## [27] GenomicFeatures_1.60.0
-## [28] GenomicRanges_1.60.0
-## [29] GenomeInfoDb_1.44.0
-## [30] org.Hs.eg.db_3.21.0
-## [31] AnnotationDbi_1.70.0
-## [32] IRanges_2.42.0
-## [33] S4Vectors_0.46.0
-## [34] Biobase_2.68.0
-## [35] BiocGenerics_0.54.0
-## [36] generics_0.1.4
-## [37] BiocStyle_2.36.0
+## [18] BSgenome_1.80.0
+## [19] rtracklayer_1.72.0
+## [20] BiocIO_1.22.0
+## [21] Biostrings_2.80.0
+## [22] XVector_0.52.0
+## [23] GenomeInfoDb_1.48.0
+## [24] EnsDb.Hsapiens.v86_2.99.0
+## [25] ensembldb_2.36.0
+## [26] AnnotationFilter_1.36.0
+## [27] TxDb.Hsapiens.UCSC.hg38.knownGene_3.22.0
+## [28] GenomicFeatures_1.64.0
+## [29] GenomicRanges_1.64.0
+## [30] Seqinfo_1.2.0
+## [31] org.Hs.eg.db_3.23.1
+## [32] AnnotationDbi_1.74.0
+## [33] IRanges_2.46.0
+## [34] S4Vectors_0.50.1
+## [35] Biobase_2.72.0
+## [36] BiocGenerics_0.58.1
+## [37] generics_0.1.4
+## [38] BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
-## [1] RColorBrewer_1.1-3 rstudioapi_0.17.1
-## [3] jsonlite_2.0.0 magrittr_2.0.3
-## [5] magick_2.8.7 farver_2.1.2
-## [7] rmarkdown_2.29 vctrs_0.6.5
-## [9] memoise_2.0.1 Rsamtools_2.24.0
-## [11] RCurl_1.98-1.17 base64enc_0.1-3
-## [13] tinytex_0.57 htmltools_0.5.8.1
-## [15] S4Arrays_1.8.1 progress_1.2.3
-## [17] curl_6.4.0 SparseArray_1.8.0
+## [1] RColorBrewer_1.1-3 rstudioapi_0.18.0
+## [3] jsonlite_2.0.0 magrittr_2.0.5
+## [5] magick_2.9.1 farver_2.1.2
+## [7] rmarkdown_2.31 vctrs_0.7.3
+## [9] memoise_2.0.1 Rsamtools_2.28.0
+## [11] RCurl_1.98-1.18 base64enc_0.1-6
+## [13] tinytex_0.59 htmltools_0.5.9
+## [15] S4Arrays_1.12.0 progress_1.2.3
+## [17] curl_7.1.0 SparseArray_1.12.2
## [19] Formula_1.2-5 sass_0.4.10
-## [21] bslib_0.9.0 htmlwidgets_1.6.4
-## [23] httr2_1.1.2 cachem_1.1.0
-## [25] GenomicAlignments_1.44.0 mime_0.13
-## [27] lifecycle_1.0.4 pkgconfig_2.0.3
-## [29] Matrix_1.7-3 R6_2.6.1
-## [31] fastmap_1.2.0 GenomeInfoDbData_1.2.14
-## [33] MatrixGenerics_1.20.0 digest_0.6.37
-## [35] colorspace_2.1-1 Hmisc_5.2-3
-## [37] RSQLite_2.4.1 labeling_0.4.3
-## [39] filelock_1.0.3 timechange_0.3.0
-## [41] httr_1.4.7 abind_1.4-8
-## [43] compiler_4.5.1 withr_3.0.2
-## [45] bit64_4.6.0-1 htmlTable_2.4.3
-## [47] backports_1.5.0 BiocParallel_1.42.1
-## [49] DBI_1.2.3 rappdirs_0.3.3
-## [51] DelayedArray_0.34.1 rjson_0.2.23
-## [53] tools_4.5.1 foreign_0.8-90
-## [55] nnet_7.3-20 glue_1.8.0
-## [57] restfulr_0.0.16 checkmate_2.3.2
-## [59] cluster_2.1.8.1 gtable_0.3.6
-## [61] tzdb_0.5.0 data.table_1.17.6
-## [63] hms_1.1.3 utf8_1.2.6
-## [65] xml2_1.3.8 BiocVersion_3.21.1
-## [67] pillar_1.10.2 lattice_0.22-7
+## [21] bslib_0.11.0 htmlwidgets_1.6.4
+## [23] httr2_1.2.2 cachem_1.1.0
+## [25] GenomicAlignments_1.48.0 lifecycle_1.0.5
+## [27] pkgconfig_2.0.3 Matrix_1.7-5
+## [29] R6_2.6.1 fastmap_1.2.0
+## [31] MatrixGenerics_1.24.0 digest_0.6.39
+## [33] colorspace_2.1-2 Hmisc_5.2-5
+## [35] RSQLite_3.52.0 labeling_0.4.3
+## [37] filelock_1.0.3 timechange_0.4.0
+## [39] httr_1.4.8 abind_1.4-8
+## [41] compiler_4.6.0 withr_3.0.2
+## [43] bit64_4.8.0 htmlTable_2.5.0
+## [45] S7_0.2.2 backports_1.5.1
+## [47] BiocParallel_1.46.0 DBI_1.3.0
+## [49] rappdirs_0.3.4 DelayedArray_0.38.1
+## [51] rjson_0.2.23 tools_4.6.0
+## [53] foreign_0.8-91 otel_0.2.0
+## [55] nnet_7.3-20 glue_1.8.1
+## [57] restfulr_0.0.16 checkmate_2.3.4
+## [59] cluster_2.1.8.2 gtable_0.3.6
+## [61] tzdb_0.5.0 data.table_1.18.4
+## [63] hms_1.1.4 utf8_1.2.6
+## [65] xml2_1.5.2 BiocVersion_3.23.1
+## [67] pillar_1.11.1 lattice_0.22-9
## [69] bit_4.6.0 deldir_2.0-4
-## [71] biovizBase_1.56.0 tidyselect_1.2.1
-## [73] knitr_1.50 gridExtra_2.3
-## [75] bookdown_0.43 ProtGenerics_1.40.0
-## [77] SummarizedExperiment_1.38.1 xfun_0.52
+## [71] biovizBase_1.60.0 tidyselect_1.2.1
+## [73] knitr_1.51 gridExtra_2.3
+## [75] bookdown_0.46 ProtGenerics_1.44.0
+## [77] SummarizedExperiment_1.42.0 xfun_0.57
## [79] matrixStats_1.5.0 stringi_1.8.7
-## [81] UCSC.utils_1.4.0 lazyeval_0.2.2
-## [83] yaml_2.3.10 evaluate_1.0.4
-## [85] codetools_0.2-20 interp_1.1-6
-## [87] BiocManager_1.30.26 cli_3.6.5
-## [89] rpart_4.1.24 jquerylib_0.1.4
-## [91] dichromat_2.0-0.1 Rcpp_1.1.0
-## [93] png_0.1-8 XML_3.99-0.18
-## [95] parallel_4.5.1 blob_1.2.4
-## [97] prettyunits_1.2.0 latticeExtra_0.6-30
-## [99] jpeg_0.1-11 bitops_1.0-9
-## [101] VariantAnnotation_1.54.1 scales_1.4.0
-## [103] crayon_1.5.3 rlang_1.1.6
+## [81] UCSC.utils_1.8.0 lazyeval_0.2.3
+## [83] yaml_2.3.12 evaluate_1.0.5
+## [85] codetools_0.2-20 cigarillo_1.2.0
+## [87] interp_1.1-6 BiocManager_1.30.27
+## [89] cli_3.6.6 rpart_4.1.27
+## [91] jquerylib_0.1.4 dichromat_2.0-0.1
+## [93] Rcpp_1.1.1-1.1 png_0.1-9
+## [95] XML_3.99-0.23 parallel_4.6.0
+## [97] blob_1.3.0 prettyunits_1.2.0
+## [99] latticeExtra_0.6-31 jpeg_0.1-11
+## [101] bitops_1.0-9 VariantAnnotation_1.58.0
+## [103] scales_1.4.0 crayon_1.5.3
+## [105] rlang_1.2.0
5.2 Acknowledgements
From 185199408b139081741e231000a10d87d6d5c1ad Mon Sep 17 00:00:00 2001
From: billila
Date: Mon, 18 May 2026 13:10:14 +0200
Subject: [PATCH 5/7] update L1_intro
---
intro-to-r-bioc/lab-1-intro-to-r-bioc.Rmd | 4 ++--
intro-to-r-bioc/lab-1-intro-to-r-bioc.html | 8 ++++----
2 files changed, 6 insertions(+), 6 deletions(-)
diff --git a/intro-to-r-bioc/lab-1-intro-to-r-bioc.Rmd b/intro-to-r-bioc/lab-1-intro-to-r-bioc.Rmd
index fdb799c..7e1010c 100644
--- a/intro-to-r-bioc/lab-1-intro-to-r-bioc.Rmd
+++ b/intro-to-r-bioc/lab-1-intro-to-r-bioc.Rmd
@@ -21,11 +21,11 @@ knitr::opts_chunk$set(
**Original Authors:** Martin Morgan, Sonali Arora, Lori Shepherd
**Presenting Author:** [Davide Risso][], [Ilaria Billato][]
-**Date:** 6-11 July, 2025
+**Date:** 24-29 May, 2026
**Back:** [Monday labs](lab-1-intro-to-r-bioc.html)
[Davide Risso]: mailto: davide.risso@unipd.it
-[Ilaria Billato]: mailto: ilaria.billato@phd.unipd.it
+[Ilaria Billato]: mailto: ilaria.billato@unipd.it
This lab has five parts. Each part consists of informative material
diff --git a/intro-to-r-bioc/lab-1-intro-to-r-bioc.html b/intro-to-r-bioc/lab-1-intro-to-r-bioc.html
index 1034fa5..7f417a4 100644
--- a/intro-to-r-bioc/lab-1-intro-to-r-bioc.html
+++ b/intro-to-r-bioc/lab-1-intro-to-r-bioc.html
@@ -10,7 +10,7 @@
-
+
Lab 1: Introduction to R and Bioconductor
@@ -701,7 +701,7 @@
Lab 1: Introduction to R and Bioconductor
-4 July 2025
+18 May 2026
@@ -720,8 +720,8 @@ 4 July 2025
});
Original Authors: Martin Morgan, Sonali Arora, Lori Shepherd
-Presenting Author: Davide Risso, Ilaria Billato
-Date: 6-11 July, 2025
+Presenting Author: Davide Risso, Ilaria Billato
+Date: 24-29 May, 2026
Back: Monday labs
This lab has five parts. Each part consists of informative material
followed by short or intermediate length exercises and case
From 853c1020e61dcad1b2975ad5e8ae1aa82ce4ef14 Mon Sep 17 00:00:00 2001
From: billila
Date: Mon, 18 May 2026 13:11:18 +0200
Subject: [PATCH 6/7] update L1.3
---
intro-to-r-bioc/L1.3-starting-an-analysis.Rmd | 4 +-
.../L1.3-starting-an-analysis.html | 55 +++++++++----------
2 files changed, 29 insertions(+), 30 deletions(-)
diff --git a/intro-to-r-bioc/L1.3-starting-an-analysis.Rmd b/intro-to-r-bioc/L1.3-starting-an-analysis.Rmd
index 3a03838..bc45859 100644
--- a/intro-to-r-bioc/L1.3-starting-an-analysis.Rmd
+++ b/intro-to-r-bioc/L1.3-starting-an-analysis.Rmd
@@ -25,11 +25,11 @@ suppressPackageStartupMessages({
**Original Authors:** Martin Morgan, Sonali Arora, Lori Shepherd
**Presenting Author:** [Davide Risso][], [Ilaria Billato][]
-**Date:** 6-11 July, 2025
+**Date:** 24-29 May, 2026
**Back:** [Monday labs](lab-1-intro-to-r-bioc.html)
[Davide Risso]: mailto: davide.risso@unipd.it
-[Ilaria Billato]: mailto: ilaria.billato@phd.unipd.it
+[Ilaria Billato]: mailto: ilaria.billato@unipd.it
**Objective**: An overview of software available in _Bioconductor_.
diff --git a/intro-to-r-bioc/L1.3-starting-an-analysis.html b/intro-to-r-bioc/L1.3-starting-an-analysis.html
index 5de1c29..4f91272 100644
--- a/intro-to-r-bioc/L1.3-starting-an-analysis.html
+++ b/intro-to-r-bioc/L1.3-starting-an-analysis.html
@@ -10,7 +10,7 @@
-
+
Lab 1.3: Starting an analysis
@@ -701,7 +701,7 @@
Lab 1.3: Starting an analysis
-4 July 2025
+18 May 2026
@@ -742,8 +742,8 @@ Contents
});
Original Authors: Martin Morgan, Sonali Arora, Lori Shepherd
-Presenting Author: Davide Risso, Ilaria Billato
-Date: 6-11 July, 2025
+Presenting Author: Davide Risso, Ilaria Billato
+Date: 24-29 May, 2026
Back: Monday labs
Objective: An overview of software available in Bioconductor.
Lessons learned:
@@ -777,7 +777,7 @@ 1.1 Packages, vignettes, work flo
- 1750 software packages
- Discover and navigate via biocViews
-- Package ‘landing page’, e.g., Gviz
+
- Package ‘landing page’, e.g., Gviz
- Title, author / maintainer, short description, citation,
installation instructions, …, download statistics
@@ -1135,9 +1135,9 @@ 5 End matter
5.1 Session Info
sessionInfo()
-## R version 4.5.1 (2025-06-13)
+## R version 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
-## Running under: Ubuntu 24.04.2 LTS
+## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
@@ -1159,29 +1159,28 @@ 5.1 Session Info
## [8] base
##
## other attached packages:
-## [1] GenomicAlignments_1.44.0 Rsamtools_2.24.0
-## [3] SummarizedExperiment_1.38.1 Biobase_2.68.0
-## [5] MatrixGenerics_1.20.0 matrixStats_1.5.0
-## [7] GenomicRanges_1.60.0 Biostrings_2.76.0
-## [9] GenomeInfoDb_1.44.0 XVector_0.48.0
-## [11] IRanges_2.42.0 S4Vectors_0.46.0
-## [13] BiocGenerics_0.54.0 generics_0.1.4
-## [15] BiocStyle_2.36.0
+## [1] GenomicAlignments_1.48.0 Rsamtools_2.28.0
+## [3] SummarizedExperiment_1.42.0 Biobase_2.72.0
+## [5] MatrixGenerics_1.24.0 matrixStats_1.5.0
+## [7] GenomicRanges_1.64.0 Biostrings_2.80.0
+## [9] Seqinfo_1.2.0 XVector_0.52.0
+## [11] IRanges_2.46.0 S4Vectors_0.50.1
+## [13] BiocGenerics_0.58.1 generics_0.1.4
+## [15] BiocStyle_2.40.0
##
## loaded via a namespace (and not attached):
-## [1] Matrix_1.7-3 jsonlite_2.0.0 compiler_4.5.1
-## [4] BiocManager_1.30.26 crayon_1.5.3 bitops_1.0-9
-## [7] parallel_4.5.1 jquerylib_0.1.4 BiocParallel_1.42.1
-## [10] yaml_2.3.10 fastmap_1.2.0 lattice_0.22-7
-## [13] R6_2.6.1 S4Arrays_1.8.1 knitr_1.50
-## [16] DelayedArray_0.34.1 bookdown_0.43 GenomeInfoDbData_1.2.14
-## [19] bslib_0.9.0 rlang_1.1.6 cachem_1.1.0
-## [22] xfun_0.52 sass_0.4.10 SparseArray_1.8.0
-## [25] cli_3.6.5 digest_0.6.37 grid_4.5.1
-## [28] rstudioapi_0.17.1 lifecycle_1.0.4 evaluate_1.0.4
-## [31] codetools_0.2-20 abind_1.4-8 rmarkdown_2.29
-## [34] httr_1.4.7 tools_4.5.1 htmltools_0.5.8.1
-## [37] UCSC.utils_1.4.0
+## [1] Matrix_1.7-5 jsonlite_2.0.0 compiler_4.6.0
+## [4] BiocManager_1.30.27 crayon_1.5.3 bitops_1.0-9
+## [7] parallel_4.6.0 jquerylib_0.1.4 BiocParallel_1.46.0
+## [10] yaml_2.3.12 fastmap_1.2.0 lattice_0.22-9
+## [13] R6_2.6.1 S4Arrays_1.12.0 knitr_1.51
+## [16] DelayedArray_0.38.1 bookdown_0.46 bslib_0.11.0
+## [19] rlang_1.2.0 cachem_1.1.0 xfun_0.57
+## [22] sass_0.4.10 otel_0.2.0 SparseArray_1.12.2
+## [25] cli_3.6.6 digest_0.6.39 grid_4.6.0
+## [28] rstudioapi_0.18.0 lifecycle_1.0.5 evaluate_1.0.5
+## [31] cigarillo_1.2.0 codetools_0.2-20 abind_1.4-8
+## [34] rmarkdown_2.31 tools_4.6.0 htmltools_0.5.9
5.2 Acknowledgements
From bf70e682f535946dc460d4ed722cdf6a0d4cc7fc Mon Sep 17 00:00:00 2001
From: billila
Date: Mon, 18 May 2026 13:13:18 +0200
Subject: [PATCH 7/7] check regression lab
---
regression/randomness_and_linear.html | 1997 +++++++------------------
regression/randomness_and_linear.qmd | 4 +-
2 files changed, 550 insertions(+), 1451 deletions(-)
diff --git a/regression/randomness_and_linear.html b/regression/randomness_and_linear.html
index c6e2e32..01aba47 100644
--- a/regression/randomness_and_linear.html
+++ b/regression/randomness_and_linear.html
@@ -2,12 +2,12 @@
-
+
-
+
Randomness and the linear model
-
+