diff --git a/Dockerfile b/Dockerfile
index 5daf4ed8f52054eda978ecd8aa2fe9fbc207c8ad..bac78a1291055a9e522af6daf0140f2dac99d986 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -170,17 +170,19 @@ RUN sudo apt-get install -yq --no-install-recommends libudunits2-0 libudunits2-d
 
 RUN Rscript -e 'devtools::install_github(c("cole-trapnell-lab/leidenbase"))'
 
-RUN Rscript -e 'devtools::install_github(c("cole-trapnell-lab/monocle3"))'
 RUN Rscript -e 'devtools::install_github(c("ChristophH/sctransform"))'
 
 
 # install github packages
-RUN Rscript -e 'devtools::install_github(c("immunogenomics/harmony", "LTLA/beachmat", "MarioniLab/DropletUtils", "tallulandrews/M3Drop", "hemberg-lab/scRNA.seq.funcs"))'
 RUN Rscript -e 'install.packages(c("foreach"))'
 RUN Rscript -e 'install.packages(c("iterators"))'
 RUN Rscript -e 'install.packages(c("rsample"))'
 RUN Rscript -e 'install.packages(c("Rcpp"))'
 RUN Rscript -e 'install.packages(c("rstan"))'
+RUN Rscript -e 'install.packages(c("gam"))'
+RUN Rscript -e 'devtools::install_github(c("cole-trapnell-lab/monocle3"))'
+RUN Rscript -e 'devtools::install_github(c("immunogenomics/harmony", "LTLA/beachmat", "MarioniLab/DropletUtils", "tallulandrews/M3Drop", "hemberg-lab/scRNA.seq.funcs"))'
+
 # install github packages
 RUN Rscript -e 'devtools::install_github(c("Vivianstats/scImpute", "theislab/kBET", "kieranrcampbell/ouija", "hemberg-lab/scfind"))'
 
@@ -191,6 +193,19 @@ COPY ./poststart.sh /home/jovyan
 
 # add course files
 COPY course_files /home/jovyan
+COPY case_study_data/case_study.Rmd /home/jovyan/
+COPY case_study_data/pre_processing_fq.Rmd /home/jovyan/
+
+COPY mig-sc-workshop-2019-data.tar.gz  /home/jovyan/data/
+# cp data/droplet_id_example_per_barcode.txt.gz  /home/jovyan/data/ && \
+#     cp data/pancreas -r  /home/jovyan/data/ && \
+#     cp data/tung -r /home/jovyan/data/ && \
+#     cp data/2000_reference.transcripts.fa  /home/jovyan/data/ && \
+#     cp data/droplet_id_example_truth.gz    /home/jovyan/data/ && \
+#     cp data/deng -r  /home/jovyan/data/ && \
+#     cp data/EXAMPLE.cram   /home/jovyan/data/ && \
+#     cp data/sce -r /home/jovyan/data/ 
+
 RUN chmod -R 777 /home/jovyan
 
 USER $NB_UID
diff --git a/course_files/book.bib b/course_files/book.bib
index 0af21371c40031f925914340561c1d448b7e75c0..e37e01a4507a3d03465b938c100c3e135a73e212 100644
--- a/course_files/book.bib
+++ b/course_files/book.bib
@@ -1127,3 +1127,43 @@ doi = {10.18637/jss.v059.i10}
   language = "en",
   doi      = "10.1101/574574"
 }
+
+
+@ARTICLE{Bais2019-hf,
+  title    = "scds: Computational Annotation of Doublets in {Single-Cell} {RNA}
+              Sequencing Data",
+  author   = "Bais, Abha S and Kostka, Dennis",
+  abstract = "MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technologies
+              enable the study of transcriptional heterogeneity at the
+              resolution of individual cells and have an increasing impact on
+              biomedical research. However, it is known that these methods
+              sometimes wrongly consider two or more cells as single cells, and
+              that a number of so-called doublets is present in the output of
+              such experiments. Treating doublets as single cells in downstream
+              analyses can severely bias a study's conclusions, and therefore
+              computational strategies for the identification of doublets are
+              needed. RESULTS: With scds, we propose two new approaches for in
+              silico doublet identification: Co-expression based doublet
+              scoring (cxds) and binary classification based doublet scoring
+              (bcds). The co-expression based approach, cxds, utilizes
+              binarized (absence/presence) gene expression data and, employing
+              a binomial model for the co-expression of pairs of genes, yields
+              interpretable doublet annotations. bcds, on the other hand, uses
+              a binary classification approach to discriminate artificial
+              doublets from original data. We apply our methods and existing
+              computational doublet identification approaches to four data sets
+              with experimental doublet annotations and find that our methods
+              perform at least as well as the state of the art, at comparably
+              little computational cost. We observe appreciable differences
+              between methods and across data sets and that no approach
+              dominates all others. In summary, scds presents a scalable,
+              competitive approach that allows for doublet annotation of data
+              sets with thousands of cells in a matter of seconds.
+              AVAILABILITY: scds is implemented as a Bioconductor R package
+              (doi: 10.18129/B9.bioc.scds). SUPPLEMENTARY INFORMATION:
+              Supplementary data are available at Bioinformatics online.",
+  journal  = "Bioinformatics",
+  month    =  sep,
+  year     =  2019,
+  language = "en"
+}