Commit 9ba5352d authored by Ruqian Lyu's avatar Ruqian Lyu
Browse files

docker file update

parent a86f3d1d
......@@ -194,10 +194,13 @@ 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 case_study_data /home/jovyan/case_study_data
RUN ls -la /home/jovyan/
COPY mig-sc-workshop-2019-data.tar.gz /home/jovyan/data/
COPY mig_2019_scrnaseq-workshop-data.tar.gz /home/jovyan
RUN tar -xzvf mig_2019_scrnaseq-workshop-data.tar.gz -C /home/jovyan/data/ && rm mig_2019_scrnaseq-workshop-data.tar.gz
RUN ls -la /home/jovyan/data/
#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/ && \
......
......@@ -29,7 +29,7 @@ mkdir LD_cr_counts
cd LD_cr_counts
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3612nnn/GSM3612832/suppl/GSM3612832_LD_genes.tsv.gz
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3612nnn/GSM3612832/suppl/GSM3612832_LD_barcodes.tsv.gz
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3612nnn/GSM3612832/suppl/GSM3612831_LD_matrix.mtx.gz
wget ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM3612nnn/GSM3612832/suppl/GSM3612832_LD_matrix.mtx.gz
cd ..
mkdir Ctrl_cr_counts
......@@ -52,9 +52,9 @@ library(Matrix)
### Read in control cells
cellbarcodes <- read.table("../case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_barcodes.tsv.gz",stringsAsFactors = FALSE)
genenames <- read.table("../case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_genes.tsv.gz",stringsAsFactors = FALSE)
molecules <- readMM("../case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_matrix.mtx.gz")
cellbarcodes <- read.table("./case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_barcodes.tsv.gz",stringsAsFactors = FALSE)
genenames <- read.table("./case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_genes.tsv.gz",stringsAsFactors = FALSE)
molecules <- readMM("./case_study_data/retinal/GEO_downloads/Ctrl/GSM3612831_ctrl_matrix.mtx.gz")
head(cellbarcodes)
......@@ -74,9 +74,9 @@ sce_ctrl <- SingleCellExperiment(
sce_ctrl
## Read in LD cells
cellbarcodes <- read.table("../case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_barcodes.tsv.gz",stringsAsFactors = FALSE)
genenames <- read.table("../case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_genes.tsv.gz",stringsAsFactors = FALSE)
molecules <- readMM("../case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_matrix.mtx.gz")
cellbarcodes <- read.table("./case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_barcodes.tsv.gz",stringsAsFactors = FALSE)
genenames <- read.table("./case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_genes.tsv.gz",stringsAsFactors = FALSE)
molecules <- readMM("./case_study_data/retinal/GEO_downloads/LD/GSM3612832_LD_matrix.mtx.gz")
head(cellbarcodes)
......@@ -246,6 +246,15 @@ plot(sce_cr$total_features_by_counts, sce_cr$pct_counts_Mito,
sce_cr <- sce_cr[, keep]
table(sce_cr$Sample)
# relations of QC metrics respect to each other ( in rough aggreement)
par(mfrow=c(1,2))
plot(sce_cr$total_features_by_counts, sce_cr$total_counts/1e6,
xlab="Number of expressed genes",
ylab="Library size (millions)")
plot(sce_cr$total_features_by_counts, sce_cr$pct_counts_Mito,
xlab="Number of expressed genes",
ylab="Mitochondrial proportion (%)")
```
### Doublets
......
......@@ -113,6 +113,8 @@ umi <- calculateQCMetrics(
MT = isSpike(umi, "MT")
)
)
umi
colData(umi)
```
......@@ -218,6 +220,8 @@ filter_by_ERCC <- umi$batch != "NA19098.r2"
table(filter_by_ERCC)
filter_by_MT <- umi$pct_counts_MT < 10
table(filter_by_MT)
filter_by_total_counts <- umi$total_counts > 25000
#filter_by_expr_features <- umi$total_features_by_counts <7000
```
__Exercise 4__
......@@ -247,6 +251,7 @@ umi$use <- (
)
```
```{r}
table(umi$use)
```
......
......@@ -319,7 +319,7 @@ There are different ways of implementing the RGE framework, `Monocle 2` uses `DD
DDRTree returns a principal tree of the centroids of cell clusters in low dimension, pseudotime is derived for individual cells by calculating geomdestic distance of their projections onto the tree from the root (user-defined or arbitrarily assigned).
__Note__ Informally, a principal graph is like a principal curve which passes through the middle of a data set but is
_Note_ Informally, a principal graph is like a principal curve which passes through the middle of a data set but is
allowed to have branches.
```{r monocle2-all-genes, message=FALSE, warning=FALSE,include=TRUE}
......
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