There are some tiny probelms in the scripts Zhuoer wrote for matrix processing. Imputation and batch removal were not included in matrix processing.

• Add imputation.
• Add batch removal including RUVs and combat.
• Remove logic error.
• ...

rexSeek

• matrix-process.R

add imputation function

• normalization.R

main function zhuoer wrote lacking imputation, I add this part into normalization main function.

• batch-removal.R

origin script:
home/chenxupeng/projects/exseek/jupyter/matrix_processing.ipynb

combat:
http://jtleek.com/genstats/inst/doc/02_13_batch-effects.html
https://www.bioconductor.org/packages/release/bioc/vignettes/sva/inst/doc/sva.pdf

• plot.R

run well on my account, but failed in Binbin & Xupeng's

• refer_output.ipynb

for function norm_cpm_refer, I add a command, allowing producing refer_id.txt

top k function changed

top k genes use counts top k sum as factor others use count down sum as factor

But what happened to counts value near cut-off? How to solve this problem?

• plot-python_{dataset}.ipynb

modify each pic, fontsize, lable setting

write basic plot function, such as abundance plot, RNA batch by batch, RLE batch, PCA batch

write feature_weight_bar function

top K feature

RLE

have fun, wordcloud combined with pic, and HTML5 learning, use WordPress to make website

kBET exploration

(codes is too complicated, but there are some tricks author ignored )

• plot-python_scirep-2-28.ipynb

PCA alpha function added

• heterogeneity.ipynb

heterogeneity plot, ref-gene acquired from MiRbase, catplot function exploration

• quiz-ROC plot

• quiz.ipynb

(use diff_exp feature to plot ROC, fpr-tpr dataframe provided)

• quiz_tired.ipynb

to make feature-selection files used for ROC plot

• quiz_right.ipynb

ROC plot

• news [Lightning Network]

(http://lightning.pictures/)

https://satoshis.place/

https://cloud.google.com/bigquery/?hl=zh-cn