Preface
Author
Became a data scientist
How did I started programing?
More background
1
Introduction
2
Basic biology
2.1
Blood samples
2.2
DNA
2.2.1
Common terms in populational genetics
2.3
RNA
2.4
Protein
3
Biomakers in pharmaceutical settings
3.1
Reference
4
Immunophenotyping
4.1
flowcytometry data
4.2
Cell type deconvolution
5
Bulk RNA seqeuncing analysis
5.1
Preprocessing
5.1.1
Filtering
5.1.2
Normalization
5.2
Differential expression analysis
5.3
Differential co-expression analysis
6
Nanostring technology
7
Proteinomics
7.1
OLINK
7.2
Semalogic
8
Pharmacogenetics analysis
8.1
Basic genetic concepts
8.2
Pharmacoscan
8.2.1
Software for analysis
8.2.2
Single nucleotide variant data
8.3
HLA region targeted sequencing
8.3.1
HLA nomenclature and sequencing output
8.3.2
DRB1 and DRB3/DRB4/DRB5
8.4
Reference
9
Multiplicity
9.1
Family wise comparison
9.2
False discovery rate (FDR)
9.2.1
Adjusted p-value
9.3
Local FDR
10
Power and sample size
10.1
Sample size calculation in
in vitro
studies
10.2
Sample size calculation in
omics
studies
10.3
Sample size calculation for calibration studies
10.3.1
1. Agreement between two measurements
10.3.2
2. Sample size for calibrations
10.3.3
Choosing samples range
10.3.4
Estimating coefficient variation (CV) within the same measures
10.3.5
Final consideration for sample size
10.3.6
Reference
11
Detection limit of assays
11.1
Upper and lower detection limit
11.2
Violation in distribution assumption and bias in effect estimate
11.3
Methods for detection limit problem
11.3.1
Substitution
11.3.2
KM
11.3.3
ROS
11.3.4
MLE
11.4
Comparisons of the methods
12
Pharmacaldynamic analysis
12.1
Variance covariance in repeated measures
12.2
Hierarchical modeling
12.2.1
GEE vs. GLMM
13
Pathway analysis/ gene set enrichement analysis
13.1
Gene set enrichment analysis (GSEA)
13.2
Single sample gene set enrichment analysis (ssGSEA)
13.3
Gene set variation analysis (GSVA)
13.4
CAMERA
13.5
Comparisons of of gene set enrichment analysis methods
13.5.1
Type I error rate
13.5.2
power
13.5.3
Biological relavence
13.6
Clustering analysis
13.6.1
Low dimensional clustering
13.6.2
High dimensional clustering
14
Visualization
14.1
packages
14.2
Basic static plot
14.3
Special plots
14.3.1
Genetic analysis
14.3.2
RNA seq analysis
14.3.3
Clustering analysis
4
Immunophenotyping
4.1
flowcytometry data
4.2
Cell type deconvolution