library(tidyverse)
library(knitr)
df_exp <- read_csv('data/experiments.csv', col_types=list())
df_kwd <- read_csv('data/keywords.csv', col_types=list())
df_fcs <- read_csv('data/fcs_files.csv', col_types=list())
df_chl <- read_csv('data/fcs_channels_resolved.csv', col_types=list())
set.seed(1)df_exp %>% select(-attachments) %>% sample_n(5) %>% kable()
| exp_id |
exp_name |
investigators |
researchers |
n_fcs_files_total |
n_fcs_files_parse |
| FR-FCM-ZZCB |
Effect of monensin on IFNg mRNA and Protein expression |
Emily Park |
Chip Lomas |
12 |
12 |
| FR-FCM-ZZG6 |
data analysis activated sludge |
Susanne Günther |
Susanne Günther |
10 |
10 |
| FR-FCM-ZZUE |
B-1 phenotype of peripheral CD19+ Mac-1+ B cells of a representative recipient of DL1-Fc 2.5-activated ESC-HSC_Fig.1E, S1B, S2A-B |
yifen lu |
yifen lu |
32 |
30 |
| FR-FCM-ZYWC |
BRAF and MEK inhibitor therapy eliminates nestin expressing melanoma cells in human tumors Experiment 5 (Additional Therapy Naive Patients All Cells) |
Jonathan Irish |
Deon Doxie |
3 |
3 |
| FR-FCM-ZZ8P |
Figure 1-B Autophagy Sonication |
Kui Lin |
Michael Degtyarev |
84 |
30 |
df_kwd %>% sample_n(5) %>% kable()
| exp_id |
keyword |
| FR-FCM-ZYHL |
automated data analysis |
| FR-FCM-ZYMF |
mass cytometry |
| FR-FCM-ZZVF |
FACSDiva |
| FR-FCM-ZZU7 |
Silver nanoparticles |
| FR-FCM-ZZ3L |
fluorescence |
df_fcs %>% sample_n(5) %>% kable()
| exp_id |
version |
filename |
size |
creator |
n_params |
| FR-FCM-ZZ8S |
FCS3.0 |
DMSO_D1_D01.fcs |
239547 |
NA |
5 |
| FR-FCM-ZZ7U |
FCS3.0 |
768243.fcs |
15093818 |
BD FACSDiva Software Version 6.1.1 |
16 |
| FR-FCM-ZY6D |
FCS3.0 |
Compensation Controls_APC Stained Control_008.fcs |
282686 |
BD FACSDiva Software Version 8.0.1 |
14 |
| FR-FCM-ZZFM |
FCS2.0 |
TS22 652 P6…Live.fcs |
24509776 |
FlowJo |
22 |
| FR-FCM-ZYAY |
FCS2.0 |
Donor9 NK phenoD4_TW NK+IL-2 mixA 004.LMD |
107989 |
NA |
13 |
df_chl %>% sample_n(5) %>% kable()
| param_channel |
param_name |
filename |
exp_id |
term |
precedence |
param |
resolution |
| Time |
NA |
AJO 3min no tto_0 min PETG.fcs |
FR-FCM-ZY2D |
TIME |
3 |
Time |
lookup |
| Tm169Di |
169Tm_ICOS |
081216-Mike-HIMC ctrls-885d4_01_1.fcs |
FR-FCM-ZYAJ |
ICOS |
5 |
CD278 |
lookup |
| Yb171Di |
171Yb_CD27 |
Plate 8_9.fcs |
FR-FCM-ZYT6 |
CD27 |
5 |
CD27 |
lookup |
| Y4-A |
PE-Vio770-A |
A555_Acceptor.fcs |
FR-FCM-ZZR6 |
NA |
6 |
PE-Vio770-A |
original |
| Xe131Di |
131Xe |
Clambey LO 11022016 IL10 KO lung 4_01.fcs |
FR-FCM-ZYDW |
NA |
6 |
131Xe |
original |
df_chl %>%
# Ignore any parameter names that couldn't be matched (results are too messy otherwise)
filter(resolution == 'lookup') %>%
# Get distinct set of experiment ids and parameter names
group_by(exp_id, param) %>% tally %>% select(-n) %>% ungroup %>%
# Count and plot parameter frequencies
group_by(param) %>% tally %>% arrange(desc(n)) %>% head(100) %>%
mutate(param=reorder(param, -n)) %>%
ggplot(aes(x=param, y=n)) + geom_bar(stat='identity') +
labs(x='Parameter', y='Num Datasets', title='Top 100 Parameters') +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
