Read FCS files#
In this notebook, we load an fcs file into the anndata format, move the forward scatter (FCS) and sideward scatter (SSC) information to the .obs section of the anndata file and perform compensation on the data.
import readfcs
import pytometry as pm
Read data from readfcs package example.
path_data = readfcs.datasets.example()
adata = pm.io.read_fcs(path_data)
adata
AnnData object with n_obs × n_vars = 65016 × 16
var: 'channel', 'marker'
uns: 'meta'
adata.var
| channel | marker | |
|---|---|---|
| FSC-A | FSC-A | |
| FSC-H | FSC-H | |
| SSC-A | SSC-A | |
| KI67 | B515-A | KI67 |
| CD3 | R780-A | CD3 |
| CD28 | R710-A | CD28 |
| CD45RO | R660-A | CD45RO |
| CD8 | V800-A | CD8 |
| CD4 | V655-A | CD4 |
| CD57 | V585-A | CD57 |
| VIVID / CD14 | V450-A | VIVID / CD14 |
| CCR5 | G780-A | CCR5 |
| CD19 | G710-A | CD19 |
| CD27 | G660-A | CD27 |
| CCR7 | G610-A | CCR7 |
| CD127 | G560-A | CD127 |
The .var section of the AnnData object contains the channel information. We set the marker names as var_names by default. In addition, we save the channel information in the "channel" column.
adata.var
| channel | marker | |
|---|---|---|
| FSC-A | FSC-A | |
| FSC-H | FSC-H | |
| SSC-A | SSC-A | |
| KI67 | B515-A | KI67 |
| CD3 | R780-A | CD3 |
| CD28 | R710-A | CD28 |
| CD45RO | R660-A | CD45RO |
| CD8 | V800-A | CD8 |
| CD4 | V655-A | CD4 |
| CD57 | V585-A | CD57 |
| VIVID / CD14 | V450-A | VIVID / CD14 |
| CCR5 | G780-A | CCR5 |
| CD19 | G710-A | CD19 |
| CD27 | G660-A | CD27 |
| CCR7 | G610-A | CCR7 |
| CD127 | G560-A | CD127 |
The .uns['meta'] section contains the header information from the FCS file.
adata.uns["meta"]
{'filename': '100715.fcs',
'sys': 'Windows XP 5.1',
'total_events': 65016,
'tube_name': 'Tube_025',
'exp_name': '070717_AB02_tb',
'cytometer': 'Main Aria (FACSAria)',
'creator': 'LYSYS',
'operator': 'Administrator',
'cst_pass': False,
'threshold': {'channel': 'FSC', 'threshold': '27000'},
'processing_date': '2007-07-17T15:38:06',
'spill': KI67 CD3 CD28 CD45RO CD8 CD4 \
KI67 1.000000 0.000000 0.000000 0.000088 0.000249 0.000645
CD3 0.000000 1.000000 0.071188 0.148448 0.338903 0.009717
CD28 0.000000 0.331405 1.000000 0.061965 0.120979 0.004053
CD45RO 0.000000 0.088621 0.389424 1.000000 0.029759 0.065553
CD8 0.000000 0.136618 0.010757 0.000000 1.000000 0.000156
CD4 0.000000 0.000124 0.019463 0.218206 0.004953 1.000000
CD57 0.000000 0.000000 0.000000 0.000000 0.001056 0.002287
VIVID / CD14 0.000000 0.000000 0.000000 0.000000 0.000000 0.008118
CCR5 0.003122 0.008526 0.001024 0.001163 0.125401 0.018142
CD19 0.002015 0.069645 0.194715 0.001008 0.151611 0.001270
CD27 0.001685 0.054340 0.277852 0.343008 0.061753 0.077523
CCR7 0.000000 0.008713 0.048213 0.073190 0.150563 0.386293
CD127 0.001684 0.000000 0.000000 0.000095 0.003463 0.015712
CD57 VIVID / CD14 CCR5 CD19 CD27 CCR7 \
KI67 0.007198 0.0 0.000000 0.000131 0.000067 0.000582
CD3 0.000000 0.0 0.301380 0.007478 0.012354 0.000000
CD28 0.000000 0.0 0.109117 0.100314 0.005832 0.000000
CD45RO 0.000000 0.0 0.031294 0.039306 0.091375 0.000396
CD8 0.000000 0.0 0.483235 0.014858 0.000000 0.000000
CD4 0.003583 0.0 0.001311 0.029646 0.408902 0.006506
CD57 1.000000 0.0 0.000389 0.000194 0.000000 0.062551
VIVID / CD14 0.170066 1.0 0.000000 0.000000 0.000000 0.000000
CCR5 0.193646 0.0 1.000000 0.066898 0.161456 0.286823
CD19 0.007133 0.0 1.150032 1.000000 0.016077 0.014674
CD27 0.004263 0.0 0.497488 0.743923 1.000000 0.010329
CCR7 0.101896 0.0 0.370277 0.613490 1.218024 1.000000
CD127 0.174122 0.0 0.023802 0.049474 0.132511 0.239216
CD127
KI67 0.002520
CD3 0.000000
CD28 0.000000
CD45RO 0.000057
CD8 0.000000
CD4 0.000119
CD57 0.132484
VIVID / CD14 0.000000
CCR5 1.238037
CD19 0.055352
CD27 0.037635
CCR7 0.065211
CD127 1.000000 }