Analysis¶
The analysis of the data is can be done after the pipeline.
Getting an analysis report¶
The analysis report is the sequencing and flow cytometry data combined. It has all count and frequencies of the flow data and the frequencies of VRC01 class among the sequences. It will then combine those frequencies to give a final frequency of VRC01 among some cell type phenotype.
The following will produce an analysis report and combine data. It will output a flow
$ g00x g002 analysis report -s g002/G002/output/final_df.feather -f g002/G002/output/flow_output.feather -o g002/G002/output/flow_and_sequencing
from g00x.data import Data
from g00x.analysis.report import combine_seq_and_flow
data = Data()
sequencing_dataframe_path = "g002/G002/output/final_df.feather"
flow_dataframe_path = "g002/G002/output/flow_output.feather"
# input the sequeences to feather
sequencing_dataframe = pd.read_feather(sequencing_dataframe_path)
flow_dataframe = pd.read_feather(flow_dataframe_path)
# generate three different dataframe
seq_and_flow_df, seq_and_flow_df_long_name, seq_and_flow_df_long_form = combine_seq_and_flow(
data, sequencing_dataframe, flow_dataframe
)
This will output a flow_and_sequencing.feather, flow_and_sequencing_long_names.feather, and flow_and_sequencing_long_form.feather.
The long form is the long form dataframe:
| run_purpose | run_date | pubID | ptid | group | weeks | visit_id | probe_set | sample_type | value_type | value | short_name | long_name | pbmc_gate_expression | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 319 | PreS | 2022-10-24 | G002-516 | G002516 | 1 | 16 | V270 | Cg28v2 | PBMC | count | 418948 | B cells_cell_count | B cells_cell_count | P4 |
| 2326 | PreS | 2022-10-31 | G002-884 | G002884 | 3 | -5 | V091 | eODGT8 | PBMC | count | 18298 | IgG-IgA-/IgM+/KO-_cell_count | IgG-IgA-/IgM+/KO-_cell_count | P25 |
The long names are pivoted:
| run_purpose | run_date | pubID | ptid | group | weeks | visit_id | probe_set | sample_type | B cells_cell_count | CD19+/CD20-/CD27+CD38+/Antigen++/KO-_cell_count | CD19+/CD20-/CD27+CD38+/Antigen++_cell_count | CD19+/CD20-/CD27+CD38+/KO-/Epitope++_cell_count | CD19+/CD20-/CD27+CD38+/KO-_cell_count | CD19+/CD20-/CD27+CD38+_cell_count | CD19+/CD20-_cell_count | CD19+_cell_count | Dump-_cell_count | Frequency of IGD- sequences that are VRC01-class | Frequency of IGHA sequences that are VRC01-class | Frequency of IGHD sequences that are VRC01-class | Frequency of IGHG sequences that are VRC01-class | Frequency of IGHM sequences that are VRC01-class | Frequency of VRC01-class sequences among IgA | Frequency of VRC01-class sequences among IgD- | Frequency of VRC01-class sequences among IgG | Frequency of VRC01-class sequences among IgM | IgD+ B cells_cell_count | IgD+/Antigen++/KO-_cell_count | IgD+/Antigen++_cell_count | IgD+/KO-/Epitope++_cell_count | IgD+/KO-_cell_count | IgD- B cells_cell_count | IgD-/Antigen++/KO-_cell_count | IgD-/Antigen++_cell_count | IgD-/KO-/Epitope++_cell_count | IgD-/KO-_cell_count | IgG-IgA-/IgM+/Antigen++/KO-_cell_count | IgG-IgA-/IgM+/Antigen++_cell_count | IgG-IgA-/IgM+/KO-/Epitope++_cell_count | IgG-IgA-/IgM+/KO-_cell_count | IgG-IgA-/IgM+_cell_count | IgG-IgA-_cell_count | IgG-IgM-/IgA+/Antigen++/KO-_cell_count | IgG-IgM-/IgA+/Antigen++_cell_count | IgG-IgM-/IgA+/KO-/Epitope++_cell_count | IgG-IgM-/IgA+/KO-_cell_count | IgG-IgM-/IgA+_cell_count | IgG-IgM-IgD-_cell_count | IgM-IgA-/IgG+/Antigen++/KO-_cell_count | IgM-IgA-/IgG+/Antigen++_cell_count | IgM-IgA-/IgG+/KO-/Epitope++_cell_count | IgM-IgA-/IgG+/KO-_cell_count | IgM-IgA-/IgG+_cell_count | IgM-IgA-_cell_count | Lymphocytes_cell_count | Number of IGD- sequences that are VRC01-class | Number of IGHA sequences that are VRC01-class | Number of IGHD sequences that are VRC01-class | Number of IGHG sequences that are VRC01-class | Number of IGHM sequences that are VRC01-class | Percent IgA^{+}KO^- among Ag^{++} | Percent IgD^{-}KO^{-} among Ag^{++} | Percent IgG^{+}KO^- among Ag^{++} | Percent IgM{+}KO^- among Ag^{++} | Percent PB{+}KO^- among Ag^{++} | Percent antigen-specific among IgD^- | Percent antigen-specific among IgG^{+} | Percent antigen-specific among IgM | Percent epitope-specific (KO^-Ag^{++}) among IgA^{+} | Percent epitope-specific (KO^-Ag^{++}) among IgD^- | Percent epitope-specific (KO^-Ag^{++}) among IgG^{+} | Percent epitope-specific (KO^-Ag^{++}) among IgM | Percent epitope-specific (KO^-Ag^{++}) among PB | Percent of antigen-specific among IgA^{+} | Percent of antigen-specific among PB | Singlets_cell_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 46 | PreS | 2022-10-31 | G002-884 | G002884 | 3 | 16 | V500 | eODGT8 | PBMC | 415845 | 0 | 5 | 0 | 1915 | 1931 | 4146 | 414462 | 472678 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 255368 | 117 | 177 | 117 | 253414 | 161568 | 391 | 1504 | 394 | 158975 | 3 | 4 | 3 | 16985 | 17142 | 25395 | 13 | 22 | 13 | 38244 | 38507 | 46508 | 340 | 1383 | 340 | 92983 | 94964 | 102932 | 555794 | nan | nan | nan | nan | nan | 59.0909 | 25.9973 | 24.5842 | 0.0176626 | 0 | 0.930877 | 1.45634 | 75 | 0.0337601 | 0.24386 | 0.35803 | 0.0175009 | 0 | 0.0571325 | 0.258933 | 550934 |
| 2 | PreS | 2022-08-25 | G002-516 | G002516 | 1 | 8 | V200 | eODGT8 | PBMC | 1.2981e+06 | 1 | 1 | 1 | 15503 | 16641 | 121174 | 1.27163e+06 | 1.34031e+06 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 864372 | 456 | 718 | 409 | 801485 | 433730 | 554 | 1070 | 469 | 404609 | 38 | 54 | 35 | 69781 | 74779 | 121496 | 47 | 97 | 51 | 96230 | 103053 | 148151 | 336 | 709 | 288 | 189984 | 203562 | 254258 | 1.37136e+06 | nan | nan | nan | nan | nan | 48.4536 | 51.7757 | 47.3907 | 0.0544561 | 100 | 0.246697 | 0.348297 | 64.8148 | 0.0494891 | 0.108132 | 0.14148 | 0.0468046 | 0.00600925 | 0.0941263 | 0.00600925 | 1.36246e+06 |
The short names are also pivoted
| run_purpose | run_date | pubID | ptid | group | weeks | visit_id | probe_set | sample_type | B cells_cell_count | CD19+/CD20-/CD27+CD38+/Antigen++/KO-_cell_count | CD19+/CD20-/CD27+CD38+/Antigen++_cell_count | CD19+/CD20-/CD27+CD38+/KO-/Epitope++_cell_count | CD19+/CD20-/CD27+CD38+/KO-_cell_count | CD19+/CD20-/CD27+CD38+_cell_count | CD19+/CD20-_cell_count | CD19+_cell_count | Dump-_cell_count | IgD+ B cells_cell_count | IgD+/Antigen++/KO-_cell_count | IgD+/Antigen++_cell_count | IgD+/KO-/Epitope++_cell_count | IgD+/KO-_cell_count | IgD- B cells_cell_count | IgD-/Antigen++/KO-_cell_count | IgD-/Antigen++_cell_count | IgD-/KO-/Epitope++_cell_count | IgD-/KO-_cell_count | IgG-IgA-/IgM+/Antigen++/KO-_cell_count | IgG-IgA-/IgM+/Antigen++_cell_count | IgG-IgA-/IgM+/KO-/Epitope++_cell_count | IgG-IgA-/IgM+/KO-_cell_count | IgG-IgA-/IgM+_cell_count | IgG-IgA-_cell_count | IgG-IgM-/IgA+/Antigen++/KO-_cell_count | IgG-IgM-/IgA+/Antigen++_cell_count | IgG-IgM-/IgA+/KO-/Epitope++_cell_count | IgG-IgM-/IgA+/KO-_cell_count | IgG-IgM-/IgA+_cell_count | IgG-IgM-IgD-_cell_count | IgM-IgA-/IgG+/Antigen++/KO-_cell_count | IgM-IgA-/IgG+/Antigen++_cell_count | IgM-IgA-/IgG+/KO-/Epitope++_cell_count | IgM-IgA-/IgG+/KO-_cell_count | IgM-IgA-/IgG+_cell_count | IgM-IgA-_cell_count | Lymphocytes_cell_count | Singlets_cell_count | num_IGHA_vrc01_class_sequences | num_IGHD_vrc01_class_sequences | num_IGHG_vrc01_class_sequences | num_IGHM_vrc01_class_sequences | num_igdneg_vrc01_class_sequences | percent_IGHA_vrc01_class_sequences | percent_IGHD_vrc01_class_sequences | percent_IGHG_vrc01_class_sequences | percent_IGHM_vrc01_class_sequences | percent_ag_among_iga | percent_ag_among_igd_neg | percent_ag_among_igg | percent_ag_among_igm | percent_ag_among_pb | percent_ep_among_iga | percent_ep_among_igd_neg | percent_ep_among_igg | percent_ep_among_igm | percent_ep_among_pb | percent_igako_among_ag | percent_igdneg_vrc01_class_sequences | percent_iggko_among_ag | percent_igmko_among_ag | percent_ko_among_ag_igd_neg | percent_pbko_among_ag | percent_vrc01_among_iga | percent_vrc01_among_igd_neg | percent_vrc01_among_igg | percent_vrc01_among_igm | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 85 | PreS | 2022-11-08 | G002-852 | G002852 | 1 | 8 | V200 | eODGT8 | PBMC | 992632 | 3 | 3 | 3 | 1357 | 1374 | 8892 | 982808 | 1.02292e+06 | 705541 | 833 | 1131 | 833 | 698255 | 289703 | 930 | 2231 | 937 | 285912 | 44 | 57 | 44 | 30308 | 30561 | 44012 | 43 | 82 | 44 | 84999 | 85750 | 98725 | 634 | 1548 | 634 | 147423 | 149684 | 162491 | 1.0971e+06 | 1.08986e+06 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.0956268 | 0.770099 | 1.03418 | 77.193 | 0.218341 | 0.051312 | 0.323435 | 0.423559 | 0.143974 | 0.218341 | 52.439 | nan | 40.9561 | 0.145176 | 41.6853 | 100 | nan | nan | nan | nan |
| 84 | PreS | 2022-11-08 | G002-852 | G002852 | 1 | 4 | V160 | eODGT8 | PBMC | 1.10867e+06 | 79 | 131 | 79 | 1691 | 1782 | 10146 | 1.09833e+06 | 1.15407e+06 | 820575 | 2017 | 3147 | 2017 | 813873 | 290959 | 1817 | 3937 | 1830 | 287026 | 80 | 145 | 80 | 33509 | 33766 | 47452 | 78 | 119 | 80 | 84703 | 85268 | 98461 | 792 | 1659 | 792 | 143728 | 145494 | 158611 | 1.23712e+06 | 1.2314e+06 | nan | nan | nan | nan | nan | nan | nan | nan | nan | 0.13956 | 1.35311 | 1.14025 | 55.1724 | 7.35129 | 0.0938218 | 0.628955 | 0.544352 | 0.236925 | 4.43322 | 65.5462 | nan | 47.7396 | 0.238742 | 46.1519 | 60.3053 | nan | nan | nan | nan |
Count¶
Easily count the amount of samples we have.
$ g00x g002 analysis count -f g002/G002/output/flow_output.feather -o count
This will output:
