Upload 13 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hallmark_model.keras filter=lfs diff=lfs merge=lfs -text

OncoMark.py

@@ -0,0 +1,193 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from scipy.stats import rankdata
+import time
+from st_aggrid import AgGrid, GridOptionsBuilder, GridUpdateMode
+import matplotlib.pyplot as plt
+import io
+from ulm import run_ulm
+from mlm import run_mlm
+from plotting import plot_barplot
+import tensorflow as tf
+import os
+
+# Page Configuration
+st.set_page_config(page_title="OncoMark", layout="wide")
+st.image("oncomark_title.png", caption="", use_container_width=True)
+# st.title("OncoMark")
+
+# Sidebar for uploading data
+st.sidebar.header("Upload Data")
+uploaded_file = st.sidebar.file_uploader("Upload your data file (CSV)", type=["csv"])
+
+# Description and Instructions
+# st.write("AI to predict cancer hallmarks from transcriptomics data.")
+
+# Load model
+model_path = 'hallmark_model.keras'
+scaler_path = 'hallmark_scaler.joblib'
+feature_file = 'hallmark_feature.txt'
+
+# Load the pre-trained model and scaler
+model = tf.keras.models.load_model(os.path.join(os.path.dirname(__file__), model_path))
+scaler = joblib.load(os.path.join(os.path.dirname(__file__), scaler_path))
+
+# Load feature names
+with open((os.path.join(os.path.dirname(__file__), feature_file)), 'r') as file:
+    feature_names = file.read().splitlines()
+
+# Define hallmark tasks
+hall_list = ['AIM', 'DCE', 'EGS', 'GIM', 'RCD', 'SPS', 'AID', 'IA', 'ERI', 'TPI']
+collectri = pd.read_csv('collectri_df.csv')
+progeny = pd.read_csv('progeny_df.csv')
+
+# Show an example structure if no data is uploaded
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file, index_col=0)
+    tf_acts, tf_pvals = run_ulm(mat=data, net=collectri, verbose=False)
+    pathway_acts, pathway_pvals = run_mlm(mat=data, net=progeny, verbose=False)
+
+    st.write("### Uploaded Data")
+    st.write(data.iloc[:5, :50])
+    data = data.loc[:, ~data.columns.duplicated(keep='first')]
+    data = data.reindex(columns=feature_names, fill_value=0).fillna(0)
+    data_index = data.index
+    data = rankdata(data * -1, axis=1, method='average')
+    data = np.log2(data)
+    data = scaler.transform(data)
+else:
+    st.write("### Example Input Format")
+    st.info("**Note:** I am flexible and can handle both normalized and non-normalized input data. Upload your data as is, and the model will adjust accordingly to provide accurate predictions.")
+    raw_count_data = pd.DataFrame({
+        'GeneA': [120, 150, 80],
+        'GeneB': [200, 180, 190],
+        'GeneC': [90, 75, 110],
+        'GeneD': [60, 95, 100]
+    }, index=['Sample1', 'Sample2', 'Sample3'])
+    st.write(raw_count_data)
+
+# Dummy model function (replace with actual model prediction)
+def model_predict(input_data):
+    predictions = model.predict(data)
+    prediction_df = pd.DataFrame()
+    for task_id, hall_name in enumerate(hall_list):
+        prediction_df[hall_name] = predictions[task_id].flatten()
+    prediction_df.index = data_index
+    return prediction_df
+
+def display_loading_animation():
+    with st.empty():
+        for i in range(3):
+            st.write("🔍 Predicting" + "." * (i + 1))
+            time.sleep(1.0)
+        st.write("🚀 Almost there...")
+
+# Initialize predictions to None
+predictions = None
+
+# Predict and display results if data is uploaded
+if uploaded_file is not None:
+    st.write("### Predictions")
+    display_loading_animation()
+    predictions = model_predict(data)
+    predictions = predictions.reset_index()
+    # st.write(predictions)
+else:
+    st.write("### Predictions")
+    st.info("Upload your data to see predictions.")
+
+
+selected = None
+
+# Display analysis if predictions are available
+if predictions is not None:
+    # Display predictions in AgGrid
+    gb = GridOptionsBuilder.from_dataframe(predictions)
+    gb.configure_selection(selection_mode='single', use_checkbox=False)
+    gb.configure_default_column(resizable=True, autoWidth=True, maxWidth=100)
+    grid_options = gb.build()
+
+    grid_response = AgGrid(
+        predictions,
+        gridOptions=grid_options,
+        update_mode=GridUpdateMode.SELECTION_CHANGED,
+        height=300,
+        enable_enterprise_modules=False,
+        allow_unsafe_jscode=True,
+        theme='streamlit',
+        custom_css={
+            ".ag-row-selected": {
+                "background-color": "#90EE90 !important"
+            }
+        }
+    )
+
+    csv_grid = predictions.to_csv().encode('utf-8')
+    st.download_button(
+        label="Download Table as CSV",
+        data=csv_grid,
+        file_name='aggrid_table.csv',
+        mime='text/csv'
+    )
+
+    # Extract selected row data and display bar plot on selection
+    selected = grid_response['selected_rows']
+    if selected is not None:
+        st.write("### Analysis")
+        selected_df = pd.DataFrame(selected)
+        sample_name = selected_df['index'][0]
+
+        st.write('##### Transcription factor activity')
+        st.info('If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.')
+        plot_barplot(
+        acts=tf_acts,
+        contrast=sample_name,
+        top=50,
+        vertical=False,
+        figsize=(11, 5))
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=300)
+        buf.seek(0)
+        st.pyplot(plt)
+        # Provide option to download the plot
+        st.download_button(
+            label="Download Plot as PNG",
+            data=buf,
+            file_name='tf_hallmark_{}.png'.format(sample_name),
+            mime='image/png'
+        )
+
+        st.write('##### Pathway activity')
+        st.info('If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.')
+        plot_barplot(
+        pathway_acts,
+        sample_name,
+        top=50,
+        vertical=False,
+        figsize=(6, 3))
+        buf = io.BytesIO()
+        plt.savefig(buf, format='png', dpi=300)
+        buf.seek(0)
+        st.pyplot(plt)
+        # Provide option to download the plot
+        st.download_button(
+            label="Download Plot as PNG",
+            data=buf,
+            file_name='pathway_hallmark_{}.png'.format(sample_name),
+            mime='image/png'
+        )
+
+    else:
+        st.write("### Analysis")
+        st.info('Click on a sample under predictions to see the analysis')
+else:
+    st.write("### Analysis")
+    st.info('Click on a sample under predictions to see the analysis')
+
+# Footer
+st.write("----")
+st.markdown("[Visit our GitHub Repository](https://github.com/SML-CompBio/OncoMark)", unsafe_allow_html=True)
+
+# Running the app: use `streamlit run filename.py`

README.md

@@ -0,0 +1,16 @@
+---
+title: OncoMark
+emoji: 🔬
+colorFrom: red
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.41.1
+app_file: OncoMark.py
+pinned: false
+license: apache-2.0
+thumbnail: >-
+  https://cdn-uploads.huggingface.co/production/uploads/63e1eb8ff0039731dfe3087b/EqB13HzzcCPw7Ucbvopcu.png
+short_description: AI-Driven Prediction of Cancer Hallmarks
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

hallmark_feature.txt

@@ -0,0 +1,9326 @@
+ARID3B
+MTMR6
+GBE1
+LTBP1
+RBMX2
+WTAP
+ORC4
+GJA4
+SRP9
+CARD16
+GOT1
+FOXP1
+EIF4EBP2
+HAUS3
+TRAPPC8
+PTPN23
+NOC2L
+CEP57
+BOP1
+PCSK7
+EFNB1
+TROVE2
+HDGFL2
+CACNB3
+SEC23A
+YTHDC1
+MYCBP2
+HPN
+MITD1
+SUPT7L
+RNFT1
+CPVL
+FKBP11
+ODF2
+NEU1
+ARRDC3
+NDUFAF6
+ANO6
+USP7
+RNF26
+ZNF91
+RPL27A
+LPP
+JUND
+PSMB3
+RETSAT
+PPIB
+NDUFS3
+AP2M1
+XPC
+PLAU
+TEX41
+DYRK4
+ZNF692
+UTP3
+BIK
+FAM234A
+NUP93
+ATP6V0B
+ATPAF2
+SETX
+SNTB1
+SFTPA1
+PSMB10
+FGGY
+POLR3C
+PBXIP1
+ZNF124
+RAI14
+KRT17
+AC008105.3
+TMED5
+UBC
+BRCC3
+REEP5
+TM9SF2
+SSH1
+GZMK
+ZMYM5
+CENPU
+GTF2I
+MAPK6
+BCL10
+NEK6
+CRELD2
+DSC2
+KCMF1
+MINK1
+CLPTM1
+GOLGA5
+MLLT1
+MIR181A1HG
+TM9SF3
+SPOCK1
+LUM
+SUMO3
+ADK
+VAPB
+MANBA
+AC023157.3
+C11orf71
+YPEL1
+BZW1
+UBE3A
+TCF7
+PLXNB1
+HMGN3
+NSMCE1
+VAV1
+RPS2
+KIAA1551
+ATF3
+NDUFAF4
+TRIM38
+TTYH3
+MYCBP
+HDAC10
+BAZ1B
+PRPS2
+CNPPD1
+CDKN2AIPNL
+TRIM41
+SGO2
+GPALPP1
+NME2
+NUP43
+ZSWIM7
+RPS5
+PDPK1
+TSHZ2
+CMTM3
+LINC01006
+AXL
+PEMT
+ANKRD11
+RABGGTA
+SBF2
+KDM6B
+KLHL5
+WWP1
+GRN
+MUC20-OT1
+IRF2BP2
+BCAS3
+ZBED5-AS1
+MAFB
+EMX2OS
+NOTCH2
+MOGS
+RASA3
+ALOX12-AS1
+AC008124.1
+PARVA
+PDK1
+NDST1
+FMOD
+AL445524.1
+SLC39A4
+AC016831.7
+SPC25
+ASRGL1
+SEPT4
+NHLRC3
+LYRM5
+BNIP1
+CD164
+MED15
+POFUT2
+STYX
+KLHDC4
+NDEL1
+CASP8AP2
+VCAN
+ISOC2
+MAPK8
+BAG2
+CYB5D1
+C12orf65
+FAM45A
+RPL38
+KRT7
+PINK1
+PRKAB1
+ADCY2
+ICK
+CLCN7
+STEAP1
+TCEB2
+PMEL
+TMEM222
+NINJ2
+ENDOD1
+HIST1H1C
+PPM1A
+SCAMP2
+PEX6
+PIGN
+WEE1
+HAUS5
+ARL5B
+LEMD2
+COL1A1
+ACP1
+PHF20
+S100A9
+PPIH
+DYNC1I2
+NVL
+TXLNG
+SAMD4A
+SZT2
+LCMT1
+ZNF226
+ST3GAL1
+RABL3
+GUSB
+APOO
+ZNF562
+RAP2C
+MYSM1
+DEDD
+IST1
+UPP1
+ECI2
+DSCC1
+IMPDH2
+AP1G2
+RNF125
+STOML1
+NEURL1
+ANKRD13D
+S100A6
+CNTRL
+TRAPPC4
+BCL3
+IGHV3-21
+NIPSNAP3A
+ADAM19
+MED14
+CDC40
+CSTF3
+KDM2A
+SDSL
+FARSA
+DCK
+RCE1
+WDR5
+NAV2
+BMS1
+CHRAC1
+ITFG2
+NECAP2
+PHF12
+RPL26L1
+FHOD1
+METTL5
+ZBTB7A
+IFT20
+PET117
+THOP1
+CD24
+CXCR4
+TRIM8
+ENO1
+RNF10
+EMD
+PRRC2C
+MEST
+CEBPZOS
+DDX3X
+PHC1
+PLXNA3
+LSM12
+TRIM28
+ASAH1
+TIMM8A
+CENPK
+FAM98C
+PRPF4B
+THOC6
+ADRM1
+GDI1
+ELOF1
+PBDC1
+SLC26A2
+HULC
+RPS6KA4
+MZF1
+TIMM17A
+IGLV2-8
+TOR2A
+LRCH3
+GPR107
+VSIG2
+STOM
+MT1H
+CALCOCO1
+RSRP1
+SIAH1
+OGG1
+HEXDC
+LHFPL3-AS1
+EIF2B4
+MXD1
+RHOA
+ZMIZ2
+CREB3
+FAM213A
+FOXN2
+ROCK2
+MFSD1
+HERPUD2
+BORCS5
+ATP10D
+KCNMB3
+PSIP1
+ZNF24
+SLC10A3
+ENO2
+RALA
+CUTA
+EDN1
+TMEM218
+RAB9A
+TRIM14
+FXR1
+ITM2A
+SGCB
+SDHA
+BEST1
+KDM6A
+ABCG1
+GPX3
+NELFA
+EPOR
+PNKP
+TGOLN2
+PSMA3
+GOLGB1
+TRIM4
+GABPB2
+HES6
+PPP3CB
+ITPA
+TWSG1
+KLC1
+ITGA5
+DDA1
+NFX1
+RHNO1
+MBNL1
+ATRX
+MAP2K6
+PCF11
+LRRC58
+FRAT2
+S100A4
+ECPAS
+PSMG2
+GMPPB
+MRM3
+GRINA
+PHF5A
+MEAF6
+APOBEC3H
+PPRC1
+WDR54
+RNF2
+TSPAN6
+RAD17
+NCOR1
+PRKCZ
+TXN
+DDX56
+NFIB
+TPST2
+PLTP
+TCF12
+GAS2L1
+VDAC3
+SCG5
+TUBA1A
+COL5A1
+GSTZ1
+ANAPC11
+PCNT
+NUDT4
+NPM1
+TGIF1
+LSM3
+TSC2
+TMED7
+DDX5
+SATB1
+ANAPC16
+NRBP1
+EGFL7
+DHX16
+SERTAD2
+RNF24
+GGT7
+OTUD5
+ERGIC2
+NR2C1
+LY6H
+ALG3
+NOTCH4
+SVBP
+DCXR
+SLC12A5
+BRWD1
+SPTLC2
+LZTS2
+PON2
+GCFC2
+TSPAN7
+TMCC1
+USP32
+HSPA6
+SEL1L3
+UCK1
+TMEM120B
+NGDN
+SNUPN
+ARL17A
+KLRC1
+SUGP2
+CALM3
+FUBP3
+C1orf131
+MRM2
+CDCP1
+IGHV4-31
+PTPN1
+ZNF322
+UBE2F
+INTS6
+TOR1AIP2
+PLA2G12A
+COMMD2
+HPF1
+PCOLCE
+CBX6
+GALNT11
+HSCB
+TK2
+MAD2L1
+BACE2
+IL12RB1
+MGST1
+HCFC1
+BMP7
+NFATC2IP
+CD3D
+KMT2C
+CLECL1
+WDR3
+OXSR1
+KLHDC8B
+TACO1
+DEDD2
+XXYLT1
+STC2
+DALRD3
+RANBP9
+UFL1
+ACYP2
+KLF6
+LSM4
+SF3B1
+MCM6
+RHOH
+CILP2
+ATP8A1
+SUMF2
+PHF10
+RNF216
+NR4A2
+PGS1
+COA3
+TMEM131
+SYNE2
+CAPN1
+TRIM11
+LIMK1
+CALHM6
+RPS27L
+HLA-DOA
+KPNA4
+HLA-A
+RRP8
+COX16
+NUBP2
+RCAN3
+MIIP
+PPIC
+RHOD
+GNA13
+PDCD5
+NPLOC4
+DPP7
+VARS
+NUCKS1
+TIPIN
+HOXB6
+AL355075.4
+AC004687.1
+TIA1
+SCRG1
+BX284668.5
+MCUB
+MKNK1
+CNPY3
+KLK2
+SCFD1
+SOX5
+RNASET2
+SUN1
+RNF14
+CD226
+FKBP3
+SHKBP1
+SRSF7
+UTP6
+RIC3
+SCAPER
+H2AFV
+ALG1
+HSPB1
+PAK1
+DBP
+KAZALD1
+RAB5C
+TULP4
+AGA
+SELENOM
+CCDC71L
+GMPR
+PGPEP1
+AL050341.2
+ABT1
+ANKRD26
+CENPP
+SLC7A1
+DCBLD2
+G3BP1
+LINC00958
+RHBDF2
+SHISA4
+TMLHE
+XPOT
+ISCA2
+HOMER2
+DPH5
+RNF113A
+ARCN1
+METTL26
+PHF1
+MCM7
+FILIP1L
+LYSMD3
+CUL3
+BEX1
+ATP13A3
+PTDSS1
+DNAH14
+CBR4
+ECM1
+WDR48
+DCAF12
+TMEM128
+IFI27L2
+COA7
+TP53TG1
+ZBTB43
+FOS
+CD81
+M6PR
+MUC16
+SGCD
+LRRFIP2
+ARRDC1
+SNX9
+HIRIP3
+POGLUT1
+CACYBP
+ZNF618
+C6orf120
+EEF1AKMT4
+SUV39H2
+SMIM8
+TM2D2
+REG4
+FAF2
+SLC2A11
+SPEN
+HSDL2
+INTS10
+GMNN
+UTRN
+SPCS3
+GRK3
+PAN3
+TMEM186
+KDM5B
+FBXL5
+C4orf3
+RANBP3
+DNMT3A
+CXCL1
+TMEM256
+IFNGR2
+BRD7
+ITGB2
+TRIQK
+CST6
+CNDP2
+RASGRP1
+INF2
+DPYD
+TP53INP2
+AFTPH
+PSD3
+ARID3A
+PEX26
+ACTR8
+MVB12A
+SFI1
+MRPS15
+ZYX
+GPBP1L1
+MRPS14
+MRPL12
+SDHC
+QSER1
+CLEC14A
+UBA3
+IGHV5-51
+CTSW
+OPA3
+PML
+PCED1B
+RPS6KC1
+TSPYL5
+PGGHG
+ZBTB24
+DCAF10
+MRPL41
+SHTN1
+ATMIN
+MAML3
+RALGDS
+MLH1
+ACTN4
+MED28
+NACA
+CREB5
+SETD9
+CDC42BPA
+DNAJC15
+YTHDF3
+VHL
+NUP98
+MRPL16
+POLD3
+TESC
+ATP6AP2
+PTPN2
+LRTOMT
+SLC30A9
+TCEAL2
+SSBP2
+GCNT1
+HIVEP2
+PPP1R8
+SPRTN
+ZNF207
+MT-CO3
+HIF1AN
+ITGAL
+SNX14
+GPATCH2L
+PPP4R1
+ENPP1
+TAOK3
+FOXJ3
+ZNF532
+PDK2
+GTF2H5
+CLUH
+PPP1R13L
+SKI
+RPL9
+CDC5L
+SMARCD1
+TMEM45A
+OPTN
+PIK3R5
+PBX4
+MPHOSPH9
+C1QTNF4
+CCL19
+FAM120A
+SETD6
+OXCT1
+MDK
+PREPL
+BCL6
+ANXA5
+FAM20C
+MYO1F
+RPS10
+CENPS
+NR1H3
+CBX5
+RFC3
+SF3A3
+PSMD11
+SCRIB
+SETD3
+MAP3K3
+QKI
+MELTF
+CAV2
+SUPT4H1
+IGLV2-11
+PRELP
+MFAP2
+ACAD9
+CCNH
+HEBP1
+CNP
+PUM2
+HBEGF
+SDHD
+ITPR1
+FAM181A
+AMMECR1
+SRGAP2C
+COX7A1
+DDX39A
+CPXM1
+CCDC25
+EBNA1BP2
+GPR174
+U2AF1L4
+CLASP2
+ZNF738
+DAP3
+IGHV4-39
+UTP18
+HERC1
+DHX29
+LPIN1
+SNF8
+LINC01588
+NTM
+GNPTAB
+EHD1
+HERC5
+LSP1
+AHSP
+YPEL3
+SLC35B4
+DLC1
+RNF126
+CASP1
+RNF212
+ZDHHC2
+GRTP1
+TCTA
+JPT1
+PPP1R11
+DVL2
+TMEM14C
+PALMD
+DHX38
+SCNM1
+SLC20A1
+NOL10
+STIM2
+SMC3
+PIP4P1
+SNHG29
+IDS
+ABCD4
+SYNGR2
+LIX1
+TMEM159
+SRP14
+POLE3
+TRNAU1AP
+CD38
+SLC12A4
+NDUFS6
+TRAF1
+MAP2K1
+OAS1
+CPSF1
+PEX16
+HSD17B7
+GNPNAT1
+CREB1
+RBFOX2
+TRAF3IP2
+SIT1
+CFL1
+MTCO2P12
+SST
+ZNF326
+CD2
+DNAJC5
+PI4KB
+CGAS
+RXRB
+LBH
+ZCCHC17
+HSPG2
+CIB1
+ZDHHC16
+EGR3
+IRF8
+MIEF1
+TIMM22
+CLUAP1
+FBXO11
+C19orf66
+ME2
+BUD23
+FAM214A
+CCNY
+PCBP2
+LRMP
+MAFF
+CCDC34
+REEP3
+ARFRP1
+ATP5F1C
+NCBP2
+EIF2B5
+FDCSP
+PQLC1
+CRK
+SPRED2
+UBE2D4
+DHTKD1
+GTF3C5
+ATP1B1
+NMT2
+ITGA4
+ZRANB1
+MORC3
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hallmark_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2453c374453d5fd68efed93c21cf27d53caff42100aee3d391254e6f35296b33
+size 7414084

hallmark_scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:125363094fe6a26500902da142a966504f209abc6134d4286c7d657f4f2fd9bc
+size 224439

mlm.py

@@ -0,0 +1,133 @@
+"""
+Method MLM.
+Code to run the Multivariate Linear Model (MLM) method.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy.sparse import csr_matrix
+
+from pre import extract, match, rename_net, get_net_mat, filt_min_n, return_data
+
+from scipy import stats
+
+from tqdm.auto import tqdm
+
+import numba as nb
+
+
+@nb.njit(nb.f4[:, :](nb.f4[:, :], nb.f4[:, :], nb.f4[:, :], nb.i8), parallel=True, cache=True)
+def fit_mlm(X, y, inv, df):
+    X = np.ascontiguousarray(X)
+    n_samples = y.shape[1]
+    n_fsets = X.shape[1]
+    coef, sse, _, _ = np.linalg.lstsq(X, y)
+    if len(sse) == 0:
+        raise ValueError("""Couldn\'t fit a multivariate linear model. This can happen because there are more sources
+        (covariates) than unique targets (samples), or because the network\'s matrix rank is smaller than the number of
+        sources.""")
+    sse = sse / df
+    se = np.zeros((n_samples, n_fsets), dtype=nb.f4)
+    for i in nb.prange(n_samples):
+        se[i] = np.sqrt(np.diag(sse[i] * inv))
+    t = coef.T/se
+    return t.astype(nb.f4)
+
+
+def mlm(mat, net, batch_size=10000, verbose=False):
+
+    # Get dims
+    n_samples = mat.shape[0]
+    n_features, n_fsets = net.shape
+
+    # Add intercept to network
+    net = np.column_stack((np.ones((n_features, ), dtype=np.float32), net))
+
+    # Compute inv and df for lm
+    inv = np.linalg.inv(np.dot(net.T, net))
+    df = n_features - n_fsets - 1
+
+    if isinstance(mat, csr_matrix):
+        # Init empty acts
+        n_batches = int(np.ceil(n_samples / batch_size))
+        es = np.zeros((n_samples, n_fsets), dtype=np.float32)
+        for i in tqdm(range(n_batches), disable=not verbose):
+
+            # Subset batch
+            srt, end = i * batch_size, i * batch_size + batch_size
+            y = mat[srt:end].toarray().T
+
+            # Compute MLM for batch
+            es[srt:end] = fit_mlm(net, y, inv, df)[:, 1:]
+    else:
+        # Compute MLM for all
+        es = fit_mlm(net, mat.T, inv, df)[:, 1:]
+
+    # Get p-values
+    pvals = 2 * (1 - stats.t.cdf(np.abs(es), df))
+
+    return es, pvals
+
+
+def run_mlm(mat, net, source='source', target='target', weight='weight', batch_size=10000,
+            min_n=5, verbose=False, use_raw=True):
+    """
+    Multivariate Linear Model (MLM).
+
+    MLM fits a multivariate linear model for each sample, where the observed molecular readouts in `mat` are the response
+    variable and the regulator weights in `net` are the covariates. Target features with no associated weight are set to
+    zero. The obtained t-values from the fitted model are the activities (`mlm_estimate`) of the regulators in `net`.
+
+    Parameters
+    ----------
+    mat : list, DataFrame or AnnData
+        List of [features, matrix], dataframe (samples x features) or an AnnData instance.
+    net : DataFrame
+        Network in long format.
+    source : str
+        Column name in net with source nodes.
+    target : str
+        Column name in net with target nodes.
+    weight : str
+        Column name in net with weights.
+    batch_size : int
+        Size of the samples to use for each batch. Increasing this will consume more memmory but it will run faster.
+    min_n : int
+        Minimum of targets per source. If less, sources are removed.
+    verbose : bool
+        Whether to show progress.
+    use_raw : bool
+        Use raw attribute of mat if present.
+
+    Returns
+    -------
+    estimate : DataFrame
+        MLM scores. Stored in `.obsm['mlm_estimate']` if `mat` is AnnData.
+    pvals : DataFrame
+        Obtained p-values. Stored in `.obsm['mlm_pvals']` if `mat` is AnnData.
+    """
+
+    # Extract sparse matrix and array of genes
+    m, r, c = extract(mat, use_raw=use_raw, verbose=verbose)
+
+    # Transform net
+    net = rename_net(net, source=source, target=target, weight=weight)
+    net = filt_min_n(c, net, min_n=min_n)
+    sources, targets, net = get_net_mat(net)
+
+    # Match arrays
+    net = match(c, targets, net)
+
+    if verbose:
+        print('Running mlm on mat with {0} samples and {1} targets for {2} sources.'.format(m.shape[0], len(c), net.shape[1]))
+
+    # Run MLM
+    estimate, pvals = mlm(m, net, batch_size=batch_size, verbose=verbose)
+
+    # Transform to df
+    estimate = pd.DataFrame(estimate, index=r, columns=sources)
+    estimate.name = 'mlm_estimate'
+    pvals = pd.DataFrame(pvals, index=r, columns=sources)
+    pvals.name = 'mlm_pvals'
+
+    return return_data(mat=mat, results=(estimate, pvals))

plotting.py

@@ -0,0 +1,148 @@
+import numpy as np
+
+def check_if_matplotlib(return_mpl=False):
+    if not return_mpl:
+        try:
+            import matplotlib.pyplot as plt
+        except Exception:
+            raise ImportError('matplotlib is not installed. Please install it with: pip install matplotlib')
+        return plt
+    else:
+        try:
+            import matplotlib as mpl
+        except Exception:
+            raise ImportError('matplotlib is not installed. Please install it with: pip install matplotlib')
+        return mpl
+
+
+def check_if_seaborn():
+    try:
+        import seaborn as sns
+    except Exception:
+        raise ImportError('seaborn is not installed. Please install it with: pip install seaborn')
+    return sns
+
+def set_limits(vmin, vcenter, vmax, values):
+
+    if vmin is None:
+        vmin = values.min()
+
+    if vmax is None:
+        vmax = values.max()
+
+    if vcenter is None:
+        vcenter = values.mean()
+
+    if vmin >= vcenter:
+        vmin = -vmax
+
+    if vcenter >= vmax:
+        vmax = -vmin
+
+    return vmin, vcenter, vmax
+
+def plot_barplot(acts, contrast, top=25, vertical=False, cmap='coolwarm', vmin=None, vcenter=0, vmax=None,
+                 figsize=(7, 5), dpi=100, ax=None, return_fig=False):
+    """
+    Plot barplots showing the top absolute value activities.
+
+    Parameters
+    ----------
+    acts : DataFrame
+        Activities obtained from any method.
+    contrast : str
+        Name of the contrast (row) to plot.
+    top : int
+        Number of top features to plot.
+    vertical : bool
+        Whether to plot verticaly or horizontaly.
+    cmap : str
+        Colormap to use.
+    vmin : float, None
+        The value representing the lower limit of the color scale.
+    vcenter : float, None
+        The value representing the center of the color scale.
+    vmax : float, None
+        The value representing the upper limit of the color scale.
+    figsize : tuple
+        Figure size.
+    dpi : int
+        DPI resolution of figure.
+    ax : Axes, None
+        A matplotlib axes object. If None returns new figure.
+    return_fig : bool
+        Whether to return a Figure object or not.
+    save : str, None
+        Path to where to save the plot. Infer the filetype if ending on {``.pdf``, ``.png``, ``.svg``}.
+
+    Returns
+    -------
+    fig : Figure, None
+        If return_fig, returns Figure object.
+    """
+
+    # Load plotting packages
+    sns = check_if_seaborn()
+    plt = check_if_matplotlib()
+    mpl = check_if_matplotlib(return_mpl=True)
+
+    # Check for non finite values
+    if np.any(~np.isfinite(acts)):
+        raise ValueError('Input acts contains non finite values.')
+
+    # Process df
+    df = acts.loc[[contrast]]
+    df.index.name = None
+    df.columns.name = None
+    df = (df
+
+          # Sort by absolute value and transpose
+          .iloc[:, np.argsort(abs(df.values))[0]].T
+
+          # Select top features and add index col
+          .tail(top).reset_index()
+
+          # Rename col
+          .rename({contrast: 'acts'}, axis=1)
+
+          # Sort by activities
+          .sort_values('acts'))
+
+    if vertical:
+        x, y = 'acts', 'index'
+    else:
+        x, y = 'index', 'acts'
+
+    # Plot
+    fig = None
+    if ax is None:
+        fig, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)
+    sns.barplot(data=df, x=x, y=y, ax=ax)
+
+    if vertical:
+        sizes = np.array([bar.get_width() for bar in ax.containers[0]])
+        ax.set_xlabel('Activity')
+        ax.set_ylabel('')
+    else:
+        sizes = np.array([bar.get_height() for bar in ax.containers[0]])
+        ax.tick_params(axis='x', rotation=90)
+        ax.set_ylabel('Activity')
+        ax.set_xlabel('')
+
+    # Compute color limits
+    vmin, vcenter, vmax = set_limits(vmin, vcenter, vmax, df['acts'])
+
+    # Rescale cmap
+    divnorm = mpl.colors.TwoSlopeNorm(vmin=vmin, vcenter=vcenter, vmax=vmax)
+    cmap_f = plt.get_cmap(cmap)
+    div_colors = cmap_f(divnorm(sizes))
+    for bar, color in zip(ax.containers[0], div_colors):
+        bar.set_facecolor(color)
+
+    # Add legend
+    sm = plt.cm.ScalarMappable(cmap=cmap, norm=divnorm)
+    sm.set_array([])
+    ax.get_figure().colorbar(sm, ax=ax)
+
+    if return_fig:
+        return fig

pre.py

@@ -0,0 +1,309 @@
+"""
+Preprocessing functions.
+Functions to preprocess the data before running any method.
+"""
+
+import numpy as np
+from scipy.sparse import csr_matrix, issparse
+import pandas as pd
+import logging
+from anndata import AnnData
+from numpy.random import default_rng
+
+
+def check_mat(m, r, c, verbose=False):
+
+    # Accept any sparse format but transform to csr
+    if issparse(m) and not isinstance(m, csr_matrix):
+        m = csr_matrix(m)
+
+    # Check for empty features
+    if type(m) is csr_matrix:
+        msk_features = m.getnnz(axis=0) == 0
+    else:
+        msk_features = np.count_nonzero(m, axis=0) == 0
+    n_empty_features = np.sum(msk_features)
+    if n_empty_features > 0:
+        if verbose:
+            print("{0} features of mat are empty, they will be removed.".format(n_empty_features))
+        c = c[~msk_features]
+        m = m[:, ~msk_features]
+
+    # Check for repeated features
+    if np.any(c[1:] == c[:-1]):
+        raise ValueError("""mat contains repeated feature names, please make them unique.""")
+
+    # Check for empty samples
+    if type(m) is csr_matrix:
+        msk_samples = m.getnnz(axis=1) == 0
+    else:
+        msk_samples = np.count_nonzero(m, axis=1) == 0
+    n_empty_samples = np.sum(msk_samples)
+    if n_empty_samples > 0:
+        if verbose:
+            print("{0} samples of mat are empty, they will be removed.".format(n_empty_samples))
+        r = r[~msk_samples]
+        m = m[~msk_samples]
+
+    # Check for non finite values
+    if np.any(~np.isfinite(m.data)):
+        raise ValueError("""mat contains non finite values (nan or inf), please set them to 0 or remove them.""")
+
+    return m, r, c
+
+
+def extract(mat, use_raw=True, verbose=False, dtype=np.float32):
+    """
+    Processes different input types so that they can be used downstream.
+
+    Parameters
+    ----------
+    mat : list, pd.DataFrame or AnnData
+        List of [matrix, samples, features], dataframe (samples x features) or an AnnData instance.
+    use_raw : bool
+        Use `raw` attribute of `adata` if present.
+    dtype : type
+        Type of float used.
+
+    Returns
+    -------
+    m : csr_matrix
+        Sparse matrix containing molecular readouts or statistics.
+    r : ndarray
+        Array of sample names.
+    c : ndarray
+        Array of feature names.
+    """
+
+    if type(mat) is list:
+        m, r, c = mat
+        m = np.array(m, dtype=dtype)
+        r = np.array(r, dtype='U')
+        c = np.array(c, dtype='U')
+    elif type(mat) is pd.DataFrame:
+        m = mat.values.astype(dtype)
+        r = mat.index.values.astype('U')
+        c = mat.columns.values.astype('U')
+    elif type(mat) is AnnData:
+        if use_raw:
+            if mat.raw is None:
+                raise ValueError("Received `use_raw=True`, but `mat.raw` is empty.")
+            m = mat.raw.X.astype(dtype)
+            c = mat.raw.var.index.values.astype('U')
+        else:
+            m = mat.X.astype(dtype)
+            c = mat.var.index.values.astype('U')
+        r = mat.obs.index.values.astype('U')
+
+    else:
+        raise ValueError("""mat must be a list of [matrix, samples, features], dataframe (samples x features) or an AnnData
+        instance.""")
+
+    # Check mat for empty or not finite values
+    m, r, c = check_mat(m, r, c, verbose=verbose)
+
+    # Sort genes
+    msk = np.argsort(c)
+
+    return m[:, msk].astype(dtype), r.astype('U'), c[msk].astype('U')
+
+
+def filt_min_n(c, net, min_n=5):
+    """
+    Removes sources of a `net` with less than min_n targets.
+
+    First it filters target features in `net` that are not in `mat` and then removes sources with less than `min_n` targets.
+
+    Parameters
+    ----------
+    c : ndarray
+        Column names of `mat`.
+    net : DataFrame
+        Network in long format.
+    min_n : int
+        Minimum of targets per source. If less, sources are removed.
+
+    Returns
+    -------
+    net : DataFrame
+        Filtered net in long format.
+    """
+
+    # Find shared targets between mat and net
+    msk = np.isin(net['target'].values.astype('U'), c)
+    net = net.iloc[msk]
+
+    # Count unique sources
+    sources, counts = np.unique(net['source'].values.astype('U'), return_counts=True)
+
+    # Find sources with more than min_n targets
+    msk = np.isin(net['source'].values.astype('U'), sources[counts >= min_n])
+
+    # Filter
+    net = net[msk]
+
+    if net.shape[0] == 0:
+        raise ValueError("""No sources with more than min_n={0} targets. Make sure mat and net have shared target features or
+        reduce the number assigned to min_n""".format(min_n))
+
+    return net
+
+
+def match(c, r, net):
+    """
+    Matches `mat` with a regulatory adjacency matrix.
+
+    Parameters
+    ----------
+    c : ndarray
+        Column names of `mat`.
+    r : ndarray
+        Row  names of `net`.
+    net : ndarray
+        Regulatory adjacency matrix.
+
+    Returns
+    -------
+    regX : ndarray
+        Matching regulatory adjacency matrix.
+    """
+
+    # Init empty regX
+    regX = np.zeros((c.shape[0], net.shape[1]), dtype=np.float32)
+
+    # Create an index array for rows of c corresponding to r
+    c_dict = {gene: i for i, gene in enumerate(c)}
+    idxs = [c_dict[gene] for gene in r if gene in c_dict]
+
+    # Populate regX using advanced indexing
+    regX[idxs, :] = net[: len(idxs), :]
+
+    return regX
+
+
+def rename_net(net, source='source', target='target', weight='weight'):
+    """
+    Renames input network to match decoupler's format (source, target, weight).
+
+    Parameters
+    ----------
+    net : DataFrame
+        Network in long format.
+    source : str
+        Column name where to extract source features.
+    target : str
+        Column name where to extract target features.
+    weight : str, None
+        Column name where to extract features' weights. If no weights are available, set to None.
+
+    Returns
+    -------
+    net : DataFrame
+        Renamed network.
+    """
+
+    # Check if names are in columns
+    msg = 'Column name "{0}" not found in net. Please specify a valid column.'
+    assert source in net.columns, msg.format(source)
+    assert target in net.columns, msg.format(target)
+    if weight is not None:
+        assert weight in net.columns, msg.format(weight) + """Alternatively, set to None if no weights are available."""
+    else:
+        net = net.copy()
+        net['weight'] = 1.0
+        weight = 'weight'
+
+    # Rename
+    net = net.rename(columns={source: 'source', target: 'target', weight: 'weight'})
+
+    # Sort
+    net = net.reindex(columns=['source', 'target', 'weight'])
+
+    # Check if duplicated
+    is_d = net.duplicated(['source', 'target']).sum()
+    if is_d > 0:
+        raise ValueError('net contains repeated edges, please remove them.')
+
+    return net
+
+
+def get_net_mat(net):
+    """
+    Transforms a given network to a regulatory adjacency matrix (targets x sources).
+
+    Parameters
+    ----------
+    net : DataFrame
+        Network in long format.
+
+    Returns
+    -------
+    sources : ndarray
+        Array of source names.
+    targets : ndarray
+        Array of target names.
+    X : ndarray
+        Array of interactions bewteen sources and targets (target x source).
+    """
+
+    # Pivot df to a wider format
+    X = net.pivot(columns='source', index='target', values='weight')
+    X[np.isnan(X)] = 0
+
+    # Store node names and weights
+    sources = X.columns.values
+    targets = X.index.values
+    X = X.values
+
+    return sources.astype('U'), targets.astype('U'), X.astype(np.float32)
+
+
+def mask_features(mat, log=False, thr=1, use_raw=False):
+    if log:
+        thr = np.exp(thr) - 1
+    if type(mat) is list:
+        m, r, c = mat
+        m[m < thr] = 0.0
+        return [m, r, c]
+    elif type(mat) is pd.DataFrame:
+        mat.loc[:, :] = np.where(mat.values < thr, 0.0, mat.values)
+        return mat
+    elif type(mat) is AnnData:
+        if use_raw:
+            if mat.raw is None:
+                raise ValueError("Received `use_raw=True`, but `mat.raw` is empty.")
+            mat.raw.X[mat.raw.X < thr] = 0.0
+        else:
+            mat.X[mat.X < thr] = 0.0
+    else:
+        raise ValueError("""mat must be a list of [matrix, samples, features], dataframe (samples x features) or an AnnData
+        instance.""")
+
+
+def add_to_anndata(mat, results):
+    for result in results:
+        if result is not None:
+            mat.obsm[result.name] = result
+
+
+def return_data(mat, results):
+    if isinstance(mat, AnnData):
+        if mat.obs_names.size != results[0].index.size:
+            logging.warning('Provided AnnData contains empty observations. Returning repaired object.')
+            mat = mat[results[0].index, :].copy()
+            add_to_anndata(mat, results)
+            return mat
+        else:
+            add_to_anndata(mat, results)
+            return None
+    else:
+        return tuple([result for result in results if result is not None])
+
+
+def break_ties(m, c, seed):
+    # Randomize feature order to break ties randomly
+    rng = default_rng(seed=seed)
+    idx = np.arange(c.size)
+    idx = rng.choice(idx, c.size, replace=False)
+    m, c = m[:, idx], c[idx]
+    return m, c

requirements.txt

@@ -0,0 +1,12 @@
+streamlit
+streamlit-aggrid
+matplotlib
+scikit-learn
+anndata
+seaborn
+numba
+pandas==2.2.3
+numpy==2.0.2
+tensorflow==2.18.0
+joblib==1.4.2
+scipy==1.14.1

ulm.py

@@ -0,0 +1,126 @@
+"""
+Method ULM.
+Code to run the Univariate Linear Model (ULM) method.
+"""
+
+import numpy as np
+import pandas as pd
+from scipy.sparse import csr_matrix
+
+from scipy.stats import t
+
+from pre import extract, match, rename_net, get_net_mat, filt_min_n, return_data
+
+from tqdm.auto import tqdm
+
+
+def mat_cov(A, b):
+    return np.dot(b.T - b.mean(), A - A.mean(axis=0)) / (b.shape[0]-1)
+
+
+def mat_cor(A, b):
+    cov = mat_cov(A, b)
+    ssd = np.std(A, axis=0, ddof=1) * np.std(b, axis=0, ddof=1).reshape(-1, 1)
+    return cov / ssd
+
+
+def t_val(r, df):
+    return r * np.sqrt(df / ((1.0 - r + 1.0e-16)*(1.0 + r + 1.0e-16)))
+
+
+def ulm(mat, net, batch_size=10000, verbose=False):
+
+    # Get dims
+    n_samples = mat.shape[0]
+    n_features, n_fsets = net.shape
+    df = n_features - 2
+
+    if isinstance(mat, csr_matrix):
+        n_batches = int(np.ceil(n_samples / batch_size))
+        es = np.zeros((n_samples, n_fsets), dtype=np.float32)
+        for i in tqdm(range(n_batches), disable=not verbose):
+
+            # Subset batch
+            srt, end = i * batch_size, i * batch_size + batch_size
+            batch = mat[srt:end].toarray().T
+
+            # Compute R for batch
+            r = mat_cor(net, batch)
+
+            # Compute t-value
+            es[srt:end] = t_val(r, df)
+    else:
+        # Compute R value for all
+        r = mat_cor(net, mat.T)
+
+        # Compute t-value
+        es = t_val(r, df)
+
+    # Compute p-value
+    pv = t.sf(abs(es), df) * 2
+
+    return es, pv
+
+
+def run_ulm(mat, net, source='source', target='target', weight='weight', batch_size=10000,
+            min_n=5, verbose=False, use_raw=True):
+    """
+    Univariate Linear Model (ULM).
+
+    ULM fits a linear model for each sample and regulator, where the observed molecular readouts in `mat` are the response
+    variable and the regulator weights in `net` are the explanatory one. Target features with no associated weight are set to
+    zero. The obtained t-value from the fitted model is the activity (`ulm_estimate`) of a given regulator.
+
+    Parameters
+    ----------
+    mat : list, DataFrame or AnnData
+        List of [features, matrix], dataframe (samples x features) or an AnnData instance.
+    net : DataFrame
+        Network in long format.
+    source : str
+        Column name in net with source nodes.
+    target : str
+        Column name in net with target nodes.
+    weight : str
+        Column name in net with weights.
+    batch_size : int
+        Size of the samples to use for each batch. Increasing this will consume more memmory but it will run faster.
+    min_n : int
+        Minimum of targets per source. If less, sources are removed.
+    verbose : bool
+        Whether to show progress.
+    use_raw : bool
+        Use raw attribute of mat if present.
+
+    Returns
+    -------
+    estimate : DataFrame
+        ULM scores. Stored in `.obsm['ulm_estimate']` if `mat` is AnnData.
+    pvals : DataFrame
+        Obtained p-values. Stored in `.obsm['ulm_pvals']` if `mat` is AnnData.
+    """
+
+    # Extract sparse matrix and array of genes
+    m, r, c = extract(mat, use_raw=use_raw, verbose=verbose)
+
+    # Transform net
+    net = rename_net(net, source=source, target=target, weight=weight)
+    net = filt_min_n(c, net, min_n=min_n)
+    sources, targets, net = get_net_mat(net)
+
+    # Match arrays
+    net = match(c, targets, net)
+
+    if verbose:
+        print('Running ulm on mat with {0} samples and {1} targets for {2} sources.'.format(m.shape[0], len(c), net.shape[1]))
+
+    # Run ULM
+    estimate, pvals = ulm(m, net, batch_size=batch_size, verbose=verbose)
+
+    # Transform to df
+    estimate = pd.DataFrame(estimate, index=r, columns=sources)
+    estimate.name = 'ulm_estimate'
+    pvals = pd.DataFrame(pvals, index=r, columns=sources)
+    pvals.name = 'ulm_pvals'
+
+    return return_data(mat=mat, results=(estimate, pvals))